Methods and systems for detecting one or more analytes associated with one or more autoimmune diseases

Abstract

Methods and systems for detecting one or more autoantibodies and / or analytes associated with one or more clinical feature or outcome, such as an autoimmune and related disease or other disease through binding events on a biomolecule array. Kinetic parameters of the binding events can be determined. The kinetic parameters may be used to identify one or more clinical feature or outcome, such as diagnose one or more disease, such as an autoimmune and related disease, and / or to provide a prognosis of one or more disease. The kinetic parameters may be used as inputs in one or more machine learning algorithms to identify associations and / or correlations between one or more kinetic parameter and / or other additional information and one or more clinical feature or outcome.

Description

[0001] Atty Dkt. No.155529.00024 METHODS AND SYSTEMS FOR DETECTING ONE OR MORE ANALYTES ASSOCIATED WITH ONE OR MORE AUTOIMMUNE DISEASES CROSS REFERENCE TO RELATED APPLICATION This application claims the benefit of U.S. Provisional Application Serial No. 63 / 597,981, filed November 11, 2023, U.S. Provisional Application Serial No. 63 / 600,056, filed November 17, 2023, U.S. Provisional Application Serial No. 63 / 624,255, filed January 23, 2024, U.S. Provisional Application Serial No. 63 / 632,820, filed April 11, 2024, U.S. Provisional Application Serial No. 63 / 656,496, filed June 5, 2024, and U.S. Provisional Application Serial No. 63 / 719,185, filed November 12, 2024, the entire content of each of which is incorporated by reference herein in its entirety. SEQUENCE LISTING STATEMENT An electronic sequence listing (15552900024.xml; Size: 112,255 bytes; and Date Created: November 12, 2024) accompanies this application. The sequence listing is incorporated by reference in its entirety. BACKGROUND Autoimmune diseases (ADs) are chronic and often uncurable conditions that significantly reduce quality of life and affects over 7% of Americans today, with a global prevalence that has been increasing. Systemic autoimmune rheumatic diseases (SARDs) include a group of disorders that are particularly challenging to diagnose and treat due to the heterogeneity of the diseases that produce overlapping symptoms. SARDs include systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), Sjögren’s syndrome (SjS), systemic sclerosis (SSc), mixed connective tissue disease (MCTD), and a variety of inflammatory myopathies. A hallmark feature of these disorders is the loss of immunological tolerance to otherwise healthy cellular components produced by the subject. B cells of the immune system produce autoantibodies (AAbs) that target self-antigens, driving immune complexes to form that can induce inflammation and damage to various organs and tissues depending on the SARD or SARD sub-type. Early detection of SARDs can therefore be important to initiate therapeutic interventions which may help reduce the immunological processes that can induce permanent damage over time. While the presence of autoantibodies is a diagnostic requirement for all SARDs, which in many cases can appear years prior to overt disease onset and serve as useful disease biomarkers, it can be difficult to determine once particular autoantibodies appear when a person will develop symptoms, when it is appropriate to administer treatment, or if a Atty Dkt. No.155529.00024 person will develop a particular sub-type of the disease. Oftentimes diagnostic testing is performed after symptom onset, and this delay in treatment can result in irreparable organ damage, substantially increasing the cost of disease management over the patient’s lifetime. Additionally, AAb testing performed today necessitates repeated screening across various assays tailored to detect only a few autoantibodies at a time, increasing time to diagnosis and costs. Individual AAb screening decreases clinical sensitivity and specificity, increasing the likelihood of a false positive or negative resulting in a misdiagnosis or requiring further testing when symptoms are present. Most available assays rely on an endpoint, fluorescence-based readout which can only provide a semi-quantitative assessment of AAb concentration (titer) within a sample but cannot determine the relative affinity or kinetic attributes that an AAb may have for a particular antigen. Resolving the kinetics of antibody-antigen interactions (among other analytes of interest) is valuable to assess is as it can impact the detection of certain analytes by traditional end-point assays. For example, interacting molecules with fast-off rates may dissociate before being detected. By contrast, real-time interaction monitoring techniques as leveraged herein can detect these lower affinity or quick interactions reducing the risk of false-negative rates that are present in end-point assays. Furthermore, the kinetics of antibody- antigen interactions are of clinical value since AAb affinity is involved in the pathogenesis of autoimmune diseases and their sub-types with varying physiological implications that may dictate or indicate onset of disease symptoms or worsening of disease activity, implicate a patient as a responder to particular therapeutics, or flag a patient as at risk for development of secondary complications. SUMMARY In various aspects, methods and systems are disclosed for detecting one or more analyte associated with one or more clinical feature or outcome. The methods and systems can be used for determining one or more kinetic binding parameter of the analyte to one or more biomolecule. In one aspect, the system can include a biomolecule array. The biomolecule array can include a first substrate and a biosensor surface coupled to the first substrate. A plurality of biomolecules may be coupled to the biosensor surface. The biosensor surface can include an array of discrete locations, with each discrete location including a biomolecule of a plurality of biomolecules coupled to the biosensor surface. The plurality of biomolecules is associated with the one or more clinical feature or outcome, such as an autoimmune or related disease. The array of discrete locations can include at least 100 discrete locations. The system may comprise Atty Dkt. No.155529.00024 a one or more additional substrate. For example, a second substrate may be connected to the first substrate of the biomolecule array. The plurality of biomolecules, or at least a portion of the plurality of biomolecules, may be generated on the second substrate. The plurality of biomolecules or a portion of the plurality of biomolecules may be generated from a nucleic acid by in vitro transcription, in vitro translation, and in situ cell-free expression using a cell- free lysate. The portion of the plurality of biomolecules may be purified. Further, the plurality of biomolecules may be captured to a biosensor surface of the biomolecule array. In certain aspects, a method for detecting one or more analyte in a sample using a biomolecule array is disclosed. The method can include exposing a sample to a biosensor surface of a biomolecule array. The biomolecular array can include a plurality of biomolecules coupled to the biosensor surface at discrete locations. The method can further include detecting one or more binding event involving one or more analyte in the sample to at least a portion of the biomolecules. The method can further include determining one or more kinetic parameter associated with the one or more binding event. In one or more aspects, the kinetic parameters can be utilized to identify and / or diagnose one or more clinical feature, such as an autoimmune or related disease, and / or to provide a prognosis of the one or more clinical feature, and / or to inform treatment of one or more clinical feature (e.g., autoimmune or related disease). In certain aspects, the kinetic parameters can be used as inputs in one or more machine learning algorithm to identify associations between one or more kinetic parameter and one or more clinical feature of one or more clinical feature, such as an autoimmune or related disease. These and other described features, aspects, and advantages will become better understood by persons of ordinary skill in the art upon consideration of the following drawings, detailed description, and appended claims. BRIEF DESCRIPTION OF DRAWINGS FIG. 1 illustrates a flowchart exemplative of current reflex testing performed during diagnosis of common SARDs. Initial antinuclear antibody (ANA) screens (via HEp-2 immunofluorescence assay for example) cannot definitively delineate which antigens a patient has developed AAbs against. Therefore, to inform a more precise diagnosis, numerous downstream tests are performed (typically via ELISAs or multiplexed bead assays). The flowchart outlines the various tests that initially are performed to assess presence of autoantibodies in patients suspected to have a SARD (i.e., ANA screen) and the follow up tests needed under the current testing paradigm to detect specifically which autoantibody Atty Dkt. No.155529.00024 populations someone has developed given a positive ANA test. Several antigens and their associated SARDs are listed. SLE: systemic lupus erythematosus; MCTD: mixed connective tissue disease; SjS: Sjögren’s syndrome. SSc: systemic sclerosis; PM: polymyositis; NPSLE: Neuropsychiatric SLE. Adapted from Quest Test Code 16814. FIG. 2 is a schematic depiction of the relationship between AAb affinity, epitope specificity, and the pathogenesis of representative SARDs SLE and RA. FIG. 3 is a schematic diagram illustrating an example disclosed biomolecule sensor. The biosensor first substrate 404, adhesion layer 412, and biosensor surface 408 may be comprised of a plurality of materials compatible with any range of label-free biosensing approaches. The biosensor surface itself may contain an unstructured surface (e.g., top smooth center circle, and / or a plurality of microstructured and / or nanostructured arrangements (e.g., center and bottom circles). FIG.4 shows exemplary types of biosensors and sensing methods. FIGS. 5A-5C are illustrative images of an SPR biomolecule array on which a microarray of protein has been captured and dried on the biosensor surface after array formation via cell-free protein expression (FIGS. 5A, 5B) and an instrument 500 for the depicted biomolecule array (FIG.5C). FIG.6A-6E illustrate one example method for preparing a protein array on a biosensor surface 208. (FIG.6A) shows a protein array on a coated SPR biosensor chip or slide 200 (also termed sensor-integrated proteome on chip, or SPOC). The protein array formed via cell-free protein expression of full-length folded proteins in nanowells of nanowell slides 604 and in situ capture-purification onto the biosensor chip or slide 200. Nanowell slides 604 containing pDNA harboring tagged proteins of interest act as micro reaction vessels for transcription and translation by cell-free protein expression lysate. The nanowells are sealed after being filled with lysate by a biosensor capture slide which after a 2-hour incubation yields a monolayer of purified protein that have been captured onto the biosensor slide on the surface 208 at discrete spots located directly above each nanowell, in an array format. (FIG.6B) Image of a nanowell slide into which expression plasmid DNAs are printed. Protein arrays are expressed from the nanowell slide are captured on a variety of substrates, including glass microscope slides for standard fluorescent screens, or gold-coated SPOC biosensors for label-free kinetic screening via SPR. A scanning electron microscope (SEM) image of a nanowell slide cross-section is shown below the full top-down image of the nanowell slide (25 mm x 75 mm). (FIG.6C) Image of a SPOC biosensor and the protein microarray pattern formed on the surface of the gold- coated biosensor slide after the protein expression / capture process outlined in FIG. 6A. (FIG. Atty Dkt. No.155529.00024 6D) demonstrates a method for expression and capture of IVTT proteins onto a biosensor surface with a capture tag protein, with capture tag (or fusion tag) connected directly to the biosensor surface, or captured to a linker (or capture ligand) coated on the biosensor surface that binds the capture tag to the biosensor surface. Step 1, top: print nucleic acids or proteins into nanowells. Step 2, middle: overlay capture slide and introduce cell-free expression lysate. Step 3, bottom: screen capture slide for interactions of interest. (FIG.6E) shows reproducibility of SPOC sensor binding signal post-regeneration with a bar plot showing the normalized Mouse anti-HaloTag Rmax signal observed after four injections of the antibody measured for each of 132 spots across the SPOC array (± SD). The sensor was regenerated with 10mM Glycine-HCl (pH = 2.4) between each injection and greater than 99% of the HaloTag signal was retained between regenerations for all ligand containing spots. The regeneration experiment was performed using the Carterra LSAXTSPR instrument. FIGS.7A-7F are representative examples of Ig-like proteins154. Ig-like proteins consist of variable and constant regions made up of heavy and light chains. These regions, or domains, can be rearranged in multiple formats to create unique Ig-like protein fragments. Human, camel (VHH), and shark (IgNAR) variations are depicted. Figure adapted from Ref.164. FIG.8 is an example of altering redox state conditions within an IVTT mix81. Oxidizing reagents are added to the standard IVTT mix, incubated with the Ig-like protein DNA, and arrayed in tubes, plates, nanowells, or other format for screening, here in the nanowells of the SPOC biomolecule array. Figure adapted from Ref.57. FIGS.9A-9D demonstrate printing of nucleic acids in combination with prior expressed and purified proteins or fusion proteins onto the same nanowell slide for capture of both IVTT protein expressed by cell-free lysate and capture of the printed (prior expressed and purified) protein onto capture slide surfaces (FIG. 9A). In this exemplary case an SPR biosensor was used for label-free assay (FIGs. 9B-9C) and a glass surface capture slide was used for fluorescent end-point assay (FIG. 9D). FIG. 9B analyte is 133nM mouse anti-HaloTag antibody; FIG.9C analyte is 670nM mouse anti-GST antibody; FIG.9D analyte is rabbit anti- HaloTag antibody at 1:750 dilution. FIGS. 10A-10C illustrate the plurality of capture surfaces able to be employed for ligand capture and screening by various end-point and real-time assays as outlined in this disclosure. Two exemplary capture surface modalities are demonstrated for screening by three readouts via fluorescence, surface plasmon resonance, and matrix-assisted laser desorption / ionization (MALDI) mass spectroscopy. FIG. 10A: top: glass capture surface; bottom: interaction readout. FIG. 10B: top: gold biosensor surface, SPR real-time assay; Atty Dkt. No.155529.00024 bottom: interaction readout. FIG.10C: top: gold biosensor surface, MALDI imaging end point assay; bottom: interaction readout of MBP probe binding signal and MBP probe peak. FIG. 11 is a graph illustrating an example sensogram or kinetic trace from which one or more kinetic parameters can be derived after proper control-surface normalization and blank-injection referencing. For any given AAb-antigen interaction, the parameters can include the association and dissociation rates, the max signal observed, and other kinetic trace- independent features that can be detected, such as AAb isotype, which can be used to generate patient-specific profiles and disease-specific associations. FIGS. 12A-12E is a biosensor chip as viewed by SPR instrument showing different capture densities 1,000 and 2,400 (FIG. 12A) and simultaneous collection of qualitative, quantitative, and kinetic data across the entire biosensor chip as sensograms each representing data from a unique spot on the biosensor (FIG. 12B), with expanded view of an anti-capture tag antibody titration data with mouse anti-capture tag (FIG. 12C). These show the expansion capability to larger numbers of targets with the SPOC array, with little to no cross-talk as indicated by antigen-specific (FIG. 12D) and capture-tag (FIG. 12E) antibodies probing the same sensor locations. FIGS. 13A-13D show Carterra LSAXTdata of antibody interactions on SPOC chip. (FIG.13A) Image of the SPOC sensor surface (10k array) as viewed from the flowcell of the LSAXT. The hockey-stick shaped orientation mark which aids in orienting and mapping the array is clearly visible in the center of the array. (FIG. 13B) Sensograms reporting the mouse anti-HaloTag antibody binding responses from across 384 spots simultaneously on the SPOC array (or slide) are shown and validate capture of IVTT expressed Halo fusion proteins. (FIG. 13C) sensograms corresponding to each of three spots on the SPOC array containing p53, Jun, and Src HaloTag fusion proteins are shown demonstrating the binding responses observed after injection of mouse anti-p53 (16 nM), anti-Jun (67 nM), anti-Src (67 nM), and anti-HaloTag (133 nM) antibodies. The 1:1 interaction model is overlayed on the double-referenced data (red dashed line). (FIG.13D) Iso-affinity plot of the kinetics measured for the mouse anti-p53, anti- Jun, and anti-Src antibody injections (p53 = 20 spots across 3 sensors; Jun = 3 spots across 2 sensors; Src = 9 spots across 4 sensors). FIGS. 14A-14G shows Carterra LSAXTantibody kinetic data obtained from a 2,400 feature SPOC chip produced from the high-density 30k nanowell slide(1” x 3”). (FIG. 14A) Image of the SPOC sensor surface (30k array) as viewed from the flowcell of the LSAXT. (FIG. 14B) Sensograms from the Mouse anti-p53 and anti-Src antibody injections across 384 spots of NHv430k array are shown with positive binding responses unshaded, and areas with no Atty Dkt. No.155529.00024 binding responses shaded grey. Also shown are shown larger versions of the 384 ROI sensogram from the 30k array after anti-p53 antibody injection (FIG. 14C) and anti-Src antibody injection (FIG. 14D). Representative sensograms of the protein-specific antibodies binding to their specific protein targets (p53 or Src) are shown enlarged in FIG.14E and FIG. 14F. (FIG.14E) Six representative sensograms from a total of twenty-four p53-HaloTag spots bound by the p53 antibody from FIG.14B are shown and the binding responses after the mouse anti-p53 antibody injections are displayed, the 1:1 interaction model (red solid lines) is overlayed on the double-referenced data. Concentrations of antibody titration are shown in the key above the sensograms alongside a sensogram showing binding responses observed on a representative control spot of the array. ( FIG.14F) Six representative sensograms from a total of twelve Src-HaloTag spots are shown and the binding responses after the mouse anti-Src antibody injections are displayed, the 1:1 interaction model (red solid lines) is overlayed on the double-referenced data. (FIG. 14G) Iso-affinity plot of the kinetics measured for the mouse anti-p53 and anti-Src antibody injections (p53 = 26 spots across 2 sensors; Src = 19 spots across 2 sensors). FIGS. 15A-15J shows OpenPleX data of antibody interactions on SPOC chip. (FIG. 15A) Image of the SPOC sensor surface (10k pattern) as viewed from the flowcell of the OpenPleX. The hockey-stick shaped orientation mark is visible in the center of the array. (FIG. 15B) Difference image showing the pattern of binding observed on the SPOC biosensor surface after injection of mouse anti-HaloTag antibody (133 nM). The varying intensity signals, consistent with the observation in FIG.13B, highlight differential expression and capture levels between SPOC proteins at expected locations based on the printed DNA (array map for the OpenPleX sensor is shown in FIG. 15C. (FIG. 15D) Difference image showing the pattern of binding observed after the Mouse anti-Jun antibody injection (200 nM) which specifically detects two spots (yellow marks) where Jun-HaloTag was capture-purified as expected. (FIG. 15E) 1:1 fit (orange solid line) OpenPleX data of the anti-Jun antibody binding to Jun Spots #1 and #2 from FIG. 15D show similar binding profiles and equilibrium dissociation constants. (FIG. 15F) Difference image shows the pattern of binding observed after the mouse anti-p53 antibody injection (17.8 nM) which specifically detects four spots (yellow marks) where p53- HaloTag was capture-purified as expected. (FIG. 15G) 1:1 fit (orange solid lines) OpenPleX data of the anti-p53 antibody binding to the four p53 spots detected in Fig. 15F show similar binding profiles and equilibrium dissociation constants. (FIG. 15H) Iso-affinity plot of the kinetics measured for the Mouse anti-p53 and anti-Jun injections (p53 = 10 spots across 3 sensors; Jun = 4 spots across 2 sensors). After the citrullination reaction and injection of anti- Atty Dkt. No.155529.00024 cit-HIST1H3A and secondary antibody, the resulting SPRi difference image (FIG. 15I) and graph of reflectivity change (FIG.15J) shows marked increase in HIST1H3A spots by contrast and reflectivity signals (green top and bottom boxes). FIGS. 16A-16M show nanowell slide design facilitates high-density SPOC arrays capture-purification. (FIG. 16A) Mouse anti-HaloTag fluorescence staining pattern observed on a high-density 30k nanowell slide design demonstrates expression of HaloTag fusion proteins in the nanowells of two subarrays containing various expression plasmids of interest. (FIG.16B) Mouse anti-HaloTag fluorescence staining pattern observed on a glass capture slide functionalized with HaloTag chloro-alkane linker reveals diffusion-free capture of the high- density protein array generated from the 30k nanowell slide after capture-purification on the SPOC instrument. (FIG.16C) A comparison of the spot patterning achieved on the 10k versus the 30k nanowell slides, and the respective spot densities achieved; at 1000 spots / cm2with LSAXTanalysis area of 1000 spots, and density of 1, 975 spots / cm2and LSAXTanalysis area of 2,400 spots. The 30k design generates a >2-fold denser array than the 10k. FIGS.16D-16K show automated production of pure in-situ protein arrays capture-purified on glass. (FIG.16D) Validation data showing fluorescence images of Halo-tagged proteins expressed in silicon nanowell, probed with anti-Halo, (FIG. 16E) captured onto HaloTag chloro-alkane linker- modified capture glass slides which were probed with anti-Halo, and (FIG.16F) the same slide layout as shown in FIG. 16E, but probed with anti-p53 antibody to detect p53 protein spots. These images show strong HaloTag and p53 signals on capture-purified protein arrays on capture glass slide, with <2% crosstalk between proteins in neighboring spots where protein was otherwise detected in FIG. 16E. (FIG.16G) is statistical analysis of cross reactivity (CR) and variation (CV) of p53 spot signals calculated from each block as a whole (sparse and dense areas). The enlarged images show the dense area only of each block to demonstrate lack of signal between spots and in neighboring non-p53 spots. Functional validation testing of protein arrays captured on glass slides. (FIG.16H) demonstrates the enzymatic capability of bound Src kinase proteins via on-slide phosphorylation and dephosphorylation. Slide 1–3 were stained with anti-phosphotyrosine (n = 12 Src spots). Slide 1 consisting freshly expressed and captured Halo-SRC kinase protein spots was dephosphorylated by incubating with alkaline phosphatase and then stained. Slide 2 - Src spots were phosphorylated by incubating with kinase buffer to allow Src to autophosphorylate, followed by anti-phosphotyrosine staining. Slide 3 – Src was dephosphorylated again followed by staining. (FIG. 16I) Proteins can be altered with known PTMs associated with autoimmunity, such as citrullination. Here, Halo-fibrinogen (FGA) and Halo-Hist1H3A spots were treated with or without PAD2 enzyme to catalyze citrullination and Atty Dkt. No.155529.00024 then probed with citrullination-specific anti-cit-FGA and anti-cit-HIST1H3A. (FIG. 16J) Capture protein can bind to known protein interaction partners, such as Fos and Jun. Halo-Fos and Halo-Jun spots were incubated with (FIG.16J) or without (FIG.16K) recombinant Fos in solution. Slides were then probed with anti-Fos to determine if Fos bound to Halo-Jun. Fos was detected on Halo-Jun spots only if first incubated with rFos, and was detected at all spots known to contain Halo-Fos as a positive control – demonstration functionality of produced capture- purified protein arrays. FIGS. 16L and 16M show fluorescence assay on glass surfaces demonstrating IVTT expressed and capture-purified proteins are stable and active for 10 days in storage. IVTT expressed HaloTag fusion proteins were manually captured onto a functionalized glass slide and a variety of assays were performed including (i) detection of CoV-2 RBD variants with primary and secondary antibodies, (ii) PAD2 mediated citrullination of IVTT expressed HIST1H3A, and (iii) capacity for recombinant Fos protein (rFos) to interact with IVTT expressed Jun. Fluorescent secondary antibodies were used to detect all bound proteins and their modification. Slides with captured IVTT proteins were prepared and stored (FIG.16L) overnight at 4°C in PBS supplemented with 5% glycerol before assay or (FIG.16M) stored for 10 days at -20°C in PBS with 50% glycerol prior to assay. FIGS.17A-17D show Carterra LSAXT screening of mouse anti-SARS-CoV-2 receptor binding domain (RBD) against RBD variants arrayed on SPOC chip. (FIG. 17A) Orthogonal fluorescent glass-slide assay shows the staining pattern observed after capturing via manually spotting the indicated IVTT expressed SARS-CoV-2 RBD-HaloTag proteins and probing with mouse anti-RBD and HaloTag antibodies. (FIG. 17B) Sensograms reporting the binding responses observed after a serial titration of mouse anti-RBD antibody (Lot 2) was injected over a SPOC array containing 10 RBD VoC via the Carterra LSAXT.1:1 fits are overlayed on the data (red curves) and the equilibrium dissociation constants were determined from duplicate screens from Lot 2 of the antibody. FIG. 17C shows faster off rates. ( FIG. 17D) Iso-affinity plot of the various kinetics measured for the mouse anti-RBD antibody from two different lots against the RBD VoC are reported. The antibody from Lot 1 was only screened on a sensor with 6 of the RBD VoC while Lot 2 was screened across 2 sensors containing all 10 CoV-2 RBDs of interest.  FIGS. 18 depicts an exemplary capture method with Halo tag used to capture Ig-like proteins onto the biosensor surface, which is coated with chloro-alkane linker or Halo ligand. FIGS.19A-19B depict cell-free expression (CFE) of preliminary single-chain fragment variable (scFv) antibodies and nanobodies (Nb) tested on SPOC array. CFE from IVTT mix derived from human cell lines (FIG. 19A) and manual plasmid DNA spotting onto biosensor Atty Dkt. No.155529.00024 via pipette transfer for analyte screening (FIG. 19B). All scFv / Nb are fused to HaloTag at C- terminus. FIG.20 shows expression data of scFvs and nanobodies from human-derived IVTT mix and constructs. FIG.21 shows expression data of scFvs and nanobodies from E. coli-derived IVTT mix and linear DNA constructs. Conditions: 1. Reducing (E. coli lysate 1), no disulfide bond enhancer.2. Non-reducing (E. coli lysate 1, no disulfide bond enhancer). 3. Non-reducing (E. Coli lysate 1) and disulfide bond enhancer.4. Non-reducing (E. coli lysate 2) no disulfide bond enhancer.5. Non-reducing (E. coli lysate 2) and disulfide bond enhancer. FIGS. 22A-22B depict an experimental outline for FIGS. 23-33 for screening of recombinant proteins and serum on biosensor chips containing scFvs and nanobodies using a sandwich assay approach. FIGS. 23A-23D show representative data for biosensor screening of an anti-TNFα scFv. FIGS.24A-24C show representative data for biosensor screening of an anti-IL-6 scFv. FIGS.25A-25D show representative data for biosensor screening of an anti-IFNγ scFv via direct sensing. FIGS.26A-26D show representative data for biosensor screening of an anti-IFNγ scFv via sandwich assay. FIGS.27A-27C show the data results from Figures 23-26. FIGS. 28A-28B represent data for biosensor screening of an anti-IgM nanobody with human serum with direct sensing. FIGS. 29A-29B represent data for biosensor screening of an anti-IgM nanobody with human serum with sandwich assay approach. FIGS. 30A-30B represent data for biosensor screening of an anti-IgE nanobody with human serum. FIGS.31A-31B represent data for biosensor screening of an anti-IgG scFv with human serum. FIGS.32A-32B represent data for biosensor screening of an anti-HSA scFv with human serum. FIGS.33A-33B show the experimental results from Figures 28-32. FIG. 34 is SDS-PAGE analysis of C-Terminal HaloTagged scFvs expressed using the Human 1-Step Coupled IVT cell free lysate. All expressed constructs were first covalently Atty Dkt. No.155529.00024 labeled with fluorescent Halo ligand then analyzed in reducing (R) and non-reducing (NR) conditions. FIG.35 is SDS-PAGE analysis of C-Terminal HaloTagged scFvs and VHH expressed using the PUREfrex 2.1 E.coli cell free expression system. All expressed constructs were first covalently labeled with fluorescent Halo ligand then analyzed in reducing (R) and non- reducing (NR) conditions. A subset of constructs was expressed before (-Opt) and after (+Opt) 5’ RNA structure optimization. FIGS. 36A-36D is multiple single chain antibodies, scFvs and VHHs expressed in E.coli lysate result in high levels of protein expression and capture on the biosensor surface. (FIGs. 36A, 36B) anti-Halo signals that confirm expression of the single chain antibodies in scFv and VHH or nanobody formats (FIG.36C) scFvs produced and captured - anti-p53 scFv, anti-HSA scFv, anti-IL6, anti-EGFR scFv, bound to their target antigens (FIG. 36D) VHH nanobodies TNFα VHH, and HER2 vHH, bind to their respective target antigens, detected in a label-free format. FIG. 37A-E show two VHH constructs for TNFα and HER2 chosen for full kinetic analysis and for determination of the limit of detection for the target antigen. (FIG. 37A) Injecting TNFα over the biosensor in increasing concentration from 28 pM to 20 nM using 7 dilutions total resulted in an estimated affinity (KD) of < 49 pM. This is similar to the previously reported affinity of 90 pM for this VHH. (FIG.37B) Due to the very high affinity of TNFα for the TNFα VHH, this affinity constant is an estimate because practically no dissociation of TNFα was observed during the 30 minute dissociation window; kinetics measured over a significantly longer dissociation window will be required for more accurate calculation of KD. (FIG.37C, FIG.37D) Similarly, the affinity constant for HER2 binding to the HER2 VHH was calculated to be 11.5 nM using an increasing 5-step titration of HER2 antigen from 1.8 nM to 150 nM. This is similar to the reported KDof 4 nM in literature for this VHH. (FIG.37E) Based on these data, the limit of detection for TNFα via the TNFα VHH was measured as < 100 pM in solution, and the limit of detection for HER2 antigen to the HER2 VHH was measured to be < 2 nM in solution. FIG. 38 illustrates chaperone combinations that can be used to assist Ig-like protein folding9. Figure adapted from Ref.34. FIGS. 39A-39C show cyclic scFv formation and sequence requirements. Figure adapted from Ref.156. Atty Dkt. No.155529.00024 FIG. 40 illustrates the E. coli disulfide bond formation pathway, as a representative example of all species, showing the proteins and reactions required to occur within the IVTT mix to achieve proper disulfide bond formation. Figure adapted from Ref.89. FIG.41A-41B illustrate an Ig-like protein (Fab) containing a leucine zipper to enhance dimer formation in addition to a plurality of other fusion tags which can be placed in-frame with proteins to promote higher order multimeric complex formation. FIG.41A: adapted from Ref.104, ; FIG.41B: adapted from Ref.3. FIG. 42 illustrates an example experimental workflow for directly generating genetic constructs for Ig-like proteins from B cells derived from any organism for subsequent IVTT expression and capture onto biosensor surfaces for screening binding and kinetic parameters. Adapted from Ref.106. FIG.43 displays an exemplary demonstration of mutational scanning. A protein shows the wild-type native protein (top row) and the series mutational scanned variants (below the top row) derived from introducing single-amino acid substitutions systematically along the length of the peptide chain (in this case a combination of serine, tryptophan, and alanine). Figure adapted from Ref.44. FIG.44 is an overview of an exemplary microarray for high-throughput oligonucleotide synthesis. Gene synthesis would occur in situ on a solid-phase support (e.g. silicon slide) at discrete locations as nucleotides are incorporated onto unique primer supports within these locations enabling the synthesis of a catalogue of wild-type genes and mutationally scanned gene variants. Figure adapted from Ref.94. FIG. 45 is a graphical representation of one example method utilizing biosensors and kinetic monitoring of analytes within patient samples to make important clinical determinations. FIG. 46 illustrates a protein ligand-capture ligand (capture tag) fusion labeled with its coding sequence, bound to a biosensor slide. FIG.47A-47B are examples of commercially available photocleavable linkers. FIG. 48A-48C are examples of Puromycin-Linker DNA. FIG. 48A is a commercially available enzymatically cleavable linker using PAB. FIGS. 48B and 48C show examples of puromycin-based method for linking coding sequences to translated protein. FIG. 49 is an illustrative schematic of boxB mRNA hairpin being used for labeling a protein with its coding sequence via lambaN peptide. FIGS.50A-50B is the LambdaN peptide sequence and boxB mRNA hairpin sequence. Atty Dkt. No.155529.00024 FIG. 51 illustrates cross-reactive proteins, or antigens, with similar epitopes resulting in an antibody binding to multiple protein targets rather than a single specific protein. Illustrated proteins may represent different species, genetic homologs or orthologs. FIG. 52 is a workflow for epitope mapping from biofluids using alanine scanning technique of mutationally scanned protein arrays, to determine binding data across mutated variants, and identify an epitope. FIGS.53A-C Partial or deep mutational scanning (DMS) of therapeutic targets: single amino acid residues within the epitope regions of therapeutic targets, CD20 and PD1, were substituted with side chains of diverse functionality, including alanine, aspartic acid, lysine, and serine, resulting in a comprehensive mutant library. The recombinant expression plasmids array printed in nanowell slides was then used to produce SPOC protein biosensor via in-situ cell-free nanowell protein expression / capture for subsequent screening by SPR against FDA- approved therapeutic mAbs to yield binding kinetics data. Amino acid sequences of (FIG.53A) CD20 and (FIG. 53B) PD1 extracellular domains, showing the positions of mAbs epitopes identified in literature, including rituximab, ublituximab, ocrelizumab and nivolumab, pembrolizumab, cemiplimab. Cysteine and asparagine residues highlighted in red fonts are responsible for post translational disulfide bonds and glycosylation, respectively. Residues targeted for substitution were underlined. (FIG. 53C) CD20 mutant library, indicating substituted positions. FIGS.54A-54C shows silico recombinant plasmid maps of (FIG.54A) CD20 and (FIG. 54B) PD1 extracellular domains sequences. Each gene was subcloned onto a custom cell-free expression-compatible pT7CFE1 plasmid for expression with an N-terminal HaloTag (for CD20) or C-terminal HaloTag (for PD1). (FIG.54C) SPR flowcell image of a SPOC biosensor, showing distinct protein spots as well as a hockey-shaped fiducial (highlighted in red square) for locating each member protein. FIG. 55 is a group of sensograms for expression and capture validation of CD 20 mutational-scanned target protein library produced on SPOC chip, using anti-HaloTag VHH, mouse anti-HaloTag, and mouse anti-p53. FIG. 56 is a group of sensograms of serial injections of rituximab, showing varying apparent kinetic affinities (KD’) and other kinetic parameters of interactions with CD20 mutants. FIG.57 is a group of sensograms of serial injections of Ocrelizumab, showing varying apparent kinetic affinities (KD’) and other kinetic parameters of interactions with CD20 mutants. Atty Dkt. No.155529.00024 FIG.58 is a representative comparative analysis of protein expression and capture. This heatmap shows the response level of SPOC biosensor proteins to 220 nM anti-HaloTag VHH. It validates the expression of CD20 mutants on the chip and compares the capture levels against CD20 wildtype and few controls. Protein capture level was also screened using a mouse anti- HaloTag and rabbit anti-HaloTag antibodies. Overall, the capture validation result indicates that all CD20 mutants were relatively well-expressed compared to the wildtype CD20 and controls except variants T159K and I162S, which produced very low RU values of 0.5 and 2.3 RU, respectively. Key: Column shows amino acid residues on wildtype CD20 targeted for substitution. Row indicates the type of amino acid side-chain substitution. Crossed cell indicate no substitution for that wildtype residue. FIGS. 59A-59B are heatmaps of maximum response unit (Rmax) of CD20 mutants to the highest injected concentrations of (FIG. 59A) rituximab (505 nM) and (FIG. 59B) Ocrelizumab (522 nM). Rmax below 10 units were assumed to indicate zero binding or very low interaction. In 99% of cases, all substitutions of residues of rituximab and ocrelizumab epitopes residues (area within the red lines) resulted in zero and negative Rmax values compared to the wildtype, implying a significant decrease in binding relative to the wildtype. Multiple alanine substitutions of the epitope as in E168:P169:N171 and P172:S173:E174:K175 completely eliminated interactions. Further, disulfide bond mutations C167 and C183 resulted in lower response unit compared to the wildtype. CD20 variants, T159K and I162S, were poorly expressed / captured (marked ‘x’). Key: Column shows amino acid residues on wildtype CD20 targeted for substitution. Row indicates the type of amino acid side-chain substitution. Crossed cell indicate no substitution for that wildtype residue. FIGS. 60A-60E show KD'sobtained following substitutions of rituximab epitope residues (EPANPSEK), with (FIG.60A) alanine / neutral non polar side chain, which includes single and double alanine-substituted cysteine-167 and 183 residues responsible for disulfide bond formation as well as multiple alanine substituted mutants of EPANPSEK; (FIG. 60B) aspartate or acidic side chain acid, (FIG. 60C) lysine / basic side chain, and (FIG. 60D) serine / neutral polar side chain. Broken blue lines indicate KD' of the wildtype CD20 for comparison. (FIG. 60E) is a full heatmap of apparent kinetic affinities (KD') of CD20 mutants to rituximab. Green lines enclose rituximab epitope region. The term apparent binding affinity (KD') was used to distinguish from actual binding affinity (KD) since the full rituximab antibody was used for screening, not Fab. FIGS. 61A-61E show KD'sof substituted ocrelizumab epitope residues (YNCEPANPSEKNSPSTQYC). (FIG. 61A) alanine / neutral non polar side chain mutants, Atty Dkt. No.155529.00024 including multiple alanine substituted mutants and substituted cysteine-167 and 183 residues responsible for disulfide bond formation; (FIG. 61B) aspartate or acidic side chain acid mutants, (FIG. 61C) lysine / basic side chain mutants, and (FIG. 61D) serine / neutral polar side chain mutants. Broken blue lines indicate KD' of the wildtype CD20 for comparison. FIG.61E is a heatmap of apparent kinetic affinities (KD') of CD20 mutants to ocrelizumab. Green lines enclose ocrelizumab epitope region. The term apparent binding affinity (KD') was used to distinguish from actual binding affinity (KD) since the full ocrelizumab antibody was used for screening, not Fab. FIG.62 shows a schematic of a full-length IgG with key domains identified such as the constant (Fc) and variable (Fv) regions formed by the heavy and light chains, as well as the three complementary- determining regions (CDRs) of the Fv domain directly involved in epitope binding. The schematic also highlights various antibody fragments which can be expressed individually, or generated as a result of enzymatic cleavage of the parent full-length antibody, that are capable of antigen binding and which can be synthesized and screened on SPOC chips. FIG.63A-63B are schematics of exemplary embodiments of SPOC linear DNA library construction from single B cell (SBC) and phage-derived clones, showing the construction of HaloTag Ig heavy and light chain linear expression cassettes (VH / VL_LECs) for printing in nanowell slide and cell-free expression using cell lysate (Adapted from Zhang et al. 2021, Ref.162). The original method of Zhang et al. 2021 protocol was modified to enable implementation on the disclosed SPOC platform by (1) splicing HaloTag sequence to 5’ terminal of the original VH and VL fragments, and (2) printing the LECs in nanowells for cell- free expression and immobilization on HaloTag chloroalkane-functionalized biosensor chip via the Halo capture tag for instant functional kinetic screening. Construct: HaloTag VH_LECs (FIG.63A part a) and VL_LECs (FIG.63A part b) were constructed by first assembling Halo capture tag gene sequence and VH or VL PCR products derived from sorted single B cells or monoclonal phagemids using Gibson Assembly or splicing by overlap extension (SOE) PCR. This was then followed by overlapping PCR assembly of the HaloTag / VH or VL construct with (1) pCMV fragment consisting of CMV promoter, enhancer and Ig leader, which was amplified from the plasmid of pTT5-mIgG; and (2) CH fragments or CL fragments containing the Ig constant region, stop codon, and rabbit beta globin (rbGlob) polyA tail amplified from the plasmids of pTT5-mIgG1, pTT5-mIg kappa, pTT5-huIgG1, pTT5-huIgG1_Fab, pTT5- huIg kappa, or pTT5-huIg lambda. The forward primer at the start of PCMV fragment and the reverse primer at the end of rbGlob polyA in CH / CL fragments were used for constructing VH / VL_LECs by overlapping PCR. This construct is expressed as HaloTag fusion protein Atty Dkt. No.155529.00024 which can be expressed by cell-free methods and then captured on biosensor chip via the HaloTag for label-free kinetic analysis e.g., SPR as shown in FIG 63B. FIG.64 is a map of the commercial T7 Cell-free Expression Vector. The Custom SPOC pT7CFE1_nHalo_cHis vector was derived from this commercial vector. FIGS.65A-65B is a schematic of plasmid and linear expression DNA library generation from single B cell and phage clones using highly optimized in vitro translation plasmids or derived DNA fragments. FIG. 65A shows antibody fragments are amplified from SBC and phagemids using PCR. The amplicons are then incorporated at 5’ and 3’ ends with complementary sequences from plasmid-derived DNA fragments for subsequent (FIG. 65B) gene assembly by splicing by overlapping extension (SOE) PCR to yield linear expression fragments library. Alternatively, antibody fragment amplicons can be clone directly onto linearized plasmid template to yield circular recombinant plasmid library. FIG.66 illustrates SPOC areas of applications in the study of proteins and proteoforms at scale, disease science, drug discovery and development, plasma proteomics, and diagnostics. FIG.67 illustrates an array of single chain antibody fragments produced on SPOC chip. The antibody array chip can be used to screen with therapeutic target and down-select antibodies with optimal kinetic binding parameters and sequence diversity for therapeutics development. The antibody array chip can be used to identify and characterize target binding site on an antibody (paratope) with single amino acid resolution and kinetic data. To identify the specific amino acids in the antibody CDR regions that are critical to binding the target or analyte, the amino acids in the CDR regions and the neighboring regions are mutationally scanned, by replacing one or more amino acids, sequentially or tiled, with alanine and / or other amino acids. The mutationally scanned antibody chip is then screened with target or analyte, and change in binding characteristics is measured. Loss or gain of binding (or binding affinities) caused by the specific mutations indicate the amino acids critical to binding (paratope) the target. To produce antibody sequences with high or optimal binding parameters or therapeutic development properties, mutations are induced in the antibody CDR and neighboring regions to create large mutational antibody libraries on SPOC chip. Mutations can be induced using in-silico modeling, ML or AI drug design, or randomly, or using other approaches. By screening with target or analyte, the antibodies sequences with high or optimal target binding parameters or therapeutic development properties can be down selected. FIG. 68 illustrates On-chip synthesized array of antibodies or antibody fragments for plasma proteomics. FIG.69 illustrates high plex detection of analyte by antibody fragments on SPOC chip. Atty Dkt. No.155529.00024 FIG. 70 illustrates following initial screening or assay with plasma sample or analyte, a reagent assay is performed to immobilize or covalently link the bound plasma proteins or analytes to the Ig-like proteins, scFvs, or nanobodies on the biosensor or surface, or to directly immobilize or covalently link plasma proteins or analytes to surface of the biosensor or substrate. FIG. 71 illustrates secondary antibody, aptamer or other secondary affinity binder detection. FIG. 72 illustrates secondary affinity binders can be biosensor signal amplification agents. FIG. 73 illustrates secondary antibodies or affinity binders bind to specific mutations or post translational modifications (PTMs) or isoforms or variants on plasma proteins or analytes. FIG. 74 illustrates protein – protein interaction assays or other functional assays are performed on plasma proteins or analytes bound to the biosensor or chip. FIG.75 illustrates therapeutic drug molecules assayed for binding with plasma proteins or analytes bound to the biosensor or chip, FIG. 76 is a flowchart setting forth the steps of an example method for generating classified feature data that are indicative of a diagnosis and / or prognosis of one or more of a plurality of autoimmune or other diseases, including but not limited to one or more SARDs, by inputting kinetic parameter data and epitope data to a trained machine learning model. FIG. 77 is a flowchart setting forth the steps of an example method for training a machine learning model in accordance with some examples described in the present disclosure. FIG.78 is a block diagram of an example system for detecting one or more of a plurality of autoimmune diseases, including but not limited to one or more SARDs. FIG. 79 is a block diagram of example components that can implement the system of FIG.78. FIG.80 shows seroscreen of p53 antibody detection via SPR on a SPOC chip screened with serum with known p53 antibodies. FIGS. 81A-81D show label-free real-time detection of biomolecular arrays on a biosensor surface via an exemplary method for kinetic data collection via surface plasmon resonance imagine (SPRi). SPRi monitors increases in reflected light that occurs over baseline. When an analyte binds to the proteins immobilized near the biosensor surface, in this example an antibody, a change in the refractive index at that particular spot leads to an increase in the change in percent reflected light. FIG.81A shows an increase in reflected areas of light where Atty Dkt. No.155529.00024 interactions occur, which leads to the spots becoming visible in the difference image (912). FIG.81B is a sensogram showing the change in percent reflectivity over time with each colored trace corresponding to an individual protein spot on the biosensor surface (916). Additionally, FIGS. 81C-81D illustrate how immunoglobulin isotyping can be performed label-free and in real-time in the context of this exemplative SPRi application. FIGS. 81C-81D show an experiment in which a primary mouse IgG antibody against the biomolecule capture tag was injected over the biosensor surface which could be isotyped following kinetic monitoring by subsequent injection of an anti-mouse IgG secondary antibody. The secondary antibody itself was conjugated to Horseradish peroxidase (HRP) and therefore biomolecules bound by both primary and secondary antibodies could be further detected with injection of a tertiary anti- HRP antibody. FIG. 81C shows SPRi difference images after the above three sequential antibody injections with distinct increases in reflectivity at specific areas of the biosensor following each injection. FIG.81D shows a graph of the signal obtained from each biomolecule on the array showing continuous and quantitative monitoring of the increases in reflectivity observed after each antibody injection. This scheme of secondary and tertiary antibody injection can be utilized to assess the isotype of bound immunoglobulins after injection of patient samples. FIGS. 82A-82D include images and graphs illustrating data comparing the binding observed between two different antibodies with varying binding kinetics against a common capture tag (in this case HaloTag) which was utilized to capture the biomolecule array antigens to the glass or biosensor substrate. Results from the fluorescent, end-point assay on glass show disparities between the overall staining observed of the proteins on the slide between the two primary antibodies, and the results from the binding events observed on the biosensor reveal that the antibody with apparent poor binding / staining of protein on the glass slide (Mouse anti- Capture Tag), shown in FIGS.82A-82B, had comparably faster dissociation kinetics compared to the other antibody (Rabbit anti-Capture Tag), shown in FIGS.82C-82D, which was readily detected in the end-point fluorescent assay. SPOC enables screening lead molecules against on-chip protein targets, to detect even fast off-rate binders missed by current technologies. Current fluorescence and other end-point methods miss fast off-rate binders due to wash steps - potential source of toxicity. SPOC SPR real-time biosensing detects fast off-rate binders, which if unaddressed can result in toxicity during Phase I safety trials. FIGs.83A-83E include images and graphs illustrating post-translational modifications introduced to the SPOC chips by citrullination, phosphorylation, and disulfide bonds. (FIGS. 83A-83C) show results from the detection of citrullinated HIST1H3A and FGA protein after Atty Dkt. No.155529.00024 protein microarrays captured on glass or biosensor slides were treated with PAD2 enzyme demonstrating the feasibility of introducing post-translational modifications after protein microarray assembly on various substrates. (FIG. 83D) shows results from the detection of phosphorylated SRC protein after protein microarrays captured on slides. (FIG. 83E) shows the results of detection of disulfide bonds with primary antibody. SARS-CoV-2 Receptor binding domains (RBDs) were expressed by in vitro transcription and translation either with (DSB+) or without addition of reagents to expression reactions that generate a favorable redox environment for disulfide bond formation in nascent, cell-free synthesized proteins (e.g. glutathione disulfide or co-factors like PDI, DsbC, etc.). The fluorescent immunostaining of these reactions with anti-RBD therepeutic antibodies reveals that disulfide bond formation was essential for detecting therepeutic antibody binding, a sign that the RBDs expressed in cell-free redox environments that promote disulfide bond formation are folding in a more physiologically relevant confirmation. Bamlanivimab and Etesevimab were developed pre- Omicron and therefore only stain reactions where wild-type (Wuhan) RBD is expressed. By contrast, Tixagevimab and Cilgavimab were developed to target later-emerging Omicron variants as well, and therefore these thAbs are seen to stain reactions expressing both Wuhan and Omicron variant RBD proteins. FIGS.84A-84F illustrate an example application of Protein G resin for the purification of IgG from serum and a comparison of using serum versus purified IgG as an analyte on an SPR biosensor spotted with an array of AD related antigens, which include: (FIG. 84A) a drawing illustrating a step of a protein G purification protocol; (FIG.84B) a photo of a stained acrylamide gel illustrating purified IgG; (FIGS. 84C-F) graphs illustrating SPR analysis of whole serum (FIGS.84C and 84E) and purified IgG (FIGS.84D and 84F) samples. FIGS. 85A-85C demonstrate the use of secondary antibodies to detect specific immunoglobulins bound to the biosensor surfaces. FIG. 85A is a difference image and FIG. 85B is a graph illustrating results from the detection of Aabs against p53 protein from the serum of a cancer patient and an exemplary demonstration of the use of secondary antibodies to detect specific immunoglobulins (in this case IgG) bound to the biosensor surfaces. Detection of the primary anti-p53 Aab kinetics are also shown in the kinetic traces in the panel on the right without the use of secondary antibody to boost the signal and is exemplative of a patient- specific kinetic trace that would form part of a patient test profile (FIG. 85C). A subsequent secondary anti-human IgG injection confirms the serotype and presence of the p53-bound Aab detected from the cancer patient serum, and injection of a negative control serum from a patient Atty Dkt. No.155529.00024 lacking anti-p53 Aabs (known to contain anti-Tau Aabs instead) demonstrates that the anti-p53 primary signal observed was specific to the cancer patient serum. FIG.86 is a flow chart representation of one exemplary method of utilizing biosensors and kinetic monitoring and characterization of analytes within patient samples to inform important clinical determinations. FIG.87 depicts SPOC antigen biosensor array enables deep characterization of immune responses in a single assay. SPOC SPR screening facilitates measuring antibodies against 100s to 1000s of Infectious diseases targets, or auto-antibodies in autoimmune and autoantibodies against cancer neo-antigens. With scFvs or VHH nanobodies on chip, SPOC can also detect cytokines, chemokines, TCR receptors, immune and inflammation markers to assess cell-based immune response, plus other diverse protein and metabolic biomarkers of disease of interest. FIG. 88 shows sensograms from serum sample from a 45-year-old patient who tested positive for COVID-19 three times up to 10 days prior to the sample collection data was screened against pan-pathogen SPOC protein array comprising proteomes and antigens from 11 different viral and bacterial pathogens. Following serum screening, secondary Ig-specific antibodies were used for antibody isotyping, to assess disease status, type-switching and immune response maturation. FIGS. 89A-89D depict antibody isotyping from patient serum to characterize IgG / IgA / IgM responses to multiple infectious disease targets (HKU1 orf1ab, HKU1 NS8, HRV-A NA, HRV-C NA, HPIVa NP, NL63 HCNV63gp1). The negative control is shown in FIG.89A. Distinct responses are shown with some targets eliciting IgM only (FIG.89B), IgG only (FIG.89C), or IgG and IgM (FIG.89D). Gold-coated SPR biosensor chips were prepared with human cell-free lysate expressing proteins directly from plasmid DNA nanowell arrays with simultaneous capture onto the biosensor surface via the fusion capture tag. Patient serum was diluted in running buffer and flowed over the chip while collecting real-time SPR kinetic data. Negative controls of no plasmid DNA and capture tag only were included and are shown in the figure with no response to patient antibodies in the serum. Secondary antibodies for IgG, IgA, and IgM were subsequently flowed across the chip immediately after patient serum and the SPR kinetic response measured. FIG. 90 is an illustration of a full-length antibody binding to interferon gamma (IFN- γ). An illustration of how this interaction may be screened on SPOC is shown on the right, whereby the scFv fragment of the antibody is expressed cell-free as a HaloTag fusion protein and captured onto SPR biosensor surfaces for downstream analyte screening. In this exemplary case IFN-γ is depicted as the analyte bound to its respective scFv. Atty Dkt. No.155529.00024 FIG.91 illustrates the antibody structure and amino acid sequences of various regions, including variable regions (or the Fv region); FR = framework region (conserved structural region); CDR = complementarity determining region (region that binds antigen); CH or CL = constant heavy or constant light domain. FIG. 92 illustrates post production of the sequences as in FIG. 91. The beginning and ends of variable sequences on heavy and light chain are identified and a gene construct engineered using known sequences, the gene construct is cloned in a Halo plasmid, expressed in nanowells and captured on a slide. FIG. 93 shows exemplary data validating the expression and activity of an anti-IgM scFv gene construct, as shown in previous FIG. 92, expressed by in vitro transcription and translation as a HaloTag fusion protein. The expressed anti-IgM scFv was captured onto a HaloTag ligand coated glass surface and probed with various analytes including serum, SuperBlock buffer alone, or a Rabbit anti-HaloTag primary antibody. Secondary anti-human IgM antibody incubation detected successful binding of IgM present from within the serum to the anti-IgM scFv on the surface (first condition in the far-left first lane). Anti-IgM secondary did not show any such binding signal when the primary analyte was simply buffer alone (lane 2) indicating that signal is not due to non-specific binding of the secondary antibody. Furthermore, primary serum incubation and follow up with an anti-IgG secondary antibody reveals the specificity for the scFv construct in binding only IgM immunoglobulin, and not IgG (lane 3). Finally, cell-free expression and glass surface capture of the anti-IgM scFv construct was validated, since Rabbit anti-HaloTag primary antibody was able to bind the ligand as determined by secondary anti-Rabbit antibody signal (lane 4, far-right lane). FIGS. 94A-94B show results from fluorescent immunostaining and label-free SPR screening validating expression of BA1 variant of the SARS-CoV-2 RBD domain protein expressed cell-free via SPOC assay from a linearized plasmid vector. The RBD protein was expressed and captured onto glass (FIG. 94A, fluorescent screen) or on SPR biosensor (FIG. 94B, label-free detection) and expression / capture was validated using anti-HaloTag antibodies in both cases. Results from fluorescent assay show that BA1-RBD protein encoded in linear plasmid DNA constructs was efficiently expressed and captured onto glass slides producing similar anti-HaloTag staining intensity as did HaloTag only protein which was expressed from a circular DNA vector. Capture of the proteins onto biosensors further validate that expression was successful from the linearized plasmid DNA vector. In the panel on the right, SPR results show that BA1-RBD protein expressed from a linearized plasmid vector was detected by anti- HaloTag antibody injection producing a binding response with magnitude of up to ~200 RU Atty Dkt. No.155529.00024 (FIG. 94B top panel), similar to that of the same BA1-RBD expressed from an intact circular vector (~300 RU), (FIG.94B bottom panel). FIG.95 is a schematical overview of how nanodiscs are formed by two components: a lipids, such as 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), and structural peptides (e.g., ApoA1). Here, an expression plasmid for the structural peptide and the appropriate lipids are mixed with cell-free protein lysate which expresses the ApoA1. ApoA1 wraps around the lipids forming a raft structure that yields a nanodisc of defined shape and size which can be used for stabilizing membrane proteins for downstream screening. FIGS. 96A-96B illustrate incorporating membrane nanodisc technology with SPOC platform, to produce and capture full-length functional membrane proteins with transmembrane domains on SPR biosensor chips. (FIG. 96A) Membrane Nanodiscs enable full-length membrane protein expression and assembly in cell based and cell-free protein expression systems. (FIG. 96B) Co-express membrane proteins and nanodiscs in nanowells and capture on SPR biosensor chip. Nanodiscs are co-expressed in SPOC nanowells by dispensing plasmid- DNA for membrane-scaffolding-proteins (such as but not limited to) human apolipoprotein A1 along with plasmid-DNA of target membrane proteins, and adding lipid vesicles (neutral or partially anionic or cationic) or lipid reagents and other reagents to cell-free lysate system. Co- expressed and assembled membrane protein-nanodisc complexes, in respective nanowells, are captured as array-spots onto SPOC chips via HaloTag fusion protein, that is appended either to apolipoprotein or c-terminus of membrane proteins. DETAILED DESCRIPTION Disclosed are methods and systems for detecting one or more analyte associated with one or more clinical feature or outcome using a biomolecule array. The clinical feature may be an autoimmune or related disease or other disease. Various aspects of the present disclosure relate to methods and / or systems for detecting one or more analyte associated with one or more clinical feature or outcome, for example an autoimmune or related disease or other disease using a “biomolecule array” or “biosensor array.” “Biomolecule array” and “biosensor array” are used interchangeably throughout this disclosure. The array may also be more specifically defined, such as a “protein biosensor array.” In one or more aspects, the methods and systems for detecting one or more analyte associated with one or more clinical feature or outcome (autoimmune disease) can identify one or more kinetic binding parameter of the analyte to one or more biomolecule. In certain aspects, the kinetic binding parameters and optionally additional information may be used in diagnosis, Atty Dkt. No.155529.00024 stratification, and / or prognosis of clinical feature or outcome, such as autoimmune diseases or related diseases, including SARDs, or other disease. The disclosed biomolecular array (or multiplexed biosensor array) measures quantitative, qualitative, and kinetic data simultaneously that can be used in various applications, such as pathogen diagnostics / prognostics, vaccine development, pharmaceutical drug or therapeutic development (drug discovery), immune response characterization, phenotype surveillance, among other applications. Refinement of autoimmune disease stratification via analyte detection is a historical and on-going process that has progressed from non-specific assays and increasingly towards tests that determine the exact antigen targeted by autoantibodies present in specific patient serum. In antinuclear antigen (ANA) testing for example, rodent tissue was initially used in screening of autoimmune patient serum to determine ANA reactivity, however, these rodent cells did not express Ro (SS-A) antigen which led to the characterization of an “ANA-negative” lupus. However, with the implementation of the human Hep-2 tumor cell line substrate in ANA testing, Ro sensitivity could be determined, and those patients previously identified as ANA negative were in fact ANA positive with autoantibody profiles more specific to Ro. Hep-2 substrate to screen for patient nuclear antigen reactivity by immunofluorescence assay (IFA) remains a gold standard autoantibody profiling assay for informing the diagnosis of many autoimmune diseases (Ads), although the terminology is recognized to be outdated as the specific autoantigens involved have now been characterized of which many are non-nuclear. Modern testing regimens have shifted towards antigen-specific assays based on enzyme linked immunosorbent assay (ELISA) or multiplex bead assay (MBA) displaying the classical antigens associated with specific autoimmune diseases. Overall, as analyte screening has moved towards detection of specific analytes, a need to consolidate these biomarkers into miniaturized panels on which patient serum can be screened against all putative autoantigens has arisen to reduce screening time and testing costs while increasing diagnostic accuracy via pan autoimmune signature approaches. For instance, Aab testing performed today necessitates repeated screening across various assays tailored to detect only a few autoantibodies at a time, increasing time to diagnosis and costs. FIG.1 depicts an example Aab testing workflow using numerous assays. Protein microarrays include proteins spotted in an array format on any number of solid support substrates (traditionally glass) and have been leveraged in large-scale proteomic studies for over 40 years. Functional protein arrays using full-length antigens or short peptides have been successfully used in numerous Aab diagnostic and biomarker discovery seroscreening Atty Dkt. No.155529.00024 studies over the years. Notably protein microarrays have even demonstrated superior sensitivity (up to 4-8-fold) than traditional ELISA in seroscreening assays. However, a limitation of traditional approaches in seroscreening include the reliance on indirect detection of captured analytes using secondary detection reagents which are compatible with any number of end- point readouts. Conventional protein microarrays require costly and time-consuming protein production and purification, require a secondary detection molecule (e.g., fluorescent readouts), and do not provide kinetic binding information, thereby missing vital characterization data for host responses and host pathogen interactions. Advances in biosensor technology have enabled alternative sensing modalities beyond end-point detection that are providing new avenues for enhanced biomarker discovery and detection. These technologies include a plurality of label-free and real-time detection techniques (like surface plasmon resonance (SPR), reflectometric interference spectroscopy (RifS), single-color reflectometry (SCORE), biolayer interferometry (BLI), focal molography, and field effect transistors (FET) based sensing, among others) which can be configured to analyze protein arrays on compatible biosensors. In the systems and methods described herein, in certain aspects, binding events between one or more biomolecules coupled to a biomolecule array and one or more analytes (e.g., autoantibodies) in a sample can be monitored in solution in real-time, where kinetic parameters of the interactions such as rate of association and dissociation can be measured. In various aspects, these measured kinetic parameters can provide a direct assessment of the affinity of the interactions between the analytes in solution and the biomolecules arrayed on the biosensor surface. This is in contrast to current technology, where due to the reliance of end-point fluorescent readout, only a semi-quantitative, presence / absence determination can be made with traditional microarray assays employed in seroscreening. Those microarray assays lack the capability to monitor binding events as they occur in real-time and a limited, only relative ability to resolve the affinity of any given antibody-antigen interaction. One reason the kinetics of the antibody-antigen interaction is valuable to assess is that it can impact the detection in end-point assay. In traditional microarray assays prevalent today, primary antibody (or serum containing Aabs) is diluted and incubated on the slide displaying the protein array. The slide is then washed numerous times and incubated with the fluorophore- conjugated secondary antibody for an extended period prior to final washing and fluorescent scanning for detection. Antibody-antigen interactions with certain kinetics, especially those characterized by fast dissociation, may not be detected by this method however as the antibody may dissociate from its target prior to the time that secondary antibody is incubated or prior to Atty Dkt. No.155529.00024 scanning of the slide. For analysis of antibody-antigen interactions, reliance on end-point fluorescence assays can result in false-negative results due to the inability to detect interactions with fast dissociation kinetics in real-time. Therefore, traditional fluorescent microarrays and other assays reliant on end-point detection (ELISA / MBA) can miss Aab-antigen interactions that may be occurring. A second reason that kinetics of antibody-antigen interactions are of value is that antibody affinity is involved in the pathogenesis of diseases, such as autoimmune diseases and their sub-types. The strength by which an antibody binds its target antigen can have varying physiological consequences which may dictate onset of disease symptoms, enhanced disease activity, or development of secondary complications. This varied affinity and varied physiological outcomes are illustrated in FIG.2. For example, in SLE, presence of anti-double stranded DNA (dsDNA) or anti-Smith antigen Aabs is a necessary diagnostic criterion. There is evidence that high affinity anti-dsDNA Aabs are associated with renal disease and nephritis in lupus, a leading cause of death in SLE patients. This is likely due to the high affinity anti- dsDNA Aabs forming glomerular immune complexes in circulation that leads to high deposit in the kidneys and higher risk for nephritis. Alternatively, in RA, up to 70% of patients develop Aabs against the post-translational modification citrulline; collectively referred to as anti- citrulline protein antibodies (ACPAs) which are also included in RA diagnostic criteria (along with rheumatoid factor or RF; Aabs against the IgG constant regions). Only 50% of RA patients have detectable ACPA at time of diagnosis, though ACPA appear to go through a well- characterized maturation process in the early phases of the disease whereby these Aabs become more promiscuous through a process called epitope spreading and concurrently steadily increase in affinity to a level which plateaus after the onset of symptoms. In RA, ACPA positive patients are at a higher risk for ischemic heart disease, lung pathology, and experience faster and more severe joint erosion. It has been proposed that lower affinity ACPA leads to increased synovial tissue penetration of the Aabs leading to accelerated destruction at the joints. Therefore, Aab affinity against targeted antigens can have important physiological implications that if capable of being resolved by routine diagnostic screening tests, could improve the diagnosis and prognosis of autoimmune diseases and SARDs like SLE and RA. Disclosed is a proteomic platform for producing and screening an array of functional proteins on biosensor surfaces developed to address the challenges of measuring proteomic interaction kinetics in high throughput (HTP). The disclosed Sensor-Integrated Proteome On Chip (SPOC®) platform involves in-situ cell-free protein expression in nano-liter volume wells (nanowells) directly from rapidly customizable arrays of plasmid DNA, facilitating Atty Dkt. No.155529.00024 simultaneous capture-purification of unique full-length folded proteins onto a 1.5 sq-cm surface of a single gold biosensor chip. A few to thousands of proteins can be captured on the biosensor array or chip. Arrayed SPOC sensors can then be screened by real-time label-free analysis, including surface plasmon resonance (SPR) to generate kinetic affinity, avidity data. Fluorescent and SPR assays were used to demonstrate zero crosstalk between protein spots. The recombinality of the SPOC protein array was validated by antibody binding assay, post- translational modification, mutation-mediated differential binding kinetics, and catalytic activity screening on model SPOC protein arrays containing p53, Src, Jun, Fos, HIST1H3A, and SARS-CoV-2 receptor binding domain (RBD) protein variants of interest, among others. Monoclonal antibodies were found to selectively bind their target proteins on the SPOC array. To this end, the methods and systems described herein combine functional biomolecule arrays, also referred to throughout this disclosure as microarrays, with label-free and real-time biosensing of analyte interactions, such as antibody-antigen interactions, among other relevant analyte interactions, to produce patient-specific kinetic profiles, in disclosed aspects. In the same or alternative aspects, the information obtained from the biomolecule arrays and methods described herein together with machine-learning algorithms can be utilized to inform autoimmune disease, e.g., SARD, diagnostic and prognostic decision based on Aab and / or analyte affinities. Biomolecule Array and Biomolecule Array Detectors The disclosed is sensor-integrated proteome on chip (SPOC) that overcomes major challenges in resolving complexity in functional proteomics including (1) functionality in how to produce full length, folded proteins, (2) how to measure kinetic rates (affinities) of protein interactions with other proteins / biomolecules, how to make fully-folded functional proteins at an affordable cost, and scalability to investigate and detect 1000s of proteins and their interactions at once. The disclosed SPOC biosensor array or biomolecule array is an integrated platform where proteins are directly expressed and immediately captured on the biosensor surface, eliminating the entire pipeline of recombinant protein production and purification. Capture molecules on the biosensor surface may include in situ cell-based or cell-free expressed or prior-expressed molecules directly spotted on the surface, such as antibodies, antibody fragments, nanobodies, peptides, scFvs, proteins, signaling molecules. In some embodiments, kinetic readouts occur through direct biomolecule sensing on the chip surface through technology called surface plasmon resonance (SPR), providing real-time sensing of interactions between the molecules on the biosensor surface and the analytes in the sample. Atty Dkt. No.155529.00024 The simultaneous expression-capture method enables rapid alteration of the gene set on the chip surface, providing a customizable platform. SPOC biosensor arrays have applications described herein, ranging from immune response characterization (inflammatory / inhibitory markers, antibody reactivity and isotyping, etc.), immunogen discovery for vaccine development, pan-pathogen diagnostics / prognostics, drug and therapeutic discovery and characterization (including off-target binding detection and on-target validation), host-pathogen interactions (protein-protein, protein-small molecule, protein-antibody, protein-cell surface receptor, etc.), phenotype characterization (including mutation monitoring via altered kinetic binding), membrane protein expression via nanodiscs, companion diagnostic for clinical trials for pharmaceuticals and vaccines, target discovery for therapeutics / vaccines / drugs, among others. Though the primary purpose of this application is for biopharmaceutical applications, all applications herein described may be used in research use only, academic settings, or other non-clinical purposes, particularly in the context prior to or in the absence of regulatory approvals (such as CLIA, FDA, or other similar agency). As discussed above, in various aspects, the systems and methods can include a biomolecule array having a plurality of biomolecules coupled to a biosensor surface. In certain aspects, the biomolecules can be captured in an array format on any type of biosensor slide or component that is compatible with one or more biosensing techniques capable of label-free and / or real-time biosensing. A non-limiting list of biosensing techniques capable of label-free and / or real-time biosensing via any number of approaches includes those that leverage biophysical, electrical, diffractometric and reflectometric methods such as SPR, RifS, SCORE, BLI, focal molography, FET-based sensing, Raman-based sensing, plasmonic-sensing, or electrochemical-based sensing, without limitation. Protein microarrays are in part described in United States Patent No. US 10,983,118, which is incorporated by reference in its entirety. In various aspects, the disclosed biomolecule array can include any type of substrate for supporting and / or providing a biosensor surface to which the biomolecules are coupled thereto. In certain aspects, the substrate can include clear material. In one or more aspects, the clear material can include fused silica. In certain aspects, the fused silica can include BK-7 fused silica, SF10 fused silica, or a combination thereof. In various aspects, the biosensor surface of the biomolecule array may be a material different than the substrate. In various aspects, the biosensor surface can include any type of material compatible with label-free and / or real-time biosensing. In certain aspects, a non- limiting list of example biosensor surface materials can include gold, silver, copper, aluminum, palladium, silicon, silicon dioxide, aluminum dioxide, and hafnium oxide. In one example Atty Dkt. No.155529.00024 aspect, the biosensor surface can include a thin layer of gold, e.g., a layer less than 200 nanometers (nm), less than 100 nm, or of from 1 nm-100 nm, 20 nm – 80 nm, or 45 nm – 55 nm. In various aspects, the biosensor surface may consist of a planar unstructured surface, or contain of a variety of microstructures and nanostructures, of which may consist of nanoparticles, nanorods, nanodisks, nanopores, and nanowires, among others. In one or more aspects, the biosensor surface may be coupled to the substrate using an adhesive layer. In certain aspects, the adhesive layer can include any material capable of adhering the biosensor surface to the substrate and that is compatible with label-free and / or real-time biosensing. In various aspects, the adhesive layer can include one or more of titanium, chromium, or tungsten. In certain aspects, the biosensor surface can be coated or passivated (e.g., a scaffold coating or passivation coating). For example, in one or more aspects, the biosensor surface can be coated with reagents, such as self-assembled monolayer (SAM) reagents, to passivate reactive gold surfaces of the biosensor surface and provide a scaffold for coupling antigens or other biomolecules of interest directly, or for coupling relevant ligands that are compatible with capture of the desired antigens or other biomolecules. For biosensor surfaces with gold coatings, as commonly used in SPR, SAM reagents may contain a thiol or silane moiety to absorb strongly onto the gold or gold-oxide surface and orient the SAM in a uniform manner, in certain aspects. In various aspects, the SAM may also include one or more fouling resistant surface chemistries (e.g., an anti-fouling coating) to reduce the non-specific binding of serum components to the biosensor surface, including but not limited to, N-hydroxysuccinimide ester of 16-mercaptohexadecanoic acid (NHS-MHA), polyethylene glycol (PEG), polyethylene oxide (PEO), and other zwitterionic- or peptide-based formulations, among others. In other aspects the passivation layer also serves to couple with reactive groups present on biomolecules or ligands of biomolecules as means to capture biomolecules in array format and may consist of any but not limited to a plurality of 2-D or 3-D chemistries and hydrogels consisting of, for example, dextran, carboxymethlydextran, tetraethylene glycol, polycarboxylate, aliginate, and gelatin, among others. FIG.3 is a schematic depiction of one example biomolecule sensor, e.g., for SPR-based detection. As can be seen in the aspect depicted in FIG.3, a substrate 404 is in contact with an adhesive layer 412, which is in contact with a biosensor surface 408. FIG. 3 also provides example proportions of the biomolecule array (1.2 cm wide and 2.8 cm long), and example thickness ranges of 45nm to 55 nm for the substrate 404, 2 nm to 15 nm for the adhesive layer 412, and 200 µm to 500 µm for the biosensor surface 408. Exemplary material for the substrate Atty Dkt. No.155529.00024 404 may be fused silica. Exemplary material for adhesive layer 412 include titanium, chromium, and tungsten. Exemplary material for the biosensor surface 408 include gold, silver, aluminum, and silicon. It should be understood that the biomolecule sensor depicted in FIG.3 is just one example biomolecule sensor and that other types of sensors are contemplated by the present disclosure. FIG.4 shows exemplary types of biosensors and sensing methods. In various aspects, the overall dimensions of the biomolecule array are sized to encompass the flow cell of the detector to be utilized, e.g., an SPR instrument or other detector. As discussed above, in various aspects, the biosensor arrays disclosed herein can include a plurality of biomolecules, including a plurality of mutationally scanned protein ligands, coupled to a biosensor surface, with each different biomolecule or ligand positioned at a discrete location on the biosensor surface. As used herein, a “discrete location” refers to a specific point or spot on the biomolecule array that is dedicated to the placement of a single biomolecule type (single mutationally scanned protein ligand) or mixture of biomolecules. For example, the biomolecule array may include a discrete location (e.g., a spot), where there is only one biomolecule (e.g., antigen) bound to that discrete location. FIG. 5 provides images of one example SPR biomolecule sensor showing an array and / or spot pattern on a biosensor surface. As can be seen in the example aspect depicted in FIG.5A, a biosensor surface 510 includes a pattern 504 of spots 506. As described above, in various aspects one or more of these discrete spots or locations can include a biomolecule, e.g., an antigen or analyte detection molecule. Depicted in the dashed-line box is about 500 spots present in a 1.0 cm2area. In the aspect depicted in FIG. 5B, the spots 506 may be visible on the slide itself and / or visible within the flow-cell of the instrument along with a hockey stick- shaped mark (or any other kind of fiducial) 508 utilized for orientation. FIG. 5C also shows one example instrument 500 for the depicted biomolecule array, which includes a Horiba OpenPleX SPRi detector. In such an aspect, the SPR biomolecule array may be configured as an OpenPleX-compatible SPRi biomolecule array. It should be understood that the biomolecule array depicted in FIGS.5A-5C are just one example biomolecule array and that other types of arrays, spot patterns, and fiducials are contemplated by the present disclosure. In certain aspects, the biomolecule array can include a plurality of biomolecules with any number of and / or types of biomolecules coupled to the biosensor surface. In one or more aspects, the biomolecule array can include 50 to 50,000 biomolecules or types thereof, 50 to 10,000 biomolecules or types thereof, or 100 to 5000 biomolecules or types thereof coupled to the biosensor surface. In various aspects, the biomolecule array can include at least: 2, 5, 10, 20, 40, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360, 380, 400, 420, Atty Dkt. No.155529.00024 440, 460, 480, 500, 520, 540, 560, 580, 600, 620, 640, 660, 680, 700, 720, 740, 760, 780, 800, 820, 840, 860, 880, 900, 920, 940, 960, 980, 1000, 1500, 2000, 3000, 4000. 5000, 10,000, 30,000, 40,000, or 50,000 arrayed biomolecules or types thereof (e.g., mutationally scanned protein ligands). In various aspects, the range of spots (of biomolecules) per unit of area can range considerably. For example, in certain aspects, the spotting can range from five spots per square centimeter to 50,000 spots per square centimeter. In one example aspect, the spotting can be approximately 500 spots of proteins per square centimeter. In various aspects, the biomolecule array can include any type of biomolecule coupled to the biosensor surface. In aspects, the biomolecules can include any molecule produced, used, or found in a biological system, including both naturally occurring biomolecules and synthetically-produced biomolecules. In various aspects, the biomolecules may derive from any animal or model organism including any mammal, such as a human, or non-mammal or single-cell organism or virus. In the various disclosed aspects, the biomolecule may comprise one or more of nucleic acid, peptide, polypeptide, protein, protein domain, phospholipid, immunoglobulin-like protein, small molecule, micelle, liposome, nanoparticle, polymersome, mesoporous particle, dendrimer, and aptamer, or a combination thereof. In one or more aspects, the biomolecules can include one or more biomolecules associated with one or more clinical feature or outcome, such as one or more autoimmune or related disease, or other disease. For instance, in certain aspects, biomolecules can include experimental or long-standing disease-relevant antigens that can be pathologically targeted by the immune system of patients with autoimmune diseases and / or from various SARDs. In certain aspects, the biomolecules can include one or more antigens, e.g., for detecting one or more antibodies in a sample, and / or one or more analyte detection molecules for detecting one or more analytes in a sample. Analytes may include cytokines, chemokines, proteins, peptides, protein complexes, serum factors, small molecule chemicals, nucleic acids including aptamers, and whole cells, among others. The terms “disease” or “disorder” or “condition” may be used interchangeably throughout this disclosure, and refer to a state of health of a mammal, for example a human, including any deviation from the normal state health of a mammal. The terms “patient” or “subject” may be used interchangeably in this disclosure and refer to all members of the animal kingdom prone to or suffering from a disease, disorder, or condition. The subject may be a mammal, e.g., a human or non-human. In certain aspects, biomolecules can include nucleic acids, e.g., DNA, RNA, ssDNA, dsDNA, and / or ssRNA, lipids, phospholipids, cyclic citrullinated peptides (CCP), peptides, Atty Dkt. No.155529.00024 protein domains, full length proteins, or a combination thereof. In various aspects, one or more of the plurality of biomolecules can include one or more modifications and / or post-translational modifications. In such aspects, the modifications and / or post-translational modifications can include, but are not limited to glycosylation, phosphorylation, methylation, citrullination, sumolyation, disulfide bond formation, acetylation, lipidation, methylation, ubiquitination, S- nitrosylation, hydroxylation, amide formation, sulfation, myristoylation, deamidation, palmitoylation, carboxylation, formylation, O-linked glycosylation, S-palmitoylation, N- acetylation, N-myristoylation, cysteine modifications, or other modifications or one or more detection and / or affinity tags or fusion proteins or peptides. In one aspect, a non-limiting list of antigens associated with one or more SARDs includes Smith, DNA, Ro / SSA, La / SSB, cN1A, Jo-1, PL-7, M-2, Ku, Scl-70, CENPB, Th / To, U1RNP, TRIM21, and ACPA. In various aspects, the analyte detection molecules (either printed onto the biosensor or captured in situ) can be any type of molecule capable of binding to one or more analytes in a sample. In certain aspects the analytes can include but are not limited to one or more cytokines, chemokines, autoantibodies, proteins, peptides, protein complexes, serum factors, small molecule chemicals, nucleic acids including aptamers, and whole cells or other relevant biomolecule, or a combination thereof. In certain aspects, the analyte detection molecules can include antibodies, single chain variable regions (scFv), nanobodies, antibody mimetics, or relevant analyte binding partners or a combination thereof. In one or more aspects, the analyte detection molecules are configured to bind to one or more analytes associated with the immune system, including but not limited to cytokines. In certain aspects, one or more of the plurality of biomolecules may be prepared from one or more biological sources, e.g., recombinant expression in various bacterial, yeast, and / or mammalian cell lines. In the same or alternative aspects, one or more of the biomolecules may be prepared from non-recombinant native preparations, e.g., chromatin, intact snRNP, and nucleic acid extracts (e.g., from Calf Thymus and / or Salmon Sperm extracts). In certain aspects, one or more of the plurality of biomolecules can be present in a cellular lysate, or lysate fraction, and coupled to the biosensor surface. In various aspects, the biomolecules can be coupled to the biosensor surface using any techniques compatible for use in the systems and methods disclosed herein. In certain aspects, one or more of the plurality of biomolecules can be prepared from one or more biological sources and either spotted and captured in array format from a crude lysate or from fractions containing a purified biomolecule. In various aspects, the biomolecules may be spotted and captured in array format using one or more of a plurality of contact and non-contact based Atty Dkt. No.155529.00024 microarray spotting approaches suitable for use in the systems and methods disclosed herein. In certain aspects, the microarray spotting approaches can be automated. In various aspects, the density of the biomolecules can be varied such as to overcome avidity effects which may mask important kinetic information of a given analyte-ligand interactions and / or lead to interactions which are too strong such that negligible dissociation is observed. Avidity effects occur when multivalent analytes are present in samples which can bind multiple ligands via any number of functional binding sites (not just a 1:1 interaction). In various aspects where avidity effects for multivalent analytes (e.g., immunoglobulins) are desired to be reduced and / or avoided, the density of the captured biomolecule ligands can be reduced such that the multivalent analyte is restricted to 1:1 interactions. This can be accomplished via a number of strategies including but not limited to varying the density of the capture surface chemistry (e.g., dextran, PEG, etc) to restrict the density of the capture biomolecule that can be achieved. Additionally, if the capture biomolecule is mediated by a capture ligand (or capture tag) which is coupled to the biosensor surface (activated biosensor surface), as opposed to the biomolecule captured directly to the surface chemistry itself, the concentration of the capture ligand can be adjusted such that there are fewer active areas on the sensor present for capture of biomolecules of interest thus leading to less densely captured biomolecules. In certain aspects, functional protein or other biomolecules could be prepared via cell- free protein expression and coupled to the biomolecule array via microarray spotting techniques, or via in vitro transcription, in vitro translation and in situ capture-purification. Biomolecule functional protein, such as full-length folded protein, may be produced from nanowells. DNA (plasmid) encoding unique proteins of interest are printed or dispensed in nanowells. Full length proteins are expressed in situ from the DNA in nanowells in cell-free lysate. Expression in cell-free lysate, such as human cell lysate, ensures proper protein folding and protein functionality. Expressed proteins are capture-purified as an array of ‘pure protein monolayer spots’ on the biosensor surface. Functional protein production is customizable because only a plasmid with DNA sequence of interest is required to make custom protein. An example of the in vitro transcription, in vitro translation and in situ capture-purification approach is depicted in FIG. 6A. By leveraging human cell-free in vitro transcription and translation (IVTT) lysate, a custom SPOC protein array production instrument are produced via in situ expression of full-length folded proteins, which are encoded by recombinant plasmid DNAs printed in nanowell slides. Expressed proteins are simultaneously capture-purified as a monolayer of arrayed spots onto biosensor chips for label-free analysis by SPR. The entire Atty Dkt. No.155529.00024 process from protein array production on biosensor chip to label-free kinetic analysis is called SPOC and is facilitated by the custom SPOC protein array production instrument. The SPOC protein array can produce multiple SPOC biosensors from a single nanowell slide. The SPOC protein array shown in FIG. 6A has four channels in each of which can produce a nanowell slide in a semi-automated fashion; ultimately yielding up to sixteen SPOC chips in a single run. In the example aspect depicted in FIG. 6A, this approach utilizes a nanowell slide 604 (e.g., a second substrate) where a unique gene or plasmid (nucleic acids 612a-c) can be printed into one or more or a plurality of nanowells 608a-c (e.g., a nano-liter volume well) of the nanowell slide. The nucleic acids 612a-c may contain the genes for unique antigens of interest, or analyte detection molecules of interest, or other biomolecules of interest fused to a common domain that serves as a capture tag, in various aspects. In one or more aspects, the printed nucleic acid may also include other nucleic acids that may be transcribed and / or translated including but not limited to RNA, or plasmid and non-plasmid DNA, (e.g., genomic DNA, mitochondrial DNA, viral DNA, synthetic DNA, or cDNA reverse transcribed from RNA). Subsequently, in certain aspects, the nanowells are filled with a cell-free lysate 705 and the nanowell slide 604 is sealed by the biomolecule sensor 200 or biosensor surface 208 applied on top of the nanowells. In various aspects, the biosensor surface 208 may be coated with a compatible capture tag ligand. In aspects, sealing the nanowell slide 604 can prevent diffusion of expressed protein between nanowells 608a-c. In one or more aspects, the sealed nanowell slide 604 is then incubated to allow the cell-free lysate to transcribe and translate proteins 717 (e.g., fusion proteins) encoded by the printed nucleic acids 612a-b. In one example aspect, the nanowell slide 604 may be incubated at 30°C for at least two hours. Full length folded proteins are produced in each well, and then captured on the biomolecule sensor 200 or biosensor surface 208. The full-length folded proteins are captured in an array format, overcoming the need for individual protein purification in cellular systems or automated robotic spotting. After incubation, the nanowell slide 604 can be removed from the biomolecule array 200 or biosensor surface 208. In various aspects, the end result is a monolayer of protein captured in discrete spots forming a microarray on the biosensor surface, e.g., the biosensor surface 208. The SPOC platform, yielding a functional protein array (or SPOC array) for label-free analysis by SPR (see, FIG.6A), is scalable, versatile, and highly customizable. As an example, first, a customized plasmid DNA array is created by printing the plasmids onto a nanowell slide, for example a 25 × 75 mm silicon nanowell slide, containing thousands of nanowells (e.g., 2.0 nL volume wells), with center-to-center spacing of, for example 225 to 375 μm for Atty Dkt. No.155529.00024 30,000 wells / slide (30k) (FIG. 6B, top) and 10,000 wells / slide (10k)(Fig. 6B, bottom). The assay may be configured such that proteins of interest are expressed from the printed plasmids as tagged fusion proteins, e.g., proteins fused with Halo protein as a common tag, for later covalent capture on the array. Next, the DNA-printed silicon nanowell slide is held in proximity to the surface of the biosensor capture slide whose surface has been pre-functionalized with HaloTag chloro-alkane linker to covalently capture the HaloTag protein, on the expressed fusion protein. A human HeLa-cell based, in vitro transcription and translation (IVTT) lysate mix is then injected between the nanowell slide and biosensor slides which are subsequently press-sealed against each other effectively isolating the nanowells to create nanoliter-volume reaction chambers for isolated protein expression (IPC). The nanowell slide and biosensor slides sealed together, also referred to throughout this disclosure as a sandwich, is then incubated at 30°C for 2–4 hours during which the IVTT lysate expresses the protein encoded in the printed plasmid DNA contained in each nanowell, which are simultaneously capture- purified onto the biosensor capture slide forming the SPOC protein arrays. The entire process is facilitated by a custom AutoCap instrument developed and manufactured in house. One example of a custom AutoCap instrument contains four independent expression channels that enable four 25 mm × 75 mm nanowell slides to be expressed simultaneously, in a semi- automated fashion. From each nanowell slide, four SPR biosensors containing SPOC arrays can be produced, yielding 16 SPOC chips from four channels within a single expression run. Importantly, the number of proteins that can be arrayed on any capture slide is dependent on the size of the capture slide and density of the DNA-printed wells in the silicon nanowell slides. For example, the depicted 10k silicon nanowell slide design (FIG.6B, bottom) expresses proteins in staggered pattern at a density of 716 protein spots / sq-cm, which is equivalent to about 1000 spots on the analysis area of the Carterra LSAXTSPR instrument. However, the depicted 30k nanowell slide design (FIG.6B, top) produces up to 1,975 spots / sq- cm, or 2400 protein spots on the Carterra LSAXTSPR analysis area. FIG. 6C shows an actual protein array on a gold biosensor produced by SPOC, highlighting distinct spots where individual proteins were immobilized. This innovative AutoCap instrument effectively reduces the cost 10–100 times compared to traditional workflows that involve recombinant protein production and purification, making HTP proteomic screening more accessible. FIG. 6D illustrates an example of a linker coated on the biosensor surface, which is capable of capturing or connecting a capture tag (or fusion tag) to the biosensor surface. A linker may be a chloro-alkane linker, which binds covalently to a capture tag (or fusion tag). The linker, such as a chloro-alkane linker, may be referred to as a capture ligand. A linker, or Atty Dkt. No.155529.00024 capture ligand, may be a Halo ligand, for example that binds or captures a halo protein tag. As discussed, a biomolecule (capture biomolecule) may be connected to, or mediated by, a capture tag that is coupled to the biosensor surface with the linker (capture ligand), as opposed to capture of a biomolecule directly to the surface chemistry itself. A capture tag, or fusion tag, may be a halo-protein tag. Various aspects of the present disclosure relate to the production and expression of protein, including immunoglobulin (Ig)-like proteins, arrayed on biosensors, for example with an in vitro mix for in vitro transcription and translation (IVTT). In some aspects, the proteins or Ig-like proteins may be captured on the surface of a biosensor using a variety of methods. Representative examples of Ig-like proteins164are depicted in FIGS. 7A-7F. Ig-like proteins consist of variable and constant regions made up of heavy and light chains. These regions, or domains, can be rearranged in multiple formats to create unique Ig-like protein fragments. Human, camel (VHH), and shark (IgNAR) variations are depicted. As an analyte is flowed in solution over the surface of a biosensor with proteins or Ig- like proteins captured on the surface, the analyte may interact with the proteins or Ig-like proteins constituting a binding event, which can be measured by a number of different technologies. Biosensor technologies for detecting biomolecular interactions include a plurality of label-free and real-time detection techniques, such as SPR, reflectometric interference spectroscopy (RifS), single-color reflectometry (SCORE), biolayer interferometry (BLI), focal molography, and field effect transistors (FET) based sensing, among others, without limitation. In the system and methods described herein, in certain aspects, binding events or interactions between Ig-like proteins may include kinetic, or binding affinity, measurements . Kinetic parameters may be measured, such as association constant, dissociation constant, affinity, avidity, time of half-life. Binding events also constitute qualitative (presence / absence or yes / no) and quantitative data (any numerical, quantifiable output from the instrumentation or assay), which further define the binding interaction. In aspects, Ig-like proteins may include, as examples, single-domain antibody fragments (dAbs, or nanobodies), antigen-binding fragments (Fabs), single-chain variable fragments (scFvs), camelid VH domains (VHH), shark VH domains (V-NAR, new antigen receptor), heavy-chain antibodies that lack light chains (HCAbs), cyclic scFvs, scFv region fused to a constant domain (35eco), full-length antibodies (IgG, IgA, IgE, IgD, IgM and their subtypes or isotypes), bi-specific T-cell engager (BiTE), di-scFv, multispecifics (dual-variable- domain Ig, tetravalent IgG-like antibodies, multi-specific scFvs, IgG-scFv), diabody, minibody Atty Dkt. No.155529.00024 (scFv-CH3), scFab, scFv-zipper, scFv-Fc, triabody, tetrabody, IgNAR bispecifics, trispecifics, camel Ig, monospecific, monovalent IgG, hcIgG, dual affinity retargeting antibodies (DARTs), and bispecific killer cell engagers (BiKEs), Chimeric antigen receptors (CARs), or any other combination of heavy chain, light chain, variable regions, and / or constant regions not listed, without limitation. All Ig-like proteins are defined by two parameters: their binding epitope (interacting region on the target protein) and their affinity (strength of binding to the target protein). During antibody design, through artificial intelligence or other means, the target protein is defined and candidate antibodies are screened for affinity to the target and then epitope mapped to define the binding region. As such, thousands of antibody candidates may exist in the pipeline, requiring high-throughput assays for down-selection of the lead candidates. Affinity ranking based on the kinetic binding interaction is one aspect of lead candidate selection. In other aspects, the disclosed system and methods herein provide non-kinetic parameters of and screening tools for the candidate antibodies. Such parameters may include, without limitation: stability at various temperatures, pH, buffers, salts, or other stabilizing media; expression or yield parameters based on screening different expression conditions to include, without limitation chaperones, disulfide bond catalysts, agitation, temperature, buffer, reducing or non- reducing conditions, enzymes; sequence parameters to include, without limitation signal peptides, stabilizing peptide sequences (positive versus negative charges, as an example), linker length and sequence, protease cleavage sites (or lack thereof), fusion tags (such as SUMO, as an example), chemical modification sites (or lack thereof). The platform described herein supplies information on antibody candidate characteristics listed above, provides a high- throughput sequence screening tool and method, and is applicable to all stages of the drug discovery process from sequence design / discovery through stability and bioactivity evaluation for end-stage scale-up and manufacturing. As described above, proteins, such as Ig-like proteins, can be expressed with an in vitro mix for in vitro transcription and translation (IVTT) and in situ protein production. Expression can be achieved in an IVTT mix through transcription and translation of RNA, mRNA, DNA, or other nucleic acids in plasmid (circular or linearized), genomic, fragments, or linear formats, as examples without limitation. Many IVTT mix varieties exist commercially or developed in research labs, including cell lysates derived from human (HeLa), mammalian (CHO, Chinese hamster ovary), bacteria (Escherichia coli, Agrobacterium tumefaciens), yeast (Saccharomyces cerevisiae), insect cells (Sf9), plants (wheat germ, Nicotiana benthamiana), as examples without limitation. This disclosure covers herein expression of Ig-like proteins with any and all Atty Dkt. No.155529.00024 IVTT mixes, including modified versions, derived from any source (any species, cell line, or other biological-based organism) without limitation. To facilitate disulfide bond formation, in some aspects, redox altered IVTT mix can be used, such as E. coli Shuffle82and Origami (Novagen) strains, including modified versions and derivatives thereof, though applicable to IVTT mixes from all species. FIG. 8 demonstrates how additives can be included within the lysate to alter the redox state, including changing the GSSG / GSH ratio90, IVTT mix derived from cell lines with mutated versions of disulfide bond breaking proteins (such as trxB or gro reductase), or pre-treatment of the IVTT mix with a reductase inhibitor (such as IAM)57. Proteins may be expressed using IVTT methods in tubes, plates, nanowells, or array formats, as examples without limitation. FIG.8 is an example of altering redox state conditions within an IVTT mix57. Oxidizing reagents are added to the standard IVTT mix, incubated with the Ig- like protein DNA, and arrayed in tubes, plates, nanowells, or other format for screening. FIG.6A demonstrates a method for expression and direct capture of IVTT proteins onto a biosensor surface, here onto a gold-coated SPR biosensor chip (or SPOC). This overall platform can be customized for expression and capture of any full-length protein, peptide, domain, or multi-protein complexes, including antibodies and Ig-like proteins and other small molecule-, nucleic acid- or carbohydrate-based ligand as outlined in this disclosure. The IVTT capture method outlined in FIG. 6A can be combined with printing of both nucleic acids for IVTT expression and direct printing of protein or protein-conjugated with a biomolecule into the nanowells for capture on biosensor. Capture of said printed protein or protein conjugated biomolecule onto respective capture substrates directly, without the need for, or in combination with, IVTT protein expression and capture. FIGS. 9A-9D illustrate combined printing of nucleic acids and proteins into the same nanowell slide. Data from a label-free assay and fluorescent end-point assay is also shown and demonstrates capturing both printed protein and IVTT expressed protein on to the same capture substrate in a common approach from the same nanowell slide. In this case, the exemplary capture surfaces include an SPR biosensor for label-free assay and a glass capture surface for fluorescent end-point assay. In both cases, anti-HaloTag antibody detects the GST-HaloTag protein (or any protein conjugated biomolecule) originally printed in the nanowell slide captured on the SPR biosensor and glass capture surfaces, in addition to other HaloTag protein expressed by IVTT, like the HaloTag only control. The SPR data further confirms the direct protein capture by demonstrating specific detection of conjugated biomolecule, in this case the GST-HaloTag protein during injection of an anti-GST antibody which does not pick up the neighboring IVTT expressed HaloTag protein also captured on the surface, as expected. This Atty Dkt. No.155529.00024 demonstrates how from a single nanowell slide, both recombinant proteins and those expressed from nucleic acids can be captured onto capture surfaces which can be advantageous for applications where ligands on the capture surface need to also include biomolecules or other chemicals not able to be transcribed and translated by from nucleic acids. Various embodiments of in vitro systems include but is not limited to cell based system or cell free system or cell free lysate or cell free extract or reconstituted expression system or tissue lysate. FIGS. 10A-10C illustrate the plurality of capture surfaces able to be employed for ligand capture and screening by various end-point and real-time assays as outlined in this disclosure. Two exemplary capture surface modalities are demonstrated for screening by three readouts via fluorescence, surface plasmon resonance, and matrix-assisted laser desorption / ionization (MALDI). In all cases the planar capture surfaces are coated with appropriate chemistries for the capture of ligands containing compatible moieties as would be familiar to persons of ordinary skill in the art and outlined in more detail in this disclosure. Glass capture surfaces can be employed for fluorescent end-point assay of interactions to bound ligands. Whereas gold-coated biosensors may be employed in label-free assay that are either compatible with real-time sensing as in SPR, or end-point detection as in MALDI imaging. For each exemplary planar capture surface, representative data from interactions to ligands on the surface detected via each modality is shown below the respective capture surfaces. In various aspects, where biomolecules are not coupled directly to the surface of the slide, one or more capture or fusion tags can be employed which can be fused to biomolecules and proteins of interest (e.g., antigens or analyte capture molecules) and utilized to capture the proteins or other biomolecules in the desired array format on the biosensor surface. Capture ligands (also referred to as fusion ligands) can consist of a plurality of any number of ligands compatible with various tags and may include but are not limited to Biotin (Streptavidin ligand), Glutathione (GST ligand), amylose (MBP ligand), Chloroalkane (HaloTag ligand), E- coil (K-coil ligand), K-coil (E-coil ligand). 06-benzylguanine (SNAP-tag ligand), 06- benzylcytosine (CLIP-tag ligand) SpyTag peptide (SpyCatcher ligand), SnoopTag peptide (SnoopCarther ligand), DogTag peptide (DogCather ligand) among others. Self-labeling tags, such as HaloTag, SNAP-tag, and CLIP-tag can be utilized to produce monolayers of covalently captured protein or biomolecule spots on biosensor surfaces coupled with respective ligands, in addition to other covalent tags such as SpyTag, SnoopTag, and DogTag can be employed, and any other relevant high affinity interactions commonly used to couple ligand to biosensor surfaces which would be known to those familiar with the field (e.g., a plurality of antibody- based capture via a plurality of epitope tags, E / K-coil interaction, the Streptavidin-biotin Atty Dkt. No.155529.00024 interaction, and GST / Anti-GST or Glutathione), in certain aspects. Covalent capture modalities offer distinct advantage over other approaches, by enabling regeneration of the sensor surface during experimentation without loss of ligand from the biosensor surface when using acidic, basic, or high ionic strength regeneration buffers facilitating subsequent sample injections, in aspects. In various aspects, regeneration of the sensor surface can be employed to facilitate repeated and serial probing of the biomolecules on the array with a plurality of analytes of interest from a plurality of biological sources of interest. Ideal regeneration injections would not lead to significant loss of biomolecule ligand capture level or integrity yet would effectively strip away bound analytes from previous injection facilitating another round of analyte binding without interference from previously bound analytes. FIG. 6F shows reproducibility of SPOC sensor binding signal after regeneration. The sensor was regenerated with 10mM Glycine-HCl (pH = 2.4) between injection and protein expression. In various aspects, where cell-free protein expression and in situ capture-purification of proteins is employed to form the desired biomolecule arrays, capture of non-IVTT (in vitro transcription translation) compatible or challenging to express components (e.g. nucleic-acid, phospholipids, CCP, and / or RF etc.) can be employed by one or more of a plurality of methods including but not limited to: (i) direct printing of the biomolecule into the nanowell slides for diffusion and capture onto the biosensor surface via appropriate tag and capture-ligand pairs during the cell-free protein expression incubation; or (ii) expression of tandem fusion tag protein domains whereby one tag (e.g., HaloTag) is utilized to couple the tandem fusion protein to a ligand-coated biosensor surface while the remaining tag (e.g., SNAP-tag) is utilized to capture a compatible ligand-coupled antigen or biomolecule via incubation after the biomolecule array has been formed but prior to formal experimentation. In certain aspects, where antibodies (e.g., Aabs) against post-translational modification are relevant such as in RA (citrullination), the appropriate modifications can be introduced post-production after the arrays have been formed on the biosensor surface via appropriate treatment with relevant enzymes (e.g., PAD2) for creating modifications including but not limited to phosphorylation, glycosylation, citrullination, sumolyation, disulfide bond formation, acetylation, lipidation, methylation, ubiquitination, S-nitrosylation, hydroxylation, amide formation, sulfation, myristoylation, deamidation, palmitoylation, carboxylation, formylation, O-linked glycosylation, S-palmitoylation, N-acetylation, N-myristoylation, cysteine modifications, or other modifications. Atty Dkt. No.155529.00024 In one or more aspects, whereas the above biomolecule arrays described involve capture of protein and formation of the arrays offline, biomolecule arrays as described prior can equally be prepared online of a biosensor instrument using one or more of a plurality of existing instrumentations such as the Carterra LSA. As discussed above, in various aspects, the systems and methods can include a biomolecule array having a plurality of biomolecules coupled to a biosensor surface. In certain aspects, the disclosed biomolecule arrays are compatible with one or more biosensing techniques capable of label-free and / or real-time biosensing of one or more binding events between one or more of the biomolecules on the biomolecule array and one or more analytes. In various aspects, the biosensing techniques can utilize any type of detection capable of label- free and / or real-time biosensing described herein. In certain aspects, the detectors can include detectors utilized in one or more of SPR, RifS, SCORE, BLI, focal molography, FET-based sensing, Raman-based sensing, plasmonic-sensing, or electrochemical-based sensing among others. Methods of Detecting In various aspects, as disclosed herein, the biomolecule arrays can be utilized for detecting one or more analyte associated with one or more clinical feature or outcome (e.g., autoimmune or related disease or other disease). In the same or alternative aspects, the methods and systems for detecting one or more analyte associated with one or more clinical feature or outcome can identify one or more kinetic binding parameters of the analytes to one or more of the plurality of biomolecule. In certain aspects, the kinetic binding parameters and optionally additional information may be used in diagnosis, stratification, organ-specific sub-typing and / or prognosis of clinical features or outcomes, such as autoimmune or related diseases, including, but not limited to, SARDs, or other disease. In various aspects, the disclosed methods can include exposing a sample to a biosensor surface of a biomolecule array, such as one or more of the biomolecule arrays described in detail above. In certain aspects, the sample can include any suitable sample for use with the disclosed biomolecule arrays. In one or more aspects, the sample can be a biological sample from a subject. In various aspects, the biological sample can include, without limitation, one or more of blood, lymph, plasma, serum, leukopaks, urine, saliva, sputum, breath extract (meaning exhaled air captured in a solution), bone marrow, aspirates (nasal, lung, bronchial, tracheal), eye fluid, amniotic fluid, feces, other bodily fluids and secretions, cells, and tissue specimens and dilutions or derivatives of them. In one aspect, serum from a subject is utilized. In various aspects, the samples listed above can be taken from subjects from any animal or Atty Dkt. No.155529.00024 model organism including any mammal, such as a human, or non-mammal or single-cell organism or virus. In one or more aspects, serum can be directly utilized as the sample. In the same or alternative aspects, optionally, the serum can be diluted in assay running buffer and injected into the biosensing instrument, e.g., for exposure to the biomolecule array. In various aspects, serum from the subject may be centrifuged to remove larger contaminants (lipids, cell aggregates, etc.) and then diluted to appropriate degrees (e.g., anywhere between 1:5 to 1:1000 in running buffer). In aspects where serum is directly used as the sample, a plurality of strategies to reduce non-specific binding onto the sensor surface could include various strategies on the chip surface (e.g., use of low-charge surfaces and fouling resistant SAM as described above) and / or within the experimental running buffer, including but not limited to the use of non-ionic detergents like Tween-20 (e.g., at 0.005 – 0.05%), the inclusion of dextran in running buffer (e.g., at 0.1- 10 mg / mL) and / or commercial reagents such as non-specific binding reducer (e.g., Cytiva; BR- 1006-91) and the like. In aspects where serum is directly used as the sample, bulk effects as a result of the refractive index mismatch between the running buffer utilized and the complex serum analyte can be mitigated by matching the running buffer to the refractive index of the samples via high concentrations of BSA (e.g., 0.1 – 20 mg / mL) or the use of other inert proteins or diluents. In aspects where bulk effects can influence the binding signal of interest, in addition to subtracting the kinetic traces observed from a specified series of control spots where no signal is anticipated, a double-referencing regimen can be employed whereby a “blank” reference injection is performed prior to sample injection and the signal from this control injection is also referenced or subtracted from the data in addition to the control spot referencing. While routinely performed today and known to those familiar with the state of the art, blank reference injections would in this case match the serum refractive index as closely as possible and could be prepared via a plethora of strategies such as the use of analyte-stripped serum (flow through of serum passed through any plethora of analyte purification columns) or commercially sourced immunoglobulin free-serum, among other preparations. In cases where bulk effects are a concern, label-free and real-time techniques such as focal molography which are not as affected by bulk effects can be leveraged for analyte kinetic screening directly from serum, in various aspects. In certain aspects, the sample can include one or more fractions isolated and purified, e.g., from serum, prior to injection over the biomolecule array via one or more of a plurality of Atty Dkt. No.155529.00024 antibody affinity chromatography approaches including but not limited to Protein A resin purification, Protein G resin purification, and / or Melon Gel Purification, and the like. While advantageous in many respects, allowing for the collection of immunoglobulins and / or Aabs into elution buffers which more closely match the running buffer of choice utilized during biosensor screening, in this case specific sub-types of immunoglobulins may be interrogated for kinetic analysis (e.g., IgG) and the presence of other analytes of interest would not be capable of simultaneous assessment (e.g., by the binding of serum analytes like cytokines to scFvs also spotted or captured on the sensor surface), in aspects. In various aspects, exposing the sample to the biomolecule array and / or the biosensor surface can include any techniques compatible for the systems described herein. In certain aspects, the sample is exposed to the biomolecule array under conditions that promote binding of one or more components of the sample, e.g., autoantibodies and / or analytes, to one or more biomolecules and / or to detect or determine kinetic parameters of the binding events. In various aspects, sample injection time (association) and wash time (dissociation) can be exceedingly variable ranging from as little as 2-3 minutes to hours depending on the instrumentation used and performance of the assay. Aabs and other analytes of interest may have varying kinetics requiring extended association times to detect, or strong binding affinities warranting longer dissociation times in order to have measurable dissociation of Aab and / or analyte from the biosensor surface to provide the relevant kinetic information desired. Flowrate of the running buffer and therefore of the sample is also highly variable and may include a plurality of settings including but not limited to 10-200 µL / min, in various aspects. In one or more aspects, once the sample is exposed to the biomolecule array one or more binding events, e.g., between one or more components of the sample and one or more biomolecules of the array, can be detected. In various aspects, the detection can utilize any of the detectors and / or biosensing techniques disclosed herein that are capable of label free and / or real time sensing, such as SPR. In various aspects, one or more kinetic parameters can be determined for the one or more binding events. In one aspect, the one or more kinetic parameters can be determined via the real-time biosensing of the one or more binding events, e.g., by monitoring binding events over time. In one example aspect, in the case of surface plasmons resonance imaging (SPRi), the SPR detector can monitor increases in reflected light that occurs over baseline. For instance, when an analyte and / or an autoantibody binds to one or more of the biomolecules immobilized near the biosensor surface, a change in the refractive index at that particular spot leads to an increase in the change in percent reflected light. Atty Dkt. No.155529.00024 For any given drug-ligand interaction, the measured kinetic parameters which can be evaluated from the binding signals include the association rate (ka), the dissociation rate (kd), and the max signal observed over the course of analyte injection (Rmax), illustrated in FIG.11. Further parameters which can be calculated from these measures include the equilibrium dissociation constant (KD) and the half-life of the bound complex (t1 / 2). FIG. 11 depicts an idealized sensogram 704 or kinetic trace from which important kinetic information can be derived or determined after proper control-surface normalization and blank-injection referencing. First, the association, or on-rate (ka), of any given interaction dictates the speed at which the analyte and / or autoantibody and biomolecule bind each other and therefore determines the shape of the first phase of the kinetic trace as analyte is flowed over the biosensor surface. Second, the dissociation- or off-rate (kd) of any given interaction dictates the speed at which the analyte and / or autoantibody and biomolecule un-bind each other and therefore determines the shape of the second phase of the kinetic trace as the constant flow of analyte is replaced with running buffer. Additionally, the maximum signal achieved by the binding of an analyte and / or autoantibody in solution to a biomolecule on the biosensor surface termed Rmax, can be determined by a combination of the concentration of analyte and / or autoantibody in solution which drives the formation of bound complexes and the level of functional biomolecule present on the biosensor surface. In traditional analysis where the analyte concentration in solution is known, the ratio of the off-rate over the on-rate yields the equilibrium-dissociation constant (KD) which corresponds to the concentration at which 50% of the complexes are in a bound state, and 50% are in the unbound state and provides a commonly cited measure for the affinity of biological interactions as recognized by those familiar with the field. An aspect is a method for producing and screening Ig-like proteins directly on biosensor chips using IVTT mix for expression followed by (or not followed by) detection with any number of biosensing technologies. Biosensor technologies for detecting biomolecular interactions include a plurality of label-free and real-time detection techniques, such as SPR, reflectometric interference spectroscopy (RifS), single-color reflectometry (SCORE), biolayer interferometry (BLI), focal molography, and field effect transistors (FET) based sensing, among others, without limitation. This disclosure also includes a description for the use of orthogonal assays for validation wherein end-point detection methods are used on Ig-like proteins produced from IVTT mix. End-point detection assays may include fluorescence, luminescence, ELISA, microarrays, bead assays, as examples without limitation. Atty Dkt. No.155529.00024 After the Ig-like proteins are produced and captured / purified onto biosensors, in some aspects, the biosensor is mounted onto the detection instrument and assayed with analyte(s) to measure kinetic parameters. As demonstrated in FIG. 11, these parameters may include association constant (ka), dissociation constant (kd), affinity (KD), avidity, and time of half-life (t1 / 2), as examples without limitation. Each Ig-like protein arrayed on the biosensor will have kinetic parameters measured individually, in some aspects. Data collection can occur individually or in multiplexed simultaneous collection across multiple Ig-like proteins format. Throughput of data collection can vary by instrumentation and Ig-like protein density, as outlined in FIGS. 12A-12C, which depict a biosensor chip as viewed by SPR instrument showing different capture densities and simultaneous collection of qualitative, quantitative, and kinetic data across the entire biosensor chip. Data can be collected across, 1, 10, 100, 200, 500, 1000, 2500, 5000, 10000, 30000, or more Ig-like proteins simultaneously, and any number in between, above or below, without limitation. Biosensor spot density factors into throughput and can be altered based on the expression array format, as shown inFIGS. 12A-12E. FIGS. 13A-13D show data of antibody interactions on a SPOC chip; in this example, the SPOC chip has the capacity to image 10,000 Ig-like proteins simultaneously. In other aspects, quantitative and qualitative binding to the analyte in solution will be assessed. In some aspects, analysis of binding and kinetic parameters can be done through direct detection of the analyte, or via a sandwich assay protocol, wherein a secondary analyte or detection molecule is flowed across the biosensor to increase or alter the binding signal to a detectable level. Analytes may include small molecules, peptides, biologics, proteins (full-length, truncated, domains, multimers, monomers, and mutated variants), biofluids (serum, plasma, blood, saliva, tears, stool, etc.), environmental samples (water, wastewater, agricultural runoff, etc.), as examples without limitation. Secondary detection molecules (analytes) may include antigen-specific antibodies, nanoparticles, lipoparticles, beads, chemical linkers, small binders, peptides, photocleavable peptides or molecules, as examples without limitation. Alternatively, in some aspects, any analyte may be modified or linked to a larger particle to permit detection. These linkages may be induced chemically, enzymatically, or encoded as fusion tags, as examples without limitation. In certain aspects, FIG. 18 demonstrates the Ig-like proteins may contain a capture or fusion tag (such as Halo, GST, His, etc.) or chemical linker modification (PEG, etc.) to permit adherence to the substrate surface constituting protein capture or purification from the IVTT mix for downstream assay. In some aspects, the substrate surface contains the ligand for the capture or fusion tag or suitable chemical linker, thus enabling the removal from the IVTT mix Atty Dkt. No.155529.00024 and subsequent binding to the biosensor surface. In some aspects, the Ig-like proteins can be arrayed on the biosensor surface in singlet or in multiplexed fashion. Biosensors may contain Ig-like proteins arrayed in any number such as 1, 10, 100, 200, 500, 1000, 2500, 5000, 10000, or more, in any number in between, above, or below those listed, without limitation. IVTT mix containing the Ig-like proteins may be expressed on or off biosensor, meaning the capture or adherence of the Ig-like proteins may occur simultaneously with expression or after expression has concluded. Such capture mechanisms may occur with automated equipment, semi- automated equipment, or manually (hand-addition). Addition manually via pipetting or liquid handler transfer may require the placement of gaskets on the surface of the biosensor chip to create an isolated location for contact of the IVTT mix with the surface of the biosensor, preventing cross-over to other areas of the biosensor. Transfer of IVTT mix containing Ig-like proteins to biosensor surfaces may occur via the following non-limiting examples: laminar flow, vacuum pressure injection, pipetting or hand-based fluid transfer, liquid handling instrumentation or devices, incubation in a rocker or bath or incubator or shaker, or other such method of liquid introduction or transfer. In some aspects, specific expression parameters are defined for expression of specific Ig-like proteins, including any combination of the following examples (or lack thereof), without limitation: temperature control, supplemental nitrogen source, agitation (or lack thereof), aeration (or lack thereof), pH, buffer composition (PBS, Tris, HEPES, etc.), salt addition or removal, detergent addition or removal, redox environment (reducing or non- reducing), cofactor inclusion or exclusion (examples include chaperones, disulfide bond catalysts, post-translational modification enzymes including those required for glycosylation), protease inhibitors, and more. For example, it has been demonstrated that reducing the incubation temperature to <30 °C reduces aggregation, most likely by decreasing translation rates which enhances proper Ig- like folding42,65,117. Expression temperature may vary to any degree or subdegree to achieve optimal expression, example temperatures may include 5, 10, 15, 20, 25, 30 °C, or higher or lower and any number or decimal in between, without limitation. The expression conditions may include a device or instrument to agitate and aerate the IVTT mix. Such examples may include but are not limited to rockers, shakers, mechanical vibration, hand-based movement, automated equipment, etc. Furthermore, it has been shown that an addition of nitrogen flow during expression can significantly improve the production rate of Ig-like proteins65. Nitrogen may be added through any means of providing air flow or aeration of the IVTT reaction mix. This may include nitrogen gas cylinders with regulated flow into a reaction chamber, or direct Atty Dkt. No.155529.00024 aeration to the reaction mix, or non-gas-based nitrogen sources could also be considered, which may include spiking liquids directly into the reaction mix. It has been shown that low pH (pH < 4.0) and presence of salt causes aggregation during Ig-like protein production11. Therefore, pH and salt concentration of the IVTT reaction mix may be carefully controlled in some aspects. This may include measuring and altering the pH of the IVTT mix to any level and assessing changes in expression levels. The IVTT mix pH can be altered through addition of acidic or basic solutions, which may include HCl or NaOH, as examples without limitation. The salt content can be altered through the addition of salts (may include salts such as NaCl, KCl, or others without limitation) or removal of salts through methods such as dialysis, as an example without limitation. In some aspects, Ig-like protein sequences may include signal peptides which target the protein to specific regions of the cell. Some of these compartments or regions may exist in IVTT mixes. In certain aspects, the Ig-like proteins may be internalized into these compartments during IVTT expression and production, thereby limiting their capture to the biosensor surface. As such, detergents or surfactants may be added, or excluded, from the IVTT mix to achieve optimal expression and production conditions which improve yield and capture onto the biosensor surface by disrupting the membrane components in the IVTT mix14,100. A non-limiting list of detergents and surfactants includes Triton X-100, Tween 20, N,N- Dimethylmyristylamine N-oxide, Nonidet P40, Polyethylene glycol sorbitan monolaurate, Polyoxyethylenesorbitan monolaurate, CHAPS hydrate, ASB-14, C7BzO, Polyoxyethylene (10) tridecyl ether (mixture of C11 to C14 iso-alkyl ethers with C13 iso-alkyl predominating), n-Dodecyl β-D-maltoside, Octyl β-D-glucopyranoside, Octyl β-D-1-thioglucopyranoside, 3- (Decyldimethylammonio)-propane-sulfonate inner salt zwitterionic detergent, sodium dodecyl sulfate, and more. Detergents and surfactants may be added at any range of concentrations, including, without limitation, 0, 0.01, 0.05, 0.1, 0.25, 0.5, 1% and any number or decimal above or below those listed. Other denaturing agents may, or may not be, included to achieve a similar outcome by disrupting structures within the IVTT mix, which may or may not induce protein refolding and disruption of inclusion bodies or aggregation. As non-limiting examples: urea, dimethylsulfoxide, isoproponal, oxidized glutathione, and more. Denaturing agents may be added in a range of concentrations measured in different units, such as 0-5% inclusive, 0-4 M inclusive, or any number or decimal above or below listed values. As noted here, not all Ig-like proteins require detergents or surfactants, and therefore this is not an absolute requirement of the system or methods described within this disclosure. Atty Dkt. No.155529.00024 Assessment parameters, such as qualitative, quantitative, and kinetic parameters may, in some aspects, be used to determine and identify lead candidates for drug discovery pipelines. In some aspects, the described parameters may inform on Ig-like protein stability and function or bioactivity in the following conditions as examples, without limitation: under stress factors (such as temperature, freeze / thaw, pH, oxidation), susceptibility to protease cleavage, storage conditions with and without stabilizers (such as buffers, sugars, amino acids, bacteriostatic agents, cryoprotectants, pH, etc.). Exemplary uses for biosensor screening of proteins, including Ig-like proteins described herein, include without limitation: affinity ranking, epitope mapping, epitope binning, specificity screening, cross-reactivity screening, on-target screening, off-target screening, lead candidate selection, improving AI / ML algorithms for enhanced binding and affinity predictions, bioactivity assessment, toxicity screening, neutralization capacity, and more. In other aspects, orthogonal validation assays may be performed using end-point assays such as fluorescence or luminescence-based protocols, as examples without restriction. In such cases, expression and production occurs in the same manner as described in this disclosure for biosensors via an IVTT mix. Assay is instead performed in alternative end-point formats. An end-point assay is herein referring to any assay in which a single measurement is performed after a defined incubation period. Whereas kinetic assays include multiple measurements over a specified time interval. End-point assays can be quantitative, semi-quantitative, or qualitative in nature. Applications and uses of the data collected are the same as described for biosensors herein. For example, biological activity may be characterized through a series of stress factors (temperature, freeze / thaw, pH, oxidation, etc.). Biosensors containing Ig-like proteins may be used to assess a variety of stress conditions which mimic the manufacturing, storage, and shipment processes for the final therapeutic product. As such, in some aspects, the biosensor may be screened for binding and kinetic parameters, followed by exposure to a variety of stress conditions, followed by rescreening for binding and kinetic parameters to assess whether there is a change in bioactivity of the Ig-like protein(s) under consideration. Results of this assay may be used to identify lead candidates, storage conditions, and establish the manufacturing requirements. Conditions which may be tested include, as non-limiting examples: temperature, repeated freeze and thaw cycles, changes in pH, changes in oxidation, etc. Each condition may be tested at a range of values, such as -80 to 100 °C inclusive, 0 to 10 freeze and thaw cycles inclusive, 0 to 14 pH inclusive, etc. and any number or decimal above or below those listed. Atty Dkt. No.155529.00024 Another example is monitoring or screening for pH dependent cleavage by proteases (can include inhibitors to prevent cleavage), which screen multiple sequence variants of same Ab to assess for differences in resistance to host protease cleavage. In some aspects, biosensors containing Ig-like proteins may be exposed to proteases wherein binding and kinetic parameters are assessed before and after exposure. A multitude of proteases may be assessed including those derived from intracellular and extracellular regions of any organism. The proteases may include any variety of aspartic, glutamic, metalloproteases, cysteine, serine, or threonine proteases, without limitation. The experiment may be repeated with a multitude of protease inhibitors individually or in cocktail form to confirm the specific protease activity. In some aspects, multiple genetic constructs for the same Ig-like protein but with sequence modifications may be screened to identify those most resistant or susceptible to protease activity. Conditions identified here can serve to inform on bioavailability, pharmacokinetics, degradation, stability, and any other mechanism that contributes to the Ig-like protein bioactivity. A further example is screening storage conditions with and without stabilizers (screen different buffers and sugars, amino acids, bacteriostatic agents, cryoprotectants, pH, etc.) and re-screen chip to determine best solution for activity retention. Screening may occur after storage at different temperatures, including non-cold-chain (high) temperatures, and after multiple freeze / thaws. In some aspects, the biosensor containing Ig-like proteins may be used to screen a variety of storage conditions and solutions by analyzing the binding and kinetic parameters of the Ig-like proteins before and after exposure to the different storage conditions. Results of these assays may be used to optimize manufacturing parameters, such as storage solution, storage temperature, stability, shipping conditions (cold chain required or not), and any other mechanism which contributes to ensuring the Ig-like protein stability and bioactivity after expression and production. A variety of additions to the storage solution can be assessed, including buffers (PBS, Tris, HEPES, etc.), sugars (sucrose, trehalose, mannitol, maltose, sorbitol, etc.), surfactants (polysorbate 20, polysorbate 80, poloxamer 188, PEG 3350, etc.), lyoprotectants (trehalose, sucrose, etc.), pH range 0 to 14 inclusive, pH modifying agents (histidine, citrate, succinate, acetate, phosphate, glutamate, adipic acid, aspartic acid, lactic acid, tromethamine, 2-(N-morpholino)-ethanesulfonic acid, etc.), ionic tonicity-adjusting compounds (sodium chloride, etc.), viscosity altering compounds (sodium chloride, amino acids, arginine, glycine, proline, lysine, etc.), HSA, anti-oxidants (methionine, etc.), chelating agents (EDTA, DTPA, etc.), bacteriostatic agents (sodium azide, etc.), adjuvants (aluminum, Atty Dkt. No.155529.00024 Monophosphoryl lipid A (MPL), QS-21, Cytosine phosphoguanine (CpG), saponins, oil in water emulsion with squalene, etc.), thimerosal, and more. In a non-limiting example of scFv and nanobody expression, DNA sequences encoding scFvs and nanobodies were ordered in a custom circular plasmid vector encoding a C-terminal Halo tag or in a linear DNA format. Plasmid DNA was incubated with human cell free lysate derived from HeLa cells and incubated in microtubes for ~4 h at 30 °C. Resultant IVTT lysate mix was run on a gel and probed with fluorescent TMR Halo ligand to detect successful scFv and nanobody expression, with examples shown for anti-TNFα scFv, anti-IL6 scFv, and anti- human IgM nanobody in FIGs. 19A-19B. In parallel, about 5 µL of IVTT mix was manually spotted via pipette transfer from the microtube to a biosensor chip containing a silicone gasket to create well-like structures on the biosensor surface. The biosensor contained a 3D SAM chemistry activated with EDC:NHS and chloroalkane Halo ligand to permit binding of the produced antibodies onto the sensor surface. After incubation for about 1 hour at room temperature, the biosensor chip was washed and gasket removed for downstream analysis by surface plasmon resonance. FIG.20 shows additional examples of human IVTT mix expression of scFvs and nanobodies, as confirmed by fluorescent gel with TMR Halo ligand. FIG. 21 demonstrates expression of linear DNA scFvs using two different E. coli-based IVTT mixes. Expression was assessed on a fluorescent gel using TMR Halo ligand. Variable expression levels were achieved and conditions were tested with and without disulfide bond enhancers in reducing and non-reducing conditions. Overall, the best expression was seen with the anti-IFNγ scFv and anti-human serum albumin scFv under non-reducing conditions. SPR analysis was performed on the spotted biosensor chip derived from the plasmid DNA vectors expressed with human IVTT mix (as described in FIGS.19 and 20). FIGS.22A- 22B describe the experimental set-up, wherein baseline (background) signal was detected for 30 sec, followed by a 60 min association with the scFv target analyte, and a 2 min dissociation time. Assays were performed as a sandwich assay (FIG. 22B), in which the analyte was first flowed over the biosensor on its own to measure direct binding, followed by a secondary detection molecule (in this case anti-analyte specific antibodies) to amplify the binding signal to permit detection of low signals. Analytes were either purified recombinant cytokines specific to the scFv or nanobody, or human serum for anti-Ig scFvs or nanobodies. Example results are shown in FIGS.23-33 for each individual scFv or nanobody. For anti-TNFα scFv (FIGS.23A-23D) and anti-IL-6 scFv (FIGS.24A-24C), no binding signal was detected with the recombinant cytokine as analyte alone. However, when secondary anti-TNFα and anti-IL-6 were flowed across the biosensor, clear and specific binding was Atty Dkt. No.155529.00024 detectable, indicating that the cytokines were bound to the scFvs expressed on the biosensor chip. This indicates that the human IVTT mix produced bioactive scFvs for these two cytokine targets. Though the response level was fairly low for the IL-6 scFv, the response that was detected was specific, as no negative controls responded, thus indicating this is likely true binding of the cytokine to the scFv. FIGS. 25A-25D shows apparent non-specific binding of the recombinant IFNγ cytokine to positive and negative control scFvs, including a spot with Halo tag alone. FIGS. 26A-26D show the response increases when the secondary anti-IFNγ antibody is flowed across the biosensor to all spots which had binding of the recombinant cytokine, including negative controls. This indicates likely non-specific binding and the results are inconclusive as to the bioactivity of the scFv. FIGS.27A-27C summarize the results of this experiment. FIGS. 28-32 outline the results of the scFv and nanobody assayed with human serum, with results summarized in FIGs.11A-11B. FIGS.28A-28B shows the direct detection of IgM from human serum with an anti-IgM nanobody. The response is specific to IgM as not other isotypes were detected with their respective scFvs or nanobodies. FIGS. 29A-29B show an increase in binding signal at the anti-IgM nanobody spot when the secondary anti-human IgM antibody was flowed across the biosensor surface, further validating the response is specific. No response was detected with the anti-Ige nanobody (FIGS.30A-30B), anti-IgG scFv (FIGS. 31A-31B), or anti-human serum albumin scFv (FIGS. 32A-32B). Results of the screen are summarized in FIGS.33A-33B. In one exemplary embodiment of an assay where antibodies or Ig-like proteins are captured as ligands on the surface, it is contemplated that this biosensor can be utilized to detect factors present in biological fluids for a plurality of screening purposes (FIGS. 28A-28B). Factors may constitute a plurality of biomolecules present in the biological sample that may serve as biomarker for disease (proteins, metabolites, nucleic acids, lipids, carbohydrates, etc.) or other indicator of interest desired to be detected from patient samples like serum, saliva, and urine, among others without limitation. In one exemplary embodiment, an array of Ig-like proteins may be designed to bind a plurality of chemokines or cytokines present in serum for identifying dysregulated immune signatures associated with a plurality of diseases, as example. In certain embodiments, detection of analytes within a biological sample is performed via semi- quantitative end-point assay. In further embodiments, the analytes within a biological sample are detected by real-time sensing and kinetics of the interaction is monitored. Alterations in the analyte, for example mutations in a cytokine to borrow from the previous example, may impact the kinetic signature of the analyte binding to the Ig-like ligands on the array and Atty Dkt. No.155529.00024 therefore further inform the characteristics of the sample beyond simple detection of the analyte in question. These biological screens via label-free assay may be regenerated with a plurality of acidic, basic, or high salt containing regeneration buffers, without limitation, to facilitate repeat screening of the same analyte to confirm binding profiles or subsequent samples to collect data from numerous patient samples. Protein may be expressed using various lysates. For example, scFv may be expressed in human HeLa IVT cell free lysate. SDS-PAGE analysis of C-Terminal HaloTagged scFvs expressed using the Human 1-Step Coupled IVT cell free lysate. All expressed constructs were first covalently labeled with fluorescent Halo ligand then analyzed in reducing (R) and non- reducing (NR) conditions (FIG.34). Another example is scFv expression in PUREfrex E.Coli IVT cell free lysate. SDS-PAGE analysis of C-Terminal HaloTagged scFvs and VHH expressed using the PUREfrex 2.1 E.coli cell free expression system. All expressed constructs were first covalently labeled with fluorescent Halo ligand then analyzed in reducing (R) and non-reducing (NR) conditions. A subset of constructs were expressed before (-Opt) and after (+Opt) 5’ RNA structure optimization (FIG. 35). Multiple single chain antibodies, scFvs and VHHs expressed in E. coli lysate result in high levels of protein expression and capture on the biosensor surface. (FIGS. 36A-36B) anti-Halo signals that confirm expression of the single chain antibodies in scFv and VHH or nanobody formats. (FIG. 36C) scFvs produced and captured anti-p53 scFv, anti-HSA scFv, anti-IL6, anti-EGFR scFv, bound to their target antigens. (FIG. 36D) VHH nanobodies TNFα VHH, and HER2 vHH, bind to their respective target antigens, detected in a label-free format. Two VHH constructs for TNFα and HER2 were chosen for full kinetic analysis and for determination of the limit of detection for the target antigen. (FIG. 37A) Injecting TNFα over the biosensor in increasing concentration from 28 pM to 20 nM using 7 dilutions total resulted in an estimated affinity (KD) of < 49 pM. This is similar to the previously reported affinity of 90 pM for this VHH. (FIG. 37B) Due to the very high affinity of TNFα for the TNFα VHH, this affinity constant is an estimate because practically no dissociation of TNFα was observed during the 30 minute dissociation window; kinetics measured over a significantly longer dissociation window will be required for more accurate calculation of KD. (FIG. 37C, FIG. 37D) Similarly, the affinity constant for HER2 binding to the HER2 VHH was calculated to be 11.5 nM using an increasing 5-step titration of HER2 antigen from 1.8 nM to 150 nM. This is similar to the reported KD of 4 nM in literature for this VHH. (FIG. 37E) Based on these data, the limit of detection for TNFα via the TNFα VHH was measured as < 100 pM in solution, and Atty Dkt. No.155529.00024 the limit of detection for HER2 antigen to the HER2 VHH was measured to be < 2 nM in solution. In some aspects, the addition of expression cofactors may be required to achieve optimal expression. The inclusion or exclusion of such cofactors may be Ig-like protein- specific and therefore vary for each protein arrayed on the biosensor or other substrate. In some aspects, the inclusion or addition of expression cofactors to the reaction mix can be achieved by direct expression via DNA (plasmid or linear) in the IVTT mix simultaneously expressing the Ig-like protein. To achieve chaperone screening from DNA expression within the IVTT mix, plasmid or linear DNA can be directly printed in an array or included within the tube or plate which contains the plasmid for the desired Ig-like protein. Alternatively, recombinant proteins can be added to the IVTT mix before, during, or after expression of the Ig-like protein. Optimal timing of the addition or expression of the cofactor may be required. In such cases, inducible expression via regulatory promoters can be used for directly expression cofactors, or timed addition can be an alternative method for control the cofactor inclusion in the IVTT mix. Cofactors may include chaperones which assist with protein folding. Such chaperones have been included in a variety of combinations, and some of the most commonly used chaperone combinations are available in plasmid formats commercially24,34. Examples of chaperones that can be included are given here, without limitation: dnaK, dnaJ, grpE, groES, groEL, grpE, tig, trigger factor, sHsp family, HSP60 / HSP10 family, HSP70 family, HSP90 family, prefoldin family, co-chaperones Hsp40 family, co-chaperone NEF family, etc. Non-limiting example combinations are shown in FIG. 38. Chaperones can be included (or excluded) alone or in combination in any variation, without limitation. Inclusion or exclusion of specific chaperones may increase yield, solubility, expression, capture, stability, and / or bioactivity by enabling proper folding conformations of the Ig-like proteins. As noted here, not all Ig-like proteins require chaperones, and therefore this is not an absolute requirement of the system or methods described within this disclosure. Once the optimal combination of chaperones is determined for any given Ig-like protein, this can then be applied to all downstream assays, production, and manufacturing. Other materials may be added or excluded from IVTT mixtures to improve Ig-like protein folding, expression, and yield, for example: bacterial glycoengineering for PTMs required for full-length antibodies. Glycosylation enzymes may be added, or excluded from IVTT mixes to achieve addition or absence of N- or O-linked or other glycans; inclusion or exclusion of protease inhibitors in the IVTT mix or derive IVTT mix from cell lines or organisms lacking specific proteases or with mutated proteases; and inclusion or exclusion of Atty Dkt. No.155529.00024 nanodiscs or other lipoparticles to provide a membrane or hydrophobic region to facilitate proper protein folding, reduce aggregation, permit signal peptides to home properly, or other similar mechanism to improve expression, yield, and capture of Ig-like proteins onto the biosensor surface. In some aspects, multiple sequence variations may be screened on biosensors to determine the best sequence to achieve desired expression, yield, capture, or folding of the Ig- like protein. Non-limiting examples include: (1) signal peptide screening, in which a variety of signal peptides can be added or removed from the Ig-like protein sequence which target the protein to specific compartments, regions, or cellular structures, which may still be present in IVTT mixes. This permits proper folding and structural integrity of the Ig-like protein, resulting in increased yield, reduced aggregation, improved folding, expression, or capture of the Ig-like protein onto the biosensor surface; (2) vector screening, including inducible expression (to include riboswitches). A variety of controlled expression platforms exist which permit timed expression of the encoded sequence. Non-limiting examples include lactose, rhamnose, IPTG, and riboswitches. The inducible expression system may be controlled by the addition or removal of a chemical, molecule, or biological component that effectively triggers expression to occur. Riboswitches are an example of control within the mRNA sequence itself, wherein a secondary structure of the RNA results in lack of expression while binding of an effector molecule alters the conformation to achieve post-transcriptional regulation. These controlled expression mechanisms can be included or excluded in the Ig-like protein sequence or vector to permit controlled timing of expression when the IVTT mix is introduced through timed addition of the regulatory component which induces expression; (3) fusion tags to improve yield and solubility. A variety of biological and chemical fusion tags can be included or excluded from the Ig-like protein or vector sequence to improve the Ig-like protein expression, yield, reduce aggregation, permit proper folding, or any other such mechanism to improve expression and capture onto the biosensor surface; (4) screens for combinations of signal peptides and promoters for best yield / expression. Any of the mechanisms listed in this disclosure can be used or not used in any combination, without limitation, to permit the expression and capture of Ig-like proteins onto the biosensor surface; (5) screens for sequences with different charged residues to reduce aggregation (more positive more likely to aggregate in E. coli cytoplasm). Amino acids may contain a charge (positive, negative, or neutral) and as such may affect expression and aggregation from any IVTT mix. A variety of sequences can be tested where the native charge of the Ig-like protein sequence is altered through the addition or removal of amino acids with specific charges to create a positive, negative, or neutral Atty Dkt. No.155529.00024 isoelectric point. These changes may result in alterations to expression, aggregation, folding, and capture of the Ig-like protein onto the biosensor surface. A variety of combinations can be screened to identify the optimum sequence for expression and capture onto biosensor surfaces; (6) scaffold screens for screening multiple formats of a same variable region sequence (screen as Fab, scFv, etc.) to determine the most stable format. Alterations to the scaffold sequence of the Ig-like protein may result in changes in stability. As such, a variety of sequences can be screened wherein changes are made to the scaffold region of the Ig-like protein(s) to assess changes in stability, expression, aggregation, folding, or capture onto biosensor surfaces; (7) disulfide bond insertion and alanine scanning (Cys > Ala). Screen with and without disulfide bonds to determine effect on stability (sometimes scaffold sequence is more important than disulfide bond formation). Disulfide bonds can be added or removed through the addition of Cysteines or removal of Cysteines (conversion or mutation to any other amino acid, such as Alanine, as a non-limiting example); (8) additional of chemical linkers to improve stability. Chemical linkers may be added during or post-expression and capture of Ig-like proteins onto biosensor surfaces. This may be achieved by the addition of functional groups or target sequences onto the Ig-like protein or its sequence. This may be achieved through chemical or biological (enzymatic) treatments. The chemical tags or linkers may be used to improve stability, add additional components to the antibody (such as small molecules or tags), improve yield, expression, folding, or reduce aggregation or any other mechanism to permit expression and capture of Ig-like proteins onto biosensor surfaces; (9) linker screening to improve stability, solubility, and promote specific multimer formation. Some Ig-like proteins VH and VL domains are joined together with amino acid linkers. The length of the linker may vary and the amino acid sequence of the linker may vary. A variety of linker options may be screened via IVTT expression and subsequent capture onto biosensor surfaces by altering the genetic sequence to encode for different lengths and amino acid compositions of the linker. The optimal linker sequence can be identified by those that improve stability, yield, expression, folding, or reduce aggregation or any other mechanism to permit expression and capture of Ig-like proteins onto biosensor surfaces; and (9) express VH and VL on separate plasmids for ratio screening to determine optimum ratio for best yield. In some aspects, the VH and VL domain may need to be encoded on the same or different DNA or RNA vectors (circular or linearized plasmids, or linear constructs). As such, the ratio of the VH and VL domain expression can be controlled in a variety of ways, such as inducible expression as described herein, alternate promoters with a variety of enhancers or operons which control their activity, changes to the non-coding regions which alter expression levels, or inclusion of different concentrations of the genetic Atty Dkt. No.155529.00024 constructs encoding each such that they are present in a variety of ratios. The optimal ratio of VH and VL genetic constructs can be identified by those that improve stability, yield, expression, folding, or reduce aggregation or any other mechanism to permit expression and capture of Ig-like proteins onto biosensor surfaces. In some aspects, additional genetic constructs or recombinant proteins may be required to achieve optimal expression of Ig-like proteins for capture onto biosensors. These components may improve stability, yield, expression, folding, or reduce aggregation or any other mechanism to permit expression and capture of Ig-like proteins onto biosensor surfaces. Components may be added or excluded in any combination to achieve the expression and capture of Ig-like proteins onto biosensor surfaces. For example, sortase A and HRV3C protease may be included for cyclic scFv formation. As demonstrated in FIGS. 39A-39C, the formation of cyclic scFvs requires the addition of the enzyme sortase A and protease HRV3C which recognizes specific amino acid sequences on the N- and C-termini of the Ig-like protein sequence. The amino acid sequences can be included or excluded from the Ig-like protein genetic constructs to achieve targeting by sortase A and HRV3C protease. Sortase A and HRV3C protease can be added to the IVTT mix either through inclusion of a genetic construct encoding sortase A or HRV3C protease or through the direct addition of recombinant sortase A or HRV3C, derived from any species or biologic source. Sortase A or HRV3C may also be added post-expression (post-translationally) by direct addition to the biosensor surface or at any point during the expression reaction. Sortase A and HRV3C may be added together or subsequently, encoded on the same or different genetic constructs, and used in any variation of combinations and timings to improve stability, yield, expression, folding, or reduce aggregation or any other mechanism to permit expression and capture of Ig-like proteins onto biosensor surfaces. A further example is co-expression of disulfide bond catalysts. A non-limiting list of examples of disulfide bond catalysts includes: Erv1p, DsbB, VKOR, DsbC, PDI, DsbA, DsbD, DsbG, SurA, FkpA, PpiA, PpiD, Skp, DegP, Ero1. This non-limiting example list includes proteins which both create and break disulfide bonds and the necessary co-factors required to achieve proper disulfide bond conformation, as shown in FIG. 40. FIG. 40 depicts the E. coli disulfide bond formation pathway, as a representative example of all species, showing the proteins and reactions required to occur within the IVTT mix to achieve proper disulfide bond formation. Disulfide bond catalysts may be included or excluded from the IVTT mix in a variety of ways. They may be added in any combination at a variety of concentrations via the following non-limiting examples: encoding by genetic constructs (1 or more per construct), addition of recombinant proteins derived from any biological source. The disulfide bond Atty Dkt. No.155529.00024 catalysts may be added in a variety of timings: included in the IVTT mix prior to or in conjunction with the Ig-like protein genetic construct, timed addition at any point during the expression process, or post-expression treatment of the IVTT mix or biosensor surface. The optimal ratio and combination of disulfide bond catalysts will be determined by those that improve stability, yield, expression, folding, or reduce aggregation or any other mechanism to permit expression and capture of Ig-like proteins onto biosensor surfaces. In disclosed aspects, cell-free protein synthesis via IVTT mix can be performed in a variety of ways. Examples to modify Ig-like protein genetic constructs and amino acid sequences have been described. For example, as shown in FIGS.41A-41B leucine zippers can be added to the sequences to permit dimer formation104. The leucine zipper sequences can be derived from known binding partners, such as c-Jun / c-Fos, and added to the N- or C-terminus of the heavy and / or light chain sequences to achieve the desired configuration. Alternatively, mechanisms such as the addition of a peptide tag to the Ig-like protein genetic constructs and amino acid sequences (either N- or C-terminus) to improve expression, stability, and capture onto biosensor surfaces. An example of such a tag which has been successful in yeast and E. coli systems is the SKIK tag (Serine-Lysine-Isoleucine-Lysine)107, as a non-limiting example. Furthermore, these mechanisms can be combined in a variety of ways to further improve Ig- like protein expression and capture onto biosensor surfaces. Combination of the SKIK tag and leucine zippers has been successful105. These representative examples of sequence alterations to Ig-like proteins can be screened on biosensors to determine the optimal combination or lack thereof which improves stability, yield, expression, folding, or reduce aggregation or any other mechanism to permit expression and capture of Ig-like proteins onto biosensor surfaces. In some aspects, genetic constructs may be directly derived from B cells from any organism. In certain aspects, the heavy and light chain sequences can be amplified from single B cells by RT-PCR and combined into a genetic construct including a T7 promoter, ribosome binding site, and T7 terminator. This can be achieved through PCR methods, such as overlapping PCR, or through direct cloning into an alternate genetic construct already containing these components. Subsequently, these constructs are expressed using an IVTT mix followed by capture onto a biosensor surface and subsequent screening and analysis of binding and kinetic parameters (FIG.42). A similar method has been described for ELISA58,106,121. As noted above, all aspects described herein may or may not be required for optimal expression and capture of Ig-like proteins onto biosensor surfaces, and may be specific to the Ig-like protein under consideration. Therefore, they may be used in any combination through Atty Dkt. No.155529.00024 addition or removal of components to the Ig-like protein or its genetic sequence, or addition or removal of components from the IVTT mix. Mutationally scanned proteins arrayed on biosensors Various aspects of the present disclosure relate to methods and / or systems for generating a catalogue of wild-type and mutationally scanned genes to be leveraged for the expression of either biologic drugs like antibodies or Ig-like proteins or protein ligands to be arrayed on a biosensor surface for either therapeutic target, or drug library screening. In one or more aspects, the methods and systems consist of an array of unique nucleic acid primers and means for chemical synthesis of the wild-type and mutationally scanned genes. In one or more aspects, the system includes a protein array biosensor with discrete locations containing mutationally scanned protein ligands for the screening of drug molecules and libraries. In certain aspects, the identification of binding patterns to the various mutationally scanned ligands and determination of kinetic binding parameters enables categorization of the biologic drug ligands, or drug molecule analytes, into functional classes. In various aspects, as disclosed herein, a catalogue of genes is generated. The catalogue includes one or more wild-type sequences for one or more proteins of interest in addition to a series of variants harboring nonsynonymous mutations tiled along different codons across the sequence. In some embodiments, the genes in the catalogue are utilized to express proteins for use as ligands on a biosensor array. In certain aspects, the number of mutations introduced creates only a single amino acid substitution. In one or more aspects the mutations create sets of amino acid substitutions, ranging anywhere from 2-50 grouped amino-acid changes, without limitation. In some embodiments, the mutations introduced change the wild-type amino-acid at specific positions to alanine, as in alanine scanning148. In other embodiments the mutations introduced change the wild-type sequence at specific positions to any naturally occurring amino acid (e.g., arginine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, valine, and so on) without limitation. In other embodiments, the mutations introduced change the wild-type sequence at specific positions to any non-proteinogenic or otherwise synthetic amino acid (e.g., 2-Aminoisobutyric acid, homoserine, norleucine, norvaline, Ornithine, beta-alanine, diaminopropionic acid, hydroxyproline, gamma-Aminobutyric acid, 2,4-Diaminobutyric acid, and so on), without limitation4. FIG.43 demonstrates a series of exemplary protein mutants which could be expressed from a catalogue of genes produced via mutational scanning of a wild-type gene sequence. A Atty Dkt. No.155529.00024 protein shows the wild-type native protein (top row) and the series mutational scanned variants (below the top row) derived from introducing single-amino acid substitutions systematically along the length of the peptide chain (in this case a combination of serine, tryptophan, and alanine). Figure adapted from Drici, 202144. In various aspects, the catalogue of genes harboring wild-type and the various mutationally scanned variant sequences can be generated by undirected, random mutagenesis approaches. In some embodiments, mutational scanning is generated via error-prone polymerase chain reaction (EP-PCR) whereby low fidelity polymerases are leveraged alone or in combination with PCR reaction component adjustments (including but not limited to the addition of Mn2+and / or use of increased Mg2+concentrations and / or unequal dNTP concentrations) in order to introduce mutations randomly throughout the gene during sequence amplification151. Further embodiments could make use of chemical mutagenesis to generate the desired mutationally scanned catalogue of genes via a plurality of mutagens including but not limited to: ethyl methanesulfonate, nitrous acid, mitomycin C, N-methyl-N-nitrosourea, diepoxybutane, 1,2,7,8-diepoxyoctane, methyl methane sulfonate, N-methyl-N’-nitro-N- nitrosoguanidine, 4-nitroquinoline 1-oxide (4-NQO), 2-methyloxy-6-chloro-9(3-[ethyl-2- chloroethyl]-aminopropylamino)-acridinedihydrochloride, 2-amino purine, hydroxylamine, bisulfate, and methoxylamine, among other.68In other embodiments, sequences can be mutated using mutator bacterial strains when propagating a plasmid harboring the gene of interest such as Escherichia coli XL1-Red, and Epicurian coli XL1-Red strains, among others and without limitation98. In various aspects, the catalogue of genes harboring wild-type and numerous mutationally scanned variant sequences can be generated by directed approaches. In some embodiments, PCR-based site-directed mutagenesis and / or overlap extension and / or via a plurality of directional cloning techniques can be utilized to systematically introduce mutations along a given gene of interest, producing the mutationally scanned catalogue of gene variants12. In other embodiments, a plurality of gene-editing tools can be leveraged for site-directed mutagenesis including but not limited to zinc-finger nucleases, transcription activator-like effector nucleases, and clustered regularly interspaced short palindromic repeat-Cas-associated nucleases (CRISPR-Cas9), among others. In further embodiments using directed approaches, a plurality of oligonucleotide synthesis strategies can be leveraged to generate the mutationally scanned catalog of gene variant. In various embodiments, a plurality of high-throughput oligo synthesis methods on a plurality of solid-support substrates can be leveraged to build up oligonucleotide sequences Atty Dkt. No.155529.00024 that constitute the gene catalogue including exemplary cases such as but not limited to Affymetrix GenChips, Roche NimbleGen, and Agilent SurePrint platforms among others94. In various aspects the solid-support may consist of a plurality of putative substrate materials including glass, silicon, and complementary metal-oxide-semiconductor (CMOS), among other, and consist of a planar support and / or multiplexed bead-based supports. FIG. 44 illustrates one conception of a multiplexed in situ oligonucleotide synthesis approach whereby nucleotides are added in a controlled fashion to discrete areas of a solid- support microarray. This would enable wild-type genes and their mutationally scanned variants to be synthesized in parallel and high-throughput, in a controlled fashion. In various aspects, the genes would have been built on top of unique primers pre-coupled to the discrete areas, thereby enabling post-synthesis amplification of specific gene sequences using PCR in combination with the respective primer. In various aspects, the post-synthesized and amplified gene sequences would then be integrated into expression vectors or utilized directly for protein expression in vitro as linear expression templates. In some embodiments, the proteins of genes of interest may have little structural information known which limits the amount of rationale mutagenesis which can be engaged in. In this case mutational scanning tiled across the entire coding region would be necessitated to investigate the impact of specific residues on protein activity and drug analyte binding efficacies. In similar embodiments where protein of interest structural information is limited, artificial intelligence (AI) and / or machine learning methods for predicting protein structure and / or drug binding pockets can be implemented to predict residues that would constitute candidates for mutagenesis and empirical testing using tools including but not limited to AlphaFold2, RoseTTAfold, ESMFold, and OpenFold, ProGen, ProteinMPNN, EvoDiff, DiffDock, and RFdiffusion that extend the AI capabilities for novel protein design111. These models may be used to predict putative binding sites of a drug to a target or may be used to generate a computationally designed library of putative target binding biologics like proteins or antibodies and Ig-like proteins which could then be produced as ligands on an array for testing as described herein. In other embodiments, where protein structure is known, more rational site-specific mutagenesis can be engaged in where only restricted regions of a protein of interest are considered for mutagenesis (e.g., solvent exposed residues or those of a membrane protein which may be extracellular or intracellular). In many aspects, mutations at specific residues can be introduced to assess which features of the ligand protein target contribute to drug binding interactions, including the role of post-translational modifications, secondary structure, and active catalytic sites, among others. Atty Dkt. No.155529.00024 In other embodiments, the mutationally scanned proteins are not drug targets, but rather potential drug therapies themselves such as biologics like antibodies or Ig-like proteins. In such cases, mutational scanning of antigen binding residues or other structural features of the antibody / Ig-like proteins yields a plurality of antibody / Ig-like protein variants against which therapeutic targets are screened against as analyte. In some embodiments, AI modeling is used to rationally define the set of mutational variants of the biologic produced as ligands for therapeutic target analyte screening. In certain aspects, the kinetic data is then used to characterize the library of variants, and to inform further AI modeling and iteratively improve the algorithm. In certain aspects, the mutationally scanned proteins can be captured in an array format on any type of biosensor slide or component that is compatible with one or more biosensing techniques capable of label-free and / or real-time biosensing. A non-limiting list of biosensing techniques capable of label-free and / or real-time biosensing via any number of approaches includes those that leverage biophysical, electrical, diffractometric and reflectometric methods including but not limited to surface plasmon resonance (SPR), reflectometric interference spectroscopy, single-color reflectometry, biolayer interferometry, focal molography, and field effect transistor (FET) based sensing, among others. Protein microarrays are in part described in United States Patent No. US 10,983,118, which is incorporated by reference in its entirety. In other aspects, the mutational scanned proteins can be captured in an array format a plurality of substrates capable of label-free and label-dependent end-point read-out including but not limited to mass-spectrometry, or by fluorescent scanning as in traditional microarrays or ELISA. In some embodiments, the mutationally scanned protein ligands on the array are screened with a plurality of drug molecules, including but not limited to fragments and / or compounds of small molecules (e.g., low molecular weight organic compounds) and other classes of therapeutic molecules that may be referred to as biologics (e.g., antibodies, proteins, nucleic acids, and so on) among others without limitation. In various aspects, the mutationally scanned proteins constitute fully folded full-length and / or domains of proteins whereby conformational and / or structural determinants of drug analyte target binding can be interrogated. In other aspect, the mutationally scanned proteins constitute linear polypeptides whereby non-structural, linear primary structural determinants of drug analyte target binging can be interrogated. In various aspects, regeneration of the sensor surface can be employed to facilitate repeated and serial probing of the biomolecules on the array with a plurality of analytes of Atty Dkt. No.155529.00024 interest from a plurality of biological sources of interest. Ideal regeneration injections would not lead to significant loss of mutationally scanned protein ligand capture level or integrity yet would effectively strip away bound drug analytes from previous injection facilitating repeated rounds of drug analyte binding without interference from previously bound analytes. In various aspects, one or more kinetic parameters can be determined for the one or more binding events. In one aspect, the one or more kinetic parameters can be determined via the real-time biosensing of the one or more binding events, e.g., by monitoring binding events over time. In one example aspect, in the case of surface plasmon resonance imaging (SPRi), the SPR detector can monitor increases in reflected light that occurs over baseline. For instance, when an analyte binds to one or more of the mutationally scanned protein ligands immobilized near the biosensor surface, a change in the refractive index at that particular spot leads to an increase in the change in percent reflected light, or response units. FIG.11 depicts an idealized sensogram or kinetic trace from which important kinetic information can be derived or determined after proper control-surface normalization and blank-injection referencing. First, the association- or on-rate (ka) of any given interaction dictates the speed at which a drug analyte binds a mutationally scanned protein ligand and therefore determines the shape of the first phase of the kinetic trace as analyte is flowed over the biosensor surface. Second, the dissociation- or off-rate (kd) of any given interaction dictates the speed at which the drug analyte and mutationally scanned protein ligand un-bind each other and therefore determines the shape of the second phase of the kinetic trace as the constant flow of analyte is replaced with running buffer. Additionally, the maximum signal achieved by the binding of an analyte in solution to mutationally scanned protein ligands on the biosensor surface termed Rmax, is dependent on the concentration of the drug analyte in solution and the level of functional biomolecule present on the biosensor surface. In traditional analysis where the analyte concentration in solution is known, the ratio of the off-rate over the on-rate yields the equilibrium-dissociation constant (KD) which corresponds to the concentration at which 50% of the complexes are in a bound state, and 50% are in the unbound state and provides a commonly cited measure for the affinity of biological interactions as recognized by those familiar with the field. Finally, the complex half-life (t1 / 2) describes the time in seconds it would take for 50% of the bound complex to dissociate and is calculated by taking the natural log of 2 divided by the kd. In many aspects, the kinetic parameters extracted from modeling of the real-time kinetic curves as described above, in whole or in part, are utilized for drug library functional classifications. Atty Dkt. No.155529.00024 In some embodiments, in combination with single and / or double referencing regimes, the kinetic trace can be further corrected to account for differences in the capture level of mutationally scanned protein ligands present on the surface of the biosensor array. Because the binding signal obtained for any given analyte-ligand interaction will be dependent on the number of active ligand captured onto the array (among other variables), varying concentrations and capture levels of the various ligand biomolecules will produce variations in signal intensity and Rmax even if all ligands are binding to an identical analyte. The varying magnitude of the SPR signal from analytes binding to biomolecule ligands with varying capture levels can therefore be normalized by taking into account the immobilization / capture level of the biomolecule ligands on the array. Immobilization level of the mutationally scanned proteins can be assessed by either monitoring of the SPR signal during protein capture onto the array (change in response before and after capture), or by obtaining an Rmaxvalue after probing the array for a common capture tag present on all or most of the proteins on the array. Once the value of capture level is determined (by assessing change in response and / or Rmax) for each ligand, a normalization factor can be computed by calculating capture level ratios. For example, one protein ligand on the array can be selected (e.g., hypothetical ligand A) as the reference from which the capture level is set to 1 (by taking the ratio of the capture level magnitude to itself; [A] / [A]). The normalization factor for each subsequent biomolecules (B, C, n) can then also be computed by taking the ratio of the capture level for A ([A]) over the capture levels of the remaining biomolecules (e.g. [A] / [B], [A] / [C], [A] / [n]). The ratios will produce a normalization factor for each mutationally scanned protein which can then be multiplied to all of the data points in their respective kinetic traces for each ligand of interest which will effectively adjust the kinetic binding signals to compensate for the varying capture levels of each ligand on the array, making the kinetic traces more directly comparable. In some embodiments, in combination with single and / or double referencing regimes, the kinetic trace can be further corrected to account for differences in the capture level of biomolecules present on the surface of the biosensor array. Because the binding signal obtained for any given analyte-ligand interaction will be dependent on the number of active ligand biomolecules captured onto the array (among other variables), varying concentrations and capture levels of the various ligand biomolecules will produce variations in signal intensity and Rmaxeven if all ligands are binding to an identical analyte. The varying magnitude of the SPR signal from analytes binding to biomolecule ligands with varying capture levels can therefore be normalized by taking into account the immobilization / capture level of the biomolecule ligands on the array. Immobilization level of the biomolecules can be assessed by either Atty Dkt. No.155529.00024 monitoring of the SPR signal during biomolecule capture onto the array (change in response before and after capture), or by obtaining an Rmaxvalue after probing the array for a common capture tag present on all or most of the biomolecules on the array. Once the value of capture level is determined (by assessing change in response and / or Rmax) for each ligand, a normalization factor can be computed by calculating capture level ratios. For example, one biomolecule ligand on the array can be selected (e.g., hypothetical ligand A) as the reference from which the capture level is set to 1 (by taking the ratio of the capture level magnitude to itself; [A] / [A]). The normalization factor for each subsequent biomolecules (B, C, n) can then also be computed by taking the ratio of the capture level for A ([A]) over the capture levels of the remaining biomolecules (e.g., [A] / [B], [A] / [C], [A] / [n]). The ratios will produce a normalization factor for each biomolecule which can then be multiplied to all of the data points in their respective kinetic traces for each biomolecule ligand of interest which will effectively adjust the kinetic binding signals to compensate for the varying capture levels of each biomolecule on the array and make the kinetic traces more directly comparable. In some embodiments, all of the above features of the kinetic signatures will be determined to assess the affinity of analytes in patient samples against a plurality of biomolecules on the biosensor surface. In other embodiments, only partial features of the kinetic signature may be evaluated. For example, the dissociation rate may be the focus of analysis and used for relative affinity ranking as this feature of the kinetic trace is concentration-independent and more easily modeled in high-throughput (especially from crude samples where the concentration of analyte is unknown) as performed in off-rate screening (ORS) analysis. The measured off-rates from properly control-surface normalized and blank- injection referenced data (so called double referencing) can then be used as inputs for a plurality of machine learning algorithms to make associations between these patient-specific AAb kinetic metrics and salient clinical features that would ultimately inform the diagnosis and prognosis of a plurality of autoimmune and relate disorders including but not limited to various SARDs. In various aspects, after primary sample injection and kinetic measurement, the use of a plurality of secondary antibodies may optionally be employed to (i) enhance the signal from the bound primary analytes in cases where signal is low and / or (ii) to identify the isotype(s) of immunoglobulin bound to the spots of the sensor surface (e.g., IgG1-4, IgM, IgA, IgD, IgE, etc.) which may have relevant physiological implications and be incorporated into diagnostic, prognostic, and other decision making. For example, an SPR sensor prepared with a biomolecule array may be exposed to serum from a patient having developed AAbs against one Atty Dkt. No.155529.00024 or more biomolecules. After the initial detection of the AAbs, the SPR sensor may then be exposed to anti-IgG secondary antibodies, or other secondary antibodies against specific isotypes such as IgM, IgA, IgD, IgE. In this example, the binding of the anti-IgG antibody may then be detected via the same SPR technology immediately following initial serum sample injection. For instance, as one example, all of the detected analytes and their kinetic parameters obtained from the screening of a subject’s sample on the assay (in addition to any further information gained about the analytes via injection of a plurality of secondary analyte detection molecules) would constitute a patient-specific “test profile” consisting of the detection of all the biomarkers present in the sample and their kinetic parameters as unique for said subject and may be created based on any combination of: the kinetic parameters, e.g., ka, kd, Rmax, and / or KD, and / or other descriptors related to kinetics, dynamics, and affinity, and additional information, e.g., isotype of immunoglobulin autoantibodies detected and detection and / or identification of specific analytes and / or biomolecules and / or cells present within the patient sample as captured by any number of analyte detection molecules as described herein. Importantly, a subject / patient’s test profile may also consist of salient features such as lack of detection of certain biomarkers, or the absence of binding events at certain biomolecules present on the biomolecule array which would also contribute to a unique test profile and constitute in whole or in part aspects of the profile which aid in making important clinical associations as described herein. Additionally, a subject / patient’s test profile, in addition to the kinetic parameters outlined herein, may also include results from other readout techniques using the same or similar biomolecule array to detect bound sample analytes via a number of compatible methods including but not limited to fluorescent readout or matrix-assisted laser desorption / ionization-time of flight mass spectrometry (MALDI-TOF MS). Results from numerous readouts from both real-time and end-point detection methods, either label-free or not, could be combined to form an integrated signature and may also constitute a subject / patient’s test profile. In various aspects, a subject’s test profile and / or one or more of the kinetic parameters and additional information obtained from the methods herein can be utilized to assist in the identification and / or diagnosis of one or more disease, such as an autoimmune and related disease, and / or to assist in the prognosis of one or more disease (e.g., autoimmune and related disease), and / or to assist in the temporal staging of one or more disease that may be in early preclinical phases, or medium, or advanced stages, and / or to assist in the characterization of the sub-type of one or more disease (e.g. autoimmune and related disease) as it relates to the Atty Dkt. No.155529.00024 particular organ-specific involvement present in the disease pathogenesis, and / or to assist in the determination of effective therapeutic regiments when treating one or more disease by determining likely responders who would benefit from particular treatments from non- responders. In certain aspects, a subject’s test profile may be used to assist in the identification and / or diagnosis and prognosis of the subject with a particular disease (e.g., autoimmune disease), e.g., by determining whether or not a correlation is present between the subject test profile and one or more attributes associated with a particular disease such as their organ- specific subtypes, temporal progression, and their likely response to available therapy. For example, in one aspect, it may be determined that a correlation is present between the subject’s test profile and a specific SARD profile of a specific SARD disease and / or disease subtype including but not limited to SLE, MCTD, SjS, SSc, PM, NPSLE, or neuropsychiatric SLE, and the subject may be diagnosed with the SARD disease or disease subtype based wholly or partially on this correlation. In the same or alternative aspects, a subject’s test profile may be used to provide a prognosis for the subject with respect to a particular disease, e.g., by determining whether a correlation is present between the subject test profile and one or more attributes associated with a positive or negative likelihood for a healthy recovery from a particular disease. For example, in one aspect, a correlation (or not) between a subject’s test profile and attributes of a disease or subtype may be used to predict if the subject has a high or low risk of complications and / or a slow or fast disease progression and / or to predict an upcoming active state or flare up of disease symptoms. In one or more aspects, the subject’s test profile may be used to provide a determination of whether a particular course of treatment would be beneficial, e.g., by determining whether or not a correlation is present between the subject test profile and one or more relevant attributes associated with a treatment outcome for a particular course of treatment for a particular disease (such as autoimmune or related disease. In one or more aspects, the subject’s test profile may be used to provide a determination of whether a particular subject is at risk for particular organ involvement by determining whether or not a correlation is present between the subject test profile and one or more relevant attributes associated specific organ failure for a particular disease. In one or more aspects, a correlation may be determined and / or the attributes of a particular disease may be determined using one or more algorithms, including machine learning algorithms, executed on one or more computing devices having a processor and memory. Atty Dkt. No.155529.00024 In various aspects, the attributes associated with a particular disease that are used in the above correlation determinations may be generated via traditional bioinformatic and statistical approaches, in addition to any number of advanced machine learning processes. For example, relevant clinical symptoms and / or medical chart information and biomarker data from a multitude of subjects having a disease, including subjects having autoimmune or related diseases and their subtypes, e.g., SARD or SARD subtypes, may be inputted into a database or program that allows for clinical associations to make via standard bioinformatics or via a machine learning model for learning the relevant profiles to be developed. Analysis may be performed via any number of mathematical models or methods, including traditional bioinformatics, machine learning, deep learning, artificial intelligence, or other techniques. FIG. 45 depicts one example workflow according to one aspect of the methods and systems disclosed herein. It should be understood that the example workflow depicted in FIG. 45 is just one example and other methods and workflows are contemplated by the disclosure herein. As can be seen in the aspect depicted in FIG. 45, a biomolecule array 200 is depicted that consists of one or more plurality of biomolecules (antigens, analyte capture molecules, etc.) 717 coupled to a biosensing surface of the array 208. In the aspect depicted in FIG. 45, subject / patient samples 809 are exposed to the biomolecule array 200 and label-free real time detection of one or more binding events can occur between one or more diverse components / analytes 800 of the sample and one or more plurality of the biomolecules on the biosensing surface. In certain aspects, one or more kinetic parameters, e.g., the kinetic parameters 818, can be determined based on the one or more binding events, which may be determined based on a change in signal over time 817 during sample exposure. In various aspects, the kinetic parameters 818 and additional information obtained as listed and described from the methods herein (e.g., isotype determination, etc.) can be utilized for forming patient specific kinetic information that inform test profiles along with other information relevant to identifying and / or diagnosing, prognosing, and characterizing one or more autoimmune and related diseases, as discussed above. In one or more aspects, one or more algorithms, including machine learning algorithms, executed on one or more computing devices 821 having a processor and memory may be utilized to aid in identifying and / or diagnosing and prognosing and characterizing one or more autoimmune and related diseases 827. In some aspects, extracting a combination of, or all of these discrete features of the kinetic parameters and analyte binding patterns and other sample characteristics is one way of obtaining a comprehensive discrete representation from distributional statistics. In certain aspects, these features can form feature vectors that are then utilized to facilitate training of Atty Dkt. No.155529.00024 machine learning algorithms for downstream tasks such as informing the diagnosis and prognosis of a plurality of diseases (such as autoimmune diseases and / or SARDs) and producing correct associations from new data. Even though in some aspects only partial features are required to train a learning algorithm (only a sub-vector is sufficient, for example, the dissociation rate may be the focus of analysis as in ORS), the features of the kinetic parameters disclosed herein can be used to assess the relative affinity of AAbs in patient samples against a plurality of disease-related antigens or other biomolecules on the biosensor surface employed to accurately and robustly determine and generalize a plurality of learning algorithms. In aspects, in addition to extracting features in all aforementioned cases, to increase the accuracy of the trained learning model, a plurality of data pre-processing methods for standardization, feature extraction, outlier detection, and noise removal may be incorporated into the feature space. Given the pre-processed data, the desired downstream task can be achieved via different approaches. As one example, the machine learning algorithm or model can be trained using a supervised learning approach. For instance, by providing labels for training samples, for instance, healthy vs. unhealthy, supervised learning can be utilized to predict the outcome for new data. Assuming the labels, the following downstream tasks can be achieved: (i) Binary Classification: learning a binary classifier to differentiate between healthy and unhealthy patients, and (ii) K-ary Classification: In addition to distinguishing between healthy and unhealthy, it can be used to categorize sub-types of a disease, such as SARDS or other autoimmune diseases. Assuming well-characterized and labeled AD and SARD samples, supervised learning methods allow for patterns within the kinetic trace signature or specific kinetic parameters extracted from patient specific AAb binding profiles to be associated with known clinical features of the sample or labels to train a predictor that can ultimately be generalized to new unseen test data points. Depending on the size of our dataset, both shallow learners (e.g., K-ary support vector machines) and deep learners (e.g., perceptron or convolutional neural networks (CNNs)) may be employed to make associations between AAb kinetic parameters and / or profiles as outlined above and clinical symptomology to yield predictive algorithms for AD and SARD diagnosis and prognosis. As another example, the machine learning algorithm or model can be trained using an unsupervised learning approach. Collecting data and labeling them often requires domain experts which can be costly. Deducing information from unlabeled data falls under the Atty Dkt. No.155529.00024 unsupervised learning paradigm. Unsupervised algorithms are based on the notion of constructing representations of the input where it is used for decision-making, predicting future inputs, efficiently communicating the inputs to another machine, etc. A classic use of unsupervised methods is clustering where the extracted representations of AAb kinetic parameters and / or profiles are divided into K-subgroups to yield a partition for AD and SARD diagnosis and prognosis. For instance, unlabeled kinetic trace signature or binding rate datasets can be fed into unsupervised algorithms for which identified features will be utilized to cluster samples based on the unique observed kinetic trace signature. Similar to supervised algorithms, these algorithms can be shallow such as Gaussian Mixture Model (GMM), or K-means, or deep such as autoencoder-based methods (Gaussian Mixture Variational Autoencoder (GMVA), or GAN-based models (sub-space clustering (DASC). As yet another example, the machine learning algorithm or model can be trained using a semi-supervised learning approach, as described above, machine learning algorithm development may be pursued via a plurality of approaches including but not limited to supervised and unsupervised learning approaches. To combine both paradigms and take advantage of both labeled (only a few of them are available) and unlabeled data, semi- supervised techniques suggest a constrained predictor that systematically classifies the diagnosis and prognosis of a plurality of diseases. Although shallow semi-supervised algorithms such as graph-based techniques have demonstrated promising results, the deep implementation of these algorithms e.g., GAN-based techniques, are employed in large datasets. Artificial intelligence (AI) and ML approaches present new challenge for software as medical device (SaMD) as they possess the unique ability to continually learn from real-world feedback (training) and improve performance in an iterative fashion (adaptation). The traditional SaMD regulation via 501(k) was not tailored for fluid software like AI / ML and thus the Food and Drug Administration (FDA) regulatory regime is shifting to a total product lifestyle (TPLC) approach. The TPLC involves (i) initial premarket assurance of safety and effectiveness, (ii) approach for modifications after initial review via established SamD Pre- specification (SPS) and Algorithm Change Protocols (ACP), and (iii) transparency and real- world performance monitoring of AI / ML-based SaMD which all contribute to ensuring Good Machine Learning Practices (GMLP). In one aspect, if the assay described herein has advanced to the degree of routine implementation in a College of American Pathologists (CAP) and / or Clinical Laboratory Improvements Amendments (CLIA) certified setting, the associated ML Atty Dkt. No.155529.00024 algorithms would be considered a SaMD and may be subject to FDA and GMLP compliance in-line with the TPLC model. In various aspects, the assay described herein may be classified as “research use only” (RUO), as a point-of-care (POC) device / assay, as a Laboratory-developed Test (LDT) in a CAP / CLIA certified laboratory setting requiring the verification of certain performance characteristics and on-going proficiency testing, as an FDA-approved medical device or diagnostic assay, or any combination of certifications listed. In all cases, appropriate calibrators, controls, and routine testing procedures can be developed and in some aspects, may be provided in a kit format for commercial purchase. Appropriate reference intervals for the acceptable range of biomolecules captured on the array which categorize patients into different categories, including qualitative or quantitative measures (such as positive, negative, out of range high, out of range low, within range, or other similar category) can be established. Reportable limits for analyte concentrations within the samples which can be reasonably detected with defined accuracy and precision, and which may define the linear behavior of the analytes can be established to ensure robust assay performance via laboratory quality control metrics based around Levey-Jennings charts and established Westgard rules, among others. Biosensors may be used in established facilities or laboratories qualified to perform the test based on proper certifications, or cores where samples are shipped for screening, or biosensors and / or assay kits may be shipped out under conditions established not to negatively impact assay performance to facilities where screening would occur. Assay results could be reported via a plurality of established methods including but not limited to Laboratory Information Management Systems (LIMS), Laboratory Information System (LIS), phone or other electronic applications (such as integration with electron health records (EHR) or electronic medical record (EMR) systems, website or phone-based applications, portals designed for patients and / or doctors, etc.), paper records, and fax, among others. Therapeutics The disclosed sensor-integrated proteome on chip (SPOC) platform is a novel biosensor platform to perform, for example target validation, antibody specificity screening (cross- reactivity), on- and off-target binding (including transient, high-off rate binding interactions), and to characterize and identify lead candidate therapeutics in a single assay. SPOC is the first and only proteomic biosensor capable of providing qualitative, quantitative, and kinetic binding affinity data against hundreds or thousands of extracellular domains and intracellular protein targets simultaneously on a single 1.5-sq cm SPR biosensor chip. Real-time kinetic SPR analysis provides insight into transient interactions to identify off-targets earlier in the Atty Dkt. No.155529.00024 discovery pipeline for lead candidate identification, and to permit design of tunable on- and off-rates for binding interactions, a feature not offered by any other high-throughput proteomics platform today. Approximately 30% of drug failures are due to off-target effects and another 40-50% due to lack of efficacy. The future of drug discovery calls for a high-throughput screening method to reduce drug failures due to off-target effects or lack of efficacy, saving biopharmaceutical companies millions of dollars in lost development costs. The new SPOC platform is poised to solve this unmet industry need by providing the ability to screen drugs on up to 2500 targets simultaneously, providing real-time, label-free target binding and kinetic information. Additionally, standard catalog chips will be available. Applications include, among others: off-target binding, target discovery, mutational drug-escape detection, and drug- repurposing. Therapeutic drug discovery suffers from a 90% failure rate in late-stage development. SPOC technology has potential to save biopharmaceutical companies millions of dollars lost to drug failures by i) identifying biologically-relevant off-targets; ii) identifying targets for new or re-purposed drugs; iii) pro-actively screening for mutational drug escape; iv) improving candidate characterization through kinetic information; v) high-throughput production and screening of Ig-like protein sequences. For example, for off-target binding, about 30% of clinical trial drug failures are due to off-target toxicity. Traditional in silico screening methods only predict biologically-relevant interactions. SPOC technology is poised to assist drug development through simultaneous evaluation of up to 2500 potential binding targets on a single biosensor chip – enabling rapid assessment of more targets prior to in vivo studies and providing biologically-relevant binding data with kinetic measurements. For on-target kinetics, new monoclonal antibody therapeutics are benefiting from rapid AI design, resulting in a need for high-throughput screening for lead candidate identification. SPOC technology combined with SPR real-time kinetic measurement provides affinity data in a high-throughput manner for improved candidate discrimination, which is not possible with any other state-of-the-art technology. An example of mutation-driven Drug Escape is mutations in COVID19 eliminated or reduced binding of mAb therapeutics, which is a problem facing all pathogen-targeting drugs. SPOC can fill this gap in technology by providing a platform to produce hundreds of protein variants for simultaneous screening of mAb therapeutics. For drug re-purposing, approximately 40-50% of drugs fail in clinical trials due to lack of efficacy. SPOC would enable drug developers to re-screen failed drugs for other potential binding interactions, providing a Atty Dkt. No.155529.00024 possible pathway to recover these lost investments. Also as used in library screening, high- throughput expression of Ig-like protein libraries designed by AI, derived from patients, or a combination thereof, on biosensors can provide for direct analysis of analyte binding, affinity ranking, determination of kinetic parameters, qualitative and quantitative binding, specificity screening, cross-reactivity screening, and lead candidate identification. Methods and systems have been described for the synthesis of an inventory of nucleic acids containing the coding region of wild-type and mutationally scanned genes. In various aspects, the methods and systems for the synthesis of an inventory of nucleic acids containing the coding region of wild-type and mutationally scanned proteins of interest can be used to express proteins to be captured as protein ligands or ligands on a biosensor array. In certain aspect as disclosed, ligand represents an active biomolecule immobilized on a surface or a biosensor. Ligands include proteins, peptides, polypeptides, antibodies, immunoglobulin-like proteins (Ig-like), antibody fragments. These ligands may be produced using cell based or cell- free in-vitro systems or cell free lysate or cell free extract or reconstituted expression systems or tissue lysates. In certain aspects, ligand on the surface or on biosensor is assayed with analyte in solution, to detect or measure or assay or monitor the binding or interaction of analyte with ligand. In certain embodiments, when ligands are antibodies, immunoglobulin-like proteins (Ig-like), or antibody fragments on the surface of a biosensor may be assayed with proteins, peptides polypeptides, organic molecules or metabolites, or nucleic acids as analytes. The binding or interaction of an analyte in solution or a biosample can be detected, measured, assayed, or monitored. Disclosed herein are methods and systems for the screening of one or more drug molecules or libraries of the same against one or more biosensors containing an array of mutationally scanned target protein ligands. In various aspects, the methods and systems for the screening of one or more drug molecules and libraries of the same against a biosensor containing a mutationally scanned protein ligand array can be used for determining one or more kinetic binding parameters of each drug molecule to one or more mutationally scanned protein ligands. In various aspects, the one or more kinetic binding parameters of the drug molecules to one or more mutationally scanned protein ligands on the biosensor can be used for the delineation of functional classes in the drug molecule library. Disclosed herein are methods and systems for the screening of one or more therapeutic target molecules and libraries of the same against a biosensor containing mutationally scanned Atty Dkt. No.155529.00024 protein ligands which constitute drugs themselves, such as biologics like antibodies or their derivatives, such as immunoglobulin-like proteins (Ig-like). In various aspects, the methods and systems for the screening of one or more therapeutic target molecules and libraries of the same against a biosensor containing mutationally scanned protein ligands which constitute drugs themselves, such as biologics like antibodies or their derivatives such as Ig-like proteins, can be used for determining one or more kinetic binding parameters of target molecule analytes to one or more mutationally scanned protein or biologic ligands. In various aspects, the one or more kinetic binding parameters of the target molecules to one or more mutationally scanned protein biologics on the biosensor can be used for the delineation of functional classes in the mutationally scanned protein drug biologic array. In various aspects, the mutationally scanned protein or biologic ligands can be generated via AI modeling against one or more putative therapeutic targets. In certain aspects, a method for detecting binding of one or more drug molecules to one or more proteins and their mutational variants using a mutational protein ligand array is disclosed. The method can include exposing a drug molecule-containing analyte sample to a biosensor surface of a mutational protein ligand array. The array can include protein ligands and their mutational variants coupled to the biosensor surface at discrete locations. The method can further include detecting one or more binding events between one or more drug molecule analytes in the sample to at least a portion of the mutational protein ligand array. The method can further include determining one or more kinetic binding parameters associated with one or more binding events detected. In certain aspects, a method for detecting binding of one or more therapeutic target molecules to one or more protein biologics such as antibodies (or their derivatives such as Ig- like proteins) and their mutational variants using a mutational protein ligand array is disclosed. The method can include exposing a therapeutic target-containing analyte sample to a biosensor surface of a mutational biologic protein ligand array. The array can include protein or biologic ligands such as antibodies (or their derivatives such as Ig-like protein) and their mutational variants coupled to the biosensor surface at discrete locations. The method can further include detecting one or more binding events between one or more target molecule analytes in the sample to at least a portion of the mutational protein biologic ligand array. The method can further include determining one or more kinetic binding parameters associated with one or more binding events detected. Atty Dkt. No.155529.00024 In various aspects the one or more kinetic binding parameters of the target molecules to one or more mutationally scanned protein biologics on the biosensor can be used to inform and iteratively improve AI models of the interactions. Epitope mapping Some aspects are methods and uses for arrays of proteins on a substrate for screening patient biofluids containing antibodies to specific diseases, such as infectious diseases, against mutationally scanned variants of pathogen proteins for the purposes of epitope mapping. The success of mAbs therapies depends not only on identifying the right target but also on selecting target epitopes that are accessible and functional. Understanding epitopes is therefore essential for designing highly specific antibodies that provide precise interventions in disease treatment. Epitope mapping relies on a variety of experimental and computational techniques. Although X-ray crystallography is considered the gold standard for accurately identifying and resolving interactions between antibodies and individual amino acids of an antigen at the atomic level, obtaining a crystal structure for an antibody-antigen complex is time-consuming, often taking months. Additionally, not all complexes successfully form crystals, leading to a moderate success rate. In alanine scanning technique, target residues are rationally or randomly selected for substitution by alanine with a neutral nonpolar side chain thereby interrupting any interaction between the amino acid side chains of the binding surface, leading to loss of antibody binding that is used as a measure to identify critical amino acids or the binding epitope. Performing additional substitutions with alternative functional side chains, such as serine (neutral polar), aspartate (acidic), and lysine (basic side chain), in the same region as validation could significantly reduce incidence of false positives and potentially facilitate the identification of epitopes and paratopes with higher confidence. In one example of epitope mapping, an array of pathogen proteins can be arrayed on a substrate which contain various amino acid substitutions or mutations to any amino acid or modified amino acid. The array can consist of any number of amino acid substitutions from 1 to 50,000, as non-limiting examples. In some aspects, the array may also contain the wild-type or canonical version of the protein for comparison. In aspects, the array is screened with a biofluid collected from a symptomatic, recovered, vaccinated, or otherwise treated patient. Patients may be human, animal, or any other biological organism producing immunoglobulin (Ig)-like proteins. In some aspects, biofluids may be serum, plasma, CSF, saliva, stool, tears, sweat, as non-limiting examples. In some embodiments, biofluids may contain Ig-like proteins which recognize and bind pathogen proteins arrayed on the substrate. Atty Dkt. No.155529.00024 In the aspects described herein, an assay can be performed with biosensors, such as gold-coated SPR chips, nanowires, biolayer interferometry, nanowires, etc. as non-limiting examples (FIG. 4). In some aspects, the assay can be performed using fluorescence, luminescence, flow cytometry, cell-based arrays, as non-limiting examples. For example, single amino acid residues within the epitope regions of therapeutic targets, CD20 and PD1, were substituted with side chains of diverse functionality, including alanine, aspartic acid, lysine, and serine, resulting in a comprehensive mutant library. The recombinant expression plasmids array printed in nanowell slides was then used to produce SPOC protein biosensor via in-situ cell-free nanowell protein expression / capture for subsequent screening by SPR against FDA-approved therapeutic mAbs to yield binding kinetics data. FIGS.53A-53C. Amino acid sequences of (FIG.53A) CD20 and (FIG.53B) PD1 extracellular domains, showing the positions of mAbs epitopes identified in literature, including rituximab, ublituximab, ocrelizumab and nivolumab, pembrolizumab, cemiplimab. Cysteine and asparagine residues highlighted in red fonts are responsible for post translational disulfide bonds and glycosylation, respectively. Residues targeted for substitution were underlined. A CD20 mutant library indicating substituted positions is shown in FIG.53C. Using a SPOC array method with in silico recombinant plasmid maps for CD20 (FIG. 54A) and PD1 (FIG. 54B) extracellular domains sequences, each gene was subcloned onto a custom cell-free expression-compatible pT7CFE1 plasmid for expression with an N-terminal HaloTag (for CD20) or C-terminal HaloTag (for PD1). An SPR flowcell image of a SPOC biosensor (FIG. 54C) shows distinct protein spots as well as a hockey-shaped fiducial (highlighted in red square) for locating each member protein. Sensograms of capture validation of SPOC protein chip using anti-HaloTag VHH, mouse anti-HaloTag, and mouse anti-p53 are shown in FIG. 55. Sensograms of serial injections of rituximab, showing varying apparent kinetic affinities (KD’) of interactions with CD20 mutants are shown in. new FIG.56, and sensograms of serial injections of Ocrelizumab showing varying apparent kinetic affinities (KD’) of interactions with CD20 mutants are shown in. new FIG.57. In some aspects, data collected may include information on binding such as qualitative, quantitative, semi-quantitative, or kinetic values. Kinetic information may include ka, kd, KD, t1 / 2, as non-limiting examples. In some aspects, binding data and information may be differential between unique sequence variants arrayed on the substrate. Differential binding patterns may be analyzed to inform on binding location, or epitope, on the pathogen protein Atty Dkt. No.155529.00024 which indicate the location at which Ig-like proteins in the biofluid are interacting with or binding to the pathogen protein. Comparative analysis of protein expression and capture is possible (FIG. 58). The heatmap shows the response level of SPOC biosensor proteins to 220 nM anti-HaloTag VHH. The results validate the expression of CD20 mutants on the chip, and compare the capture levels against CD20 wildtype and few controls. Protein capture level was also screened using a mouse anti-HaloTag and rabbit anti-HaloTag antibodies. Overall, the capture validation result indicates that all CD20 mutants were relatively well-expressed compared to the wildtype CD20 and controls except variants T159K and I162S, which produced very low RU values of 0.5 and 2.3 RU, respectively. Therefore, kinetic data obtained with these mutants will not be accepted. Heatmaps of maximum response unit (Rmax) of CD20 mutants to the highest injected concentrations of (new FIG.59A) rituximab (505 nM) and (new FIG.59B) Ocrelizumab (522 nM). Rmax below 10 units were assumed to indicate zero binding or very low interaction. In 99% of cases, all substitutions of residues of rituximab and ocrelizumab epitopes residues (area within the red lines) resulted in zero and negative Rmax values compared to the wildtype, implying a significant decrease in binding relative to the wildtype. Multiple alanine substitutions of the epitope as in E168:P169:N171 and P172:S173:E174:K175 completely eliminated interactions. Further, disulfide bond mutations C167 and C183 resulted in lower response unit compared to the wildtype. CD20 variants, T159K and I162S, were poorly expressed / captured (marked ‘x’), and therefore, Rmax of binding to rituximab and ocrelizumab should be ignored. KD's were obtained following substitutions of rituximab epitope residues (EPANPSEK) with (FIG.60A) alanine / neutral non polar side chain, which includes single and double alanine- substituted cysteine-167 and 183 residues responsible for disulfide bond formation as well as multiple alanine substituted mutants of EPANPSEK; (FIG.60B) aspartate or acidic side chain acid, (new FIG. 60C) lysine / basic side chain, and (FIG. 60D) serine / neutral polar side chain. KD' of the wildtype CD20 were used for comparison. Apparent kinetic or binding affinities (KD') of CD20 mutants to rituximab are shown in the heatmap in FIG.60E. Center, green lines enclose rituximab epitope region. The term apparent binding affinity (KD') was used to distinguish from actual binding affinity (KD) since the full rituximab antibody was used for screening, not Fab. KD's were obtained following substitutions of ocrelizumab epitope residues (YNCEPANPSEKNSPSTQYC) with (new FIG. 61A) alanine / neutral non polar side chain mutants, including multiple alanine substituted mutants and substituted cysteine-167 and 183 Atty Dkt. No.155529.00024 residues responsible for disulfide bond formation, (new FIG.61B) aspartate or acidic side chain acid mutants, (new FIG. 61C) lysine / basic side chain mutants, and (new FIG. 61D) serine / neutral polar side chain mutants. KD' of the wildtype CD20 were used for comparison. Apparent kinetic or binding affinities (KD') of CD20 mutants to ocrelizumab are shown in the heatmap in new FIG.61E. Green lines enclose ocrelizumab epitope region. The term apparent binding affinity (KD') was used to distinguish from actual binding affinity (KD) since the full ocrelizumab antibody was used for screening, not Fab. Binding patterns may be analyzed by statistical analysis, bioinformatics, or artificial intelligence and machine learning tools to determine the binding epitope. Epitope determination may result in identification of candidate vaccine targets, protein engineering (vaccine antigen design, multivalent antigen design, cross-reactive antigen design), and targets for antibody therapeutic development (FIG.62). Species cross-reactive target arrays Described is an array of proteins on a substrate for assays in which the proteins represent homologs or orthologs across species for the assessment of drug cross-reactivity or binding to each unique species sequence. Data can be collected by a plurality of biosensing techniques (FIG.4), including surface plasmon resonance, biolayer interferometry, nanowires, electrical, optical, or piezoelectric methods. In other embodiments, data can be collected by methods such as fluorescence, luminescence, without limitation. Data indicating cross-reactive binding across species may include qualitative, quantitative, semi-quantitative, or kinetic information. These data may indicate cross-reactive epitopes, or binding regions, including necessary post-translational modifications, which permit cross-reactive species binding (FIG. 51). Interpretation of epitopes may result in the identification of necessary mutations or DNA or protein sequence alterations required to induce or eliminate cross-reactive binding of the drug to alternate species than the intended target species. In some embodiments, such information can be used to inform on the development of transgenic animal models to represent disease indications or for drug safety and efficacy testing. In some aspects, a particular gene or gene family can be selected from one species and the related homologs and orthologs identified in other species of interest. This invention covers all species and is not specific to any singular human or animal model species. Species may include human, mouse, bacteria, plant, or any other biological organism of interest without limitation. All protein homologs and orthologs from a gene or gene family may be arrayed on a single substrate to permit a singular assay to be performed on all simultaneously. Homologs and orthologs may also include different isoforms of a protein, transcript variants, or disease- Atty Dkt. No.155529.00024 specific or disease-related protein versions of interest. Protein homologs, orthologs, and isoforms can be arrayed in any number from 1 to 50,000 as non-limiting examples. In some embodiments, the arrayed proteins can be screened with a therapeutic antibody, peptide, small molecule, biologic, or other therapeutic modality to collect qualitative, quantitative, semi-quantitative, or kinetic data. In some aspects, the binding data or lack of binding data will be indicative of cross-reactivity between species or across different versions of the protein. Differential binding data can be analyzed using statistical, bioinformatics, or artificial intelligence and machine learning techniques to determine binding patterns. In some aspects, the binding patterns may indicate critical protein sites, amino acids, or post- translational modifications required to permit or inhibit cross-reactive binding of the therapeutic modality. These critical sites can then inform required modifications to alternative species homologs or orthologs from the target species that would induce or eliminate cross- reactive binding, as desired. In some aspects, these critical binding sites or requirements of the binding site may constitute an epitope of the therapeutic modality’s interaction with the protein. Such modifications can inform the development of transgenic animal or species models for use in drug development. In some aspects, these transgenic animal models can be used to assess the therapeutic modality’s safety and efficacy in development, discovery, pre-clinical evaluation, or clinical trials. Proteins arrayed on biosensors labeled with its coding sequence In some aspects, the process of expressing a protein ligand using in vitro system, the coding sequence is attached to the expressed protein via covalent or non-covalent methods, and has a linker sequence which can be used to remove the coupled protein / nucleic acid molecule from the surface to which it is bound (FIG.46). The primary goal is to enable the removal of a complex comprised of a unique protein ligand in complex with an analyte (antibodies, proteins, other biomolecules, drugs, etc.) to allow for identification of the original coding sequence of the protein ligand to which the analyte is bound. Several methods have been developed for linking coding sequences to an expressed protein for the purposes of mRNA or cDNA display. In this context, a library of nucleic acids coding for different proteins is expressed in vitro and each unique coding sequence is tethered to the expressed protein to pan for binders of a specific analyte or to identify proteins with specific functions. Nemoto et al first developed a puromycin-based method for linking mRNA to protein and later developed this method for using more stable cDNA via reverse transcription.8,155Puromycin is an antibiotic derived from Streptomyces alboniger with a natural function of protein translation inhibition via ribosome-mediated incorporation into the Atty Dkt. No.155529.00024 peptide chain as it is being translated. This takes advantage of the natural ability of puromycin to bind nucleotides and RNA to develop mRNA display, which is now commonly used for peptide and protein evolution102,151. In one embodiment, the attachment of coding sequences to the expressed protein ligand could be achieved via the use of a puromycin linker 48, 49, 55, 56 (FIG.48). The lambdaN peptide is another commonly used method for linking RNA to its cognate protein. The lambdaN peptide is derived from the RNA binding domain of antiterminator protein N from lambda bacteriophage and binds to a specific RNA sequence which forms a hairpin structure15. Again taking advantage of a naturally occurring moiety in nature which promotes the linkage of a protein to a nucleic acid, Baron-Benhamou et al developed this feature into a useful method to study mRNA metabolism and to tag proteins of interest with mRNA reporters15. Others have used the lambdaN peptide for visualizing RNA in cells by tagging the RNA with GFP33and for purification of RNA molecules via a fusion of GST and lambdaN peptide35. In some embodiments, lambdaN peptide may be used near the 5’ end of the protein coding sequence to capture the mRNA of the protein as it is translated15, 35,64(FIG. 49, FIG.50). This disclosure seeks to improve upon these methods by applying this technique to proteins expressed in nanowells and captured onto biosensor surfaces. Using this method, mutational DNA libraries can be pooled into a single well, attached to and expressed into their cognate proteins, captured onto a biosensor surface with a photocleavable linker, and incubated or flowed over with analytes of interest. Following detection of bound analytes, the complexes can be removed from the biosensor via photocleavage, purified, and sequenced for identification of proteins of interest. Various aspects of the present disclosure relate to the expression and tagging of a protein ligand with its coding sequence. The coding sequence, in some aspects, may be DNA, cDNA, RNA, mRNA, or any variation thereof, and contain flanking common primer sequences useful for PCR amplification as recognized by those proficient in the art. The coding sequence, in some aspects, may be attached to the protein ligand it encodes before, during, or after the protein has completed translation. The protein ligand, in some aspects, may be labeled with its coding sequence using a variety of methods, such as puromycin linkage or lambdaN-boxB linkage, including others, without limitation. In some aspects, the protein ligand will have a linker to allow for removal from the biosensor surface. This linker may be a photocleavable linker, an enzymatic linker, biochemical cleavable, pH cleavable, electrochemical cleavable, a thermosensitive linker, or any variations thereof to those familiar in the art, without limitation, Atty Dkt. No.155529.00024 for non-destructive removal of protein from a surface. The protein ligand, in some aspects, may be captured on the surface of the biosensor using a variety of methods. As analytes are incubated with the biosensor surface containing the photocleavable, coding sequence-labeled protein ligand, they may interact with and bind to the protein ligand. The bound analytes may then be detected using a plurality of biosensor-based methods, including SPR, reflectometric interference spectroscopy (RIfS), single-color reflectometry (SCORE), biolayer interferometry (BLI), focal molography, and field effect transistors (FET) based sensing, among others, without limitation. The bound analytes may also be detected, either alone or in combination with, spectrometry-based methods, including fluorescence, absorbance, luminescence, among others, without limitation. Following detection of analytes interacting with surface-bound protein ligands, the protein-analyte complex may be removed from the surface via the photocleavable linker and the addition of an appropriate light source, which may include ultraviolet light, visible light, infrared light, and others, without limitation (FIG. 47). Alternatively, the protein-analyte complex may be removed from the surface via enzymatic cleavage. Following removal from the surface, the protein-analyte complex may be used for downstream analysis. In various aspects, the protein ligand may include soluble proteins, membrane proteins, transcription factors, enzymes, immunoglobulins, signaling proteins, structural proteins, hormones, transport proteins, contractile proteins, storage proteins, or transport proteins, mutations, variations, PTM modified proteins, and many others, without limitation. In various aspects, the protein ligand may be wild type or mutated, PTM modified, isomer, either naturally or purposefully. In various aspects, the analyte may include proteins, not limited to antibodies, protein binding partners, antibody-protein conjugates, antibody-drug conjugates, and other protein- molecule conjugates; drugs, not limited to small molecule drugs, biologicals, nucleic acid- based drugs, etc.; nucleic acids, fluorophores and other detection agents, lipids, carbohydrates, metabolites, Plasma Proteomics Plasma proteomics is the large-scale study of proteins in blood plasma. Plasma Proteomics focuses on analyzing the protein content of blood plasma or serum or whole blood, the liquid component of blood that remains after the removal of blood cells. This field of study aims to identify and quantify the various proteins present in plasma or serum or blood, understand their functions, and discover how their levels change in response to different conditions, diseases, or treatments. Plasma proteomics is a powerful tool for advancing our Atty Dkt. No.155529.00024 understanding of human health and disease, with significant potential for improving clinical outcomes through better diagnostics, personalized medicine, and new therapeutic approaches. In certain embodiments, the disclosed biosensor array is applied to detecting and analyzing large number of proteins, metabolites, nucleic acids, toxins, organic molecules, biomolecules in plasma, serum, or blood samples. In certain other embodiments the present innovation is applied to detecting and analyzing large number of proteins, metabolites, nucleic acids, toxins, organic molecules, biomolecules in saliva sample, eye fluid, CSF fluid, urine sample, in cell sample or cell lysate sample, or in tissue sample or tissue lysate sample, or stool sample, or any other biosample. The samples may be animal samples or plant samples, or other biological samples or environmental samples. In certain aspects, the samples are human samples. Plasma Proteomics has many challenges, some of which include: Complexity and Dynamic Range: Plasma contains a vast number of proteins with a wide range of concentrations, making it challenging to detect low-abundance proteins amidst more abundant ones; Protein Variability: Protein levels in plasma can vary due to biological factors such as age, gender, diet, and circadian rhythms, complicating the analysis; and Technical Variability: Differences in sample collection, processing, and analysis methods can introduce variability, affecting the reproducibility and reliability of results. Applications of plasma proteomics includes, but is not limited to: Biomarker Discovery: Plasma proteomics is crucial for identifying biomarkers— proteins whose presence, absence, or altered levels indicate specific diseases or physiological states. This can aid in early diagnosis, prognosis, and monitoring of diseases; Understanding Disease Mechanisms: By studying the changes in plasma protein profiles, researchers can gain insights into the molecular mechanisms underlying diseases, leading to the development of new therapeutic targets. In certain embodiments it can be used to unravel protein interactions networks, disease networks and pathways; Personalized Medicine: Proteomic data can help tailor medical treatments to individual patients based on their unique protein profiles, enhancing the efficacy and reducing the side effects of treatments; Clinical Diagnostics: Development of new diagnostic tests for diseases such as cancer, cardiovascular diseases, and infectious diseases; Therapeutic Monitoring: Monitoring the effectiveness of treatments and adjusting them based on changes in protein levels; and Atty Dkt. No.155529.00024 Drug Development: Identifying targets for new drugs and understanding the mechanisms of action and potential side effects. Current methods and technologies for plasma proteomics include: Mass Spectrometry (MS) that is the core technology used in proteomics, and allows for the identification and quantification of proteins by measuring the mass-to-charge ratio of peptide fragments; chromatography, such as liquid chromatography (LC), may be used to separate complex protein mixtures before MS analysis, improving the detection of low-abundance proteins; Two- Dimensional Gel Electrophoresis (2-DE) that separates proteins based on their isoelectric point and molecular weight, allowing for the visualization of protein expression patterns; immunoassays that include techniques like ELISA (enzyme-linked immunosorbent assay), which use antibodies to detect specific proteins with high sensitivity and specificity; Olink Proteomics that utilizes a proprietary technology called Proximity Extension Assay (PEA) to measure proteins in plasma. PEA involves the use of pairs of antibodies linked to DNA oligonucleotides. When these antibodies bind to their target protein, the DNA strands come into proximity and are extended, creating a unique DNA barcode for each protein. Olink panels are used to simultaneously measure hundreds of proteins in a single sample, allowing for high- throughput protein analysis. The DNA barcodes are quantified using real-time PCR or next- generation sequencing, providing relative quantification of protein levels; SomaScan that is a proteomics platform developed by SomaLogic, employing a unique approach based on modified DNA aptamers. SomaScan uses modified DNA aptamers, which are short, single- stranded DNA molecules that bind to specific protein targets with high affinity and specificity. The current SomaScan platform measures thousands of proteins simultaneously in a single sample. The aptamer-protein interactions are quantified by hybridizing the aptamers to complementary DNA strands and measuring the resulting fluorescence or by sequencing; Alamar Biosciences nanoparticle technology to detect low-abundance proteins. Nanoparticles can amplify the signal from target proteins, enabling the detection of proteins present at very low concentrations. The use of nanoparticles conjugated with specific binding agents (e.g., antibodies or aptamers) is used for protein detection. Their platform is designed to allow for the simultaneous analysis of multiple proteins in a single sample; and Seer that utilizes a diverse set of engineered nanoparticles, each designed to interact with proteins based on various physicochemical properties such as size, shape, and surface chemistry. This allows for the capture and enrichment of a broad array of proteins from plasma. The use of multiple nanoparticles is offered for unbiased capturing of proteins, enabling the detection of a wide range of proteins. After protein capture and enrichment by the nanoparticles, the proteins are Atty Dkt. No.155529.00024 processed and analyzed using mass spectrometry (MS). This integration leverages MS in protein identification and quantification. SPOC plasma proteomics In exemplary embodiments, SPOC in situ nanowell synthesized array of antibodies or antibody fragments or Ig-like proteins (FIGS.66 and 67) are used to screen and detect, measure and characterize proteins or metabolites or biomolecules or nucleic acids, screened with one or more of plasma samples, serum samples, blood samples analyte solutions, biosamples, tissue or cell lysates, patient samples, environmental samples. In embodiments of SPOC chip (FIGS. 66 and 67), each spot is an immunoglobulin-like protein (Ig-like) (antibody fragment or full- length antibody) that is designed and produced to bind to a specific plasma protein or specific epitope on protein, or specific biomarker or antigen or metabolite or nucleic acid or biomolecule or toxin or environmental analyte or ion or organic molecule. In exemplary embodiments, the Ig-like array is produced using in situ expression in nanowells, followed by capture on surface or biosensor. In aspects, the Ig-like array is produced by pin spotting or printing of prior synthesized and / or purified Ig-like molecules onto a surface or biosensor in an array format. In exemplary embodiments, the Ig-like array is produced by pin spotting or printing of commercially sourced Ig-like molecules onto a surface or biosensor in an array format. In exemplary embodiments, SPOC chip with array of Ig-like proteins is screened with plasma or serum sample, or analyte solution, buffer solution, or chemical solution, or biosample, or environmental sample, or urine sample, or stool sample, or other biosample, human derived sample, animal derived sample, or plant derived sample. As used herein, analyte is a moiety that binds to one or more of on-surface proteins or protein ligands or Ig-like proteins or aptamers or other affinity agents. Non limiting examples of analyte include plasma protein, proteins, metabolites, biomolecules, organic molecules, ions, nucleic acids, lipids, sugars, glycans, PTM modified proteins, mutated proteins, protein isoforms, mutated DNA sequences, antibodies, immune factors, inflammation factors, cytokines, chemokines, complements, organelles, exosomes, vesicles, nanoparticles, particles, complexes. As used herein, analyte solution or analyte sample is any sample or solution, either naturally derived, human derived, or synthetic derived sample, chemically derived sample, that is screened with proteins or Ig-like proteins or aptamers or affinity agents arrayed on biosensor or any surface. An analyte solution can have one or 10 or 100 or 1000 or 10,000 or 100,000 or million or billion or trillion or more analytes. Atty Dkt. No.155529.00024 In exemplary embodiments, the biosensor produces first response to binding of plasma proteins or biomarkers or metabolites or analytes to one or more antibody fragments or Ig-like proteins on biosensor chip or surface (FIG.69). In certain embodiments, the amplitude of the first biosensor signal or response is used to evaluate the quantitative amounts of plasma protein or analyte present in the sample or analyte solution. In some aspects, the biosensor or surface is regenerated by chemical or other treatment and rescreened with sample or analyte solution. In certain embodiments, rescreening enables confirmation of biosensor signal or response and / or reproducibility of biosensor signal or response. In some aspects, one or more secondary antibody, aptamer, or other affinity-based binder specific to the target plasma proteins or biomarkers or metabolites or analyte (FIG.71) are used for further confirmation, high sensitivity detection, quantitative analysis each bound analyte plasma protein or biomarker or metabolite or analyte. In certain embodiments, the amplitude of the second response is used to evaluate the quantitative amounts of plasma protein or analyte present in the sample or analyte solution. In certain embodiments, the biosensor signal or response is used to evaluate the quantitative amounts or functional nature or characteristic or kinetic parameters of plasma protein or analyte present in the sample or analyte solution. As used herein, secondary affinity binders can be one or more of antibodies, antibody fragments, nanobodies, aptamers, proteins, synthetically designed chemicals or biochemicals, synthetic affinity molecules, nucleic acids, nucleic acid protein conjugates, bioconjugates, or a solution or sample comprising the same. In certain embodiments, secondary affinity binders are presented in a solution or sample and screened with biosensor or surface. In certain embodiments, solution or sample containing secondary affinity binders are screened. Secondary affinity binders and solution comprising secondary affinity binders are used interchangeably here. In exemplary embodiments, the biosensor or surface is regenerated by chemical or other treatment and rescreened with secondary binders. In certain embodiments, rescreening enables confirmation of response and / or reproducibility of response. In exemplary embodiments, regeneration and rescreening is done repeatedly, once or twice or up to 5 times, or up to 10 times or up to 100 times or up to 1000 times or higher. In some aspects, post regeneration of biosensor or surface, rescreening is done with a different sample or analyte solution. In some aspects, secondary affinity binders are selected to bind to different epitopes on the plasma protein or analyte, than the epitope that binds to antibody fragments or Ig-like proteins on the biosensor surface. In certain embodiments, this enables simultaneous and / or Atty Dkt. No.155529.00024 non-overlapping or non-competing binding of secondary binders and the Ig-like proteins on biosensor or surface. In some aspects, secondary affinity binders compete with Ig-like proteins on biosensor or surface, to bind to the plasma protein or analyte bound on the biosensor or surface. In some aspects, secondary affinity binders bind to specific epitopes or mutations or PTM modifications on the plasma protein or analyte. In exemplary embodiments, secondary affinity binders are selected to binding to specific epitopes or mutations or PTM modifications on the plasma protein or analyte (FIG.73). In certain aspects, tertiary affinity binders are used to screen the biosensor or surface bound plasma protein or analyte, after screening with the secondary affinity binders. In certain embodiments, the biosensor signal or response is used to evaluate the quantitative amounts or functional nature or characteristic or kinetic parameters of plasma protein or analyte present in the sample or analyte solution. In certain embodiments, tertiary affinity binders bind to either plasma proteins or analytes, or secondary affinity binders. In certain aspects, quaternary affinity binders are used to screen the biosensor or surface bound plasma protein or analyte, after screening with the tertiary affinity binders. In certain embodiments, biosensor signal or response is used to evaluate the quantitative amounts or functional nature or characteristic or kinetic parameters of plasma protein or analyte present in the sample or analyte solution. In certain embodiments, quaternary affinity binders bind to either plasma proteins or analytes, or to secondary affinity binders, or to tertiary affinity binders In exemplary embodiments, there may be one or more wash steps after each of the screening steps, such as but not limited to, initial assay of analyte solution or plasma or biosample, after secondary assay with secondary affinity binders and so on. In exemplary embodiments, the secondary affinity binders or agents may be mixed with analyte solution or plasma or biosample, and flowed over the biosensor or surface for binding of secondary affinity binders to on-biosensor bound plasma proteins or analytes. The secondary or tertiary or quaternary affinity binders can be biosensor signal amplification agents (FIG.72). Exemplary binders include nanoparticles or gold particles or micro particles or beads modified with secondary or tertiary or quaternary antibodies or aptamers or affinity binders on their surfaces. In exemplary embodiments, following assay or screening with secondary affinity binders, or tertiary affinity binders, or quaternary affinity binders, the biosensor or chip is follow-on treated with chemical solution or biochemical solution or enzymatic assay. In certain embodiments, the follow-on treatment results in change of chemical or ionic or biochemical or electrical or magnetic or thermal or biophysical property of medium or local medium, resulting Atty Dkt. No.155529.00024 in producing a response or signal, which may be a response or signal localized or limited to each spot on the biosensor or surface array. In some aspects, the above screening steps reduce false positives or false negatives, increase sensitivity and / or specificity, increase accuracy. In the aspects, following initial screening or assay with plasma sample or analyte solution / sample, a reagent assay is performed to covalently link the bound plasma proteins or analytes to the Ig-like proteins or scFvs or nanobodies on the biosensor or surface, using covalent linker molecules (FIG. 70). In some aspects, this enables multiple regeneration steps and successive screening assays. In some aspects, following an initial screening or assay with a plasma sample or analyte solution / sample, a reagent assay is performed to covalently link the bound plasma proteins or analytes to the Ig-like proteins of scFvs or nanobodies on the biosensor or surface (FIG. 70). In certain embodiments, this enables multiple regeneration steps and successive screening assays. In some embodiments, the Ig-like proteins on the chip may have special functional residues or terminals to form covalent bonds with a linker molecule. A biomolecule with covalent bonds with scFv and bound plasma or analyte is formed. Next, the sample is treated with chemical reagent, biochemical reagent, regenerating reagent, or salt solution. This results in plasma proteins or analytes covalently bound to the scFvs or Ig-like proteins on the biosensor or chip. In certain embodiments, covalent linkers can be short or long, can have spacers, can have PEGS, or have functional moieties. Covalent linkers may also be DNA oligomers with bioconjugation or functionalization at the ends. In exemplary embodiments, the covalent linker molecules bind covalently to amine moieties on plasma protein or analyte and / or scFv or Ig-like protein on biosensor or surface. In exemplary embodiments, the covalent linker molecules bind covalently to carboxylic moieties on plasma protein or analyte and / or scFv or Ig-like protein on biosensor or surface In exemplary embodiments, the covalent linker molecules bind covalently to n-terminus or c- terminus on plasma protein or analyte and / or scFv or Ig-like protein on biosensor or surface In exemplary embodiments, the covalent linker molecules bind covalently to functional residues or chemical terminations or PTMs on plasma protein or analyte and / or scFv or Ig-like protein on biosensor or surface. In exemplary embodiments, scFv or Ig-like protein on biosensor or surface are designed to carry one or more functional residues or terminations or PTMs that later forms covalent bond with the covalent linker molecules. In exemplary embodiments, protein-protein interaction assays or other functional assays are performed on plasma proteins or analytes bound to the biosensor or chip (FIG.74). Atty Dkt. No.155529.00024 In exemplary embodiments, one or more of secondary assays, tertiary assays, quaternary assays are performed after covalent linking of plasma proteins or analytes to the scFvs or Ig-like proteins on biosensor or surface. In certain embodiments, covalent linker has bioconjugation and / or functional ends, and is a PEG molecule, or DNA molecule, or a polymer, or protein, or synthetic molecule, or organic molecule. In certain embodiments, covalent linker contains spacers to achieve short of long linker molecules. In exemplary embodiments, a sandwich assay or an immunoassay is used. In exemplary embodiments, scFvs or nanobodies or Ig-like proteins or aptamers on biosensor or surface are designed and produced to have high association rate, slow / low association rate, off-rate of binding, or on-rate of binding to plasma proteins or analytes. In exemplary embodiments, scFvs or nanobodies or Ig-like proteins or aptamers on biosensor or surface are designed and produced to have high disassociation rate or to have slow / low disassociation rate to plasma proteins or analytes. In exemplary embodiments, scFvs or nanobodies or Ig-like proteins or aptamers on biosensor or surface are designed and produced to have high affinities or avidities of binding to plasma proteins or analytes, or to have low affinities or avidities of binding to plasma proteins or analytes. In exemplary embodiments, secondary affinity binders have high association rate or on- rate of binding to plasma proteins or analytes, or to have slow / low association rate or on-rate of binding to plasma proteins or analytes. In exemplary embodiments, secondary affinity binders have high disassociation rate or off-rate of binding to plasma proteins or analytes, or to have slow / low disassociation rate or off-rate of binding to plasma proteins or analytes. In exemplary embodiments, secondary affinity binders have high affinities or avidities of binding to plasma proteins or analytes, or have low affinities or avidities of binding to plasma proteins or analytes. In exemplary embodiments, tertiary affinity binders have high association rate or on- rate of binding to plasma proteins or analytes, or have slow / low association rate or on-rate of binding to plasma proteins or analytes. In exemplary embodiments, tertiary affinity binders have high disassociation rate or off-rate of binding to plasma proteins or analytes, or have slow / low disassociation rate or off- rate of binding to plasma proteins or analytes. Atty Dkt. No.155529.00024 In exemplary embodiments, tertiary affinity binders have high affinities or avidities of binding to plasma proteins or analytes, or have low affinities or avidities of binding to plasma proteins or analytes. In exemplary embodiments, drug molecules are screened for binding to the plasma proteins or analytes bound the biosensor or surface (FIG.75). In exemplary embodiments, detected plasma protein or analyte is analyzed with other results or sample screenings to identify diagnostic biomarker or therapeutic biomarker or precision medicine biomarker. The disclosed biomolecule array could be immensely useful for identifying plasma markers of disease recovery. A certain percentage of patients afflicted with almost any disease have a full recovery and the reasons for their recovery are typically poorly understood. For some diseases, this percentage is extremely low (<10%) and determining the factors which resulted in those patients’ complete recovery could have important positive effects on future patients with the same disease. As an example, patients diagnosed with glioblastoma multiforme (GBM) face an estimated 10-year survival rate of <1%1. The <1% of GBM patients that survive 10 years following their diagnosis and do not have known genetic determinants of a positive prognosis have likely undergone physiological changes that are responsible for their survival, and these changes may be detectable in their blood. Similar phenomena are notable for most cancers, CNS disease, autoimmune disease, etc. This disclosure provides a method for studying those physiological changes. As an example, blood from a long-term GBM survivor could be compared at various time points (if possible) to blood from a large population of GBM patients. Using a panel of proteins on biosensors that cover the human proteome, all known oncological protein variants found in GBM patients, pathogen proteins, and Ig-like proteins for capturing serum proteins, serum or plasma from patient blood could be incubated with or flowed over a biosensor containing the protein panel. This panel would detect serum antibodies specific to wild type and mutated human proteins as well as pathogen proteins, as pathogens are quickly becoming a known etiological factor for many common human diseases. Using the Ig-like proteins in the panel, the levels of various immune and other blood components could be measured. All measurements obtained from these biosensors, machine learning analysis could be performed to make thousands of pairwise comparisons and detect patterns across the different patient groups. If specific factors were identified, such as an antibody against a specific cancer variant, it would then also be possible to perform further downstream analyses to determine the exact Atty Dkt. No.155529.00024 identify of antibodies against that variant and replicate them in the laboratory where they could be studies and potentially used as a therapeutic for patients in the future. Development of SPOC Antibody Fragment DNA Library for HT Production and Screening An integrated cell-free SPOC approach disclose for high throughput DNA library preparation from phage and single B-cell clones, followed by antibodies production and label- free binding assessment and functional screening by label-free methods. The DNA library includes from phage and single B-cell clones-derived linear expression cassettes (LEC), circular plasmid DNA libraries expressing full-length antibodies and antibody fragments such as heavy chain (VH), light chain (VL), Fab, Fab’, CH, CL, Fc, F(ab’)2, Fv, among others (FIG. 62). In antibody development, commonly employed strategies for studying repertoire of B- cell repertoires of different species to discover fully humanized therapeutic monoclonal antibodies include hybridoma technology, fluorescence-activated cell sorting (FACS)-based single B-cell cloning (SBC) and in-vitro display technologies such phage display. Hybridomas secrete antibodies into culture medium, which can be used for binding and activity screening (Ref. 161). On the other hand, the SBC approach can survey a large proportion of the B-cell immune repertoire, isolate dominant and antigen-specific clones for generating a diverse array of antibodies. It also allows the recovery of the natural cognate pairing of the antigen-specific heavy (VH) and light-chain (VL) variable regions. Similarly, in vitro display libraries such as phage display are constructed from combinatorial pairing of VH and VL genes isolated from human donors, immunized animals or vaccinated humans, or antibodies containing synthetic complementarity-determining regions (CDRs). While these platforms have been successful in antibody binding assessment and complement each other in identifying therapeutic antibodies, they however have inherent limitations that limit their use for functional screening. For example, most hybridomas have murine constant domains (Ref. 53), and therefore any functional screening needing human Fc contribution requires V-gene cloning and recombinant plasmid construction for human IgG production. Where different formats or IgG isotypes are required, separate plasmids for each type need to be constructed. Further, SBC and display platforms require time-consuming reformatting, cloning and expression prior to activity screening. Both platforms also rely heavily on bioinformatic analysis of the Ig sequences for triaging and cloning antibody genes into plasmids for transfection and expression. In phage display approaches, the phenotype-genotype linkage aids in high-throughput panning and binding-based screening, but functional screening often requires reformatting of single-chain Atty Dkt. No.155529.00024 variable fragments (scFv) and Fabs into IgGs, creating a bottleneck and prolonging the time to lead antibody selection. These plasmid-based approaches to generate recombinant mAbs (rmAbs) is labor-intensive, which limits the value of applying these technologies for rapid antibody discovery campaigns. Plasmid-free systems have been developed to express antigen-specific antibodies from mammalian cells without plasmid construction, using linear Ig VH, VL gene expression cassettes (Ref. 76, Ref. 129) and antigen-binding fragments (Fabs; Ref. 103) from cell-free protein production systems. However, these methods used either adherent 293 T cell line for expressing rmAbs in a 6-well plate format (Ref. 76) or suspension Expi293F cell line for expressing recombinant mAbs in a 96-well format (Ref.129), hence are not amenable to high throughput antibody production. Also, they resulted in low recombinant IgG expression levels (1 ng to 2 μg / ml), sufficient for binding assays but not for functional screening (Ref. 162). Zhang et al. 2021 developed an alternative to plasmid-based generation of rmAbs by constructing linear expression cassettes (LECs) of cognate VHand VLpairs from phage and SBC clones, each of which contained the required elements for mRNA transcription and translation for IgG or Fab antibody expression in mammalian cells. In that method, flow cytometry was used to identify antibodies from the spleen of human Ig transgenic mice to three different antigens and phage display approach to isolate antibodies specific to two additional antigens. The VHand VLproducts from single B cells and phage colonies were recovered by PCR. The cognate VH and VL pairs were used in an overlapping PCR to construct individual LECs, followed by co-transfection of the VH-LEC and VL-LEC genes into Expi293F cells, resulting in the production of antibodies in the concentration range of 0.05 μg / ml to 145.8 μg / ml, suitable for assessment of binding and functional screening using high throughput reporter systems. An exemplary SPOC Linear DNA library preparation from Phage library and Single B Cell Clones is described. A fully integrated SPOC high throughput cell-free method for rapid production of HaloTag antibody fragment biosensor array from VH / VL-LEC (and plasmid) DNA libraries, obtained from phage and SBC clones, enables instant multiplexed kinetic label- free screening. Modifications of the Ref. 162 protocol for implementation on the SPOC platform embody the following; 1. Use of gene assembly technique to splice HaloTag sequence to the 5’ terminal of amplified VH and VL fragments prior to subsequent assembly of other DNA fragments to form the LEC as described previously. 2. Printing the LECs DNA library in nanowells Atty Dkt. No.155529.00024 3. In situ cell-free expression and immobilization of antibody fragments on HaloTag chloroalkane-modified biosensor chip via the Halo capture tag on antibody fragments 4. Instant label-free kinetic screening of antibody fragment array for binding assessment and functional characterization (FIG.71). Amplification of antibody fragments from Single B Cells (e.g., VH / VL): To obtain antibody fragments library from SBC clones, Ig genes such as VH, VL are amplified by reverse transcription (RT) reaction and nested PCR as described previously (Ref. 76, 162). cDNA libraries are synthesized from SBC by preparing 6 μl reaction mixture containing Superscript III reverse transcriptase (Thermo Fisher Scientific, 18080093) and random hexamer (Gene Like, 26-4000-03), incubating at 55°C for 5 minutes, 20°C for 10 minutes, 55°C for 30 minutes. Nested PCR step 1 was performed in 25 μl reaction mixtures containing 2 μl of RT reaction products, 12.5 μl of AmpliTaq Gold 360 Master mix (Thermo Fisher Scientific, 4398881), and a pool of primers, each at a concentration of 0.1 μM (Table 1, Ref. 162). Nested PCR step 1 condition involves: 95°C for 5 minutes followed by 35 cycles of 95°C for 30 seconds, 55°C for 30 seconds, 72°C for 1 minute, and a final elongation step at 72°C for 7 minutes. Table 1: Primers for first round of PCR of Single B Cell Clones (Ref. 162; SEQ ID NOs: 1-18 in descending order) Following the first PCR step, the next step involves appending Halo capture tag on 5’ end of the PCR step 1 VH / VLamplicons. This was achieved by Gibson assembly or splicing by Atty Dkt. No.155529.00024 overlap PCR extension wherein few nucleotides at the 3’ end of the Halo sequence is designed to overlap with 5’ end of the PCR step 1 amplicon. Following splicing of Halo sequence onto the VH / VL amplicons, 1 μl of the resulting fusion amplicon was used as the template for the next PCR step PCR in a 25 μl reaction mixture containing 12.5 μl of AmpliTaq Gold 360 Master mix, and the nested primer pool each at a concentration of 0.1 μM (Table 2). Step 2 PCR was performed using the same conditions as step 1 but with annealing temperature of 58°C. For generation of linear expression cassette (LEC), the second set of forward and reverse primers used for nested PCR step 2 above included sequence tags overlapping with the leader sequence at 3’ end of PCMV DNA fragment and 5’ end of IgG1_HC or IgLC fragment (FIG.62 and Table 2). The PCMVand IgG1 / IgL constant fragments contains sequence tags to the VH / VLamplicons from the SBC for overlapping PCR to generate LECs. Table 2: Primers for second round of PCR of Single B cells and Phage Clones (Ref. 162; SEQ ID NOs: 19-70 in descending order).

[0002] Atty Dkt. No.155529.00024 Amplification of VH / VLfrom Monoclonal Phagemids: To obtain VH / VLDNA library from phage-derived clones, glycerol stocks of E. coli strains such as TG1, XL1-Blue, ER2738, HB2151 for harboring phagemids with unique Fab and scFv sequences with the reactivity of targets are re-arrayed into 96, 384, 1536-well suspension culture plates containing strain optimized media such as 2xYT media (Teknova, Y2140) in the case of TG1, and supplemented with 2% glucose and 100 μg / mL ampicillin using a QPix460 colony picker. These cultures are grown to confluence in appropriate shaker such as Innova44 shakers at 37°C at 220 RPM. To lyse the cells and release nucleic acid template for PCR amplification of human VH / VL, phage cultures are diluted 1:10 in sterile ultrapure water and incubated at −80°C for 2 hours. PCR reactions are first performed using sequence specific primers to generate primers to generate Atty Dkt. No.155529.00024 initial VH / VL amplicons. The reactions are prepared as follows; master mix of 10 μL / well contained 2 μL of the diluted bacterial culture, 5 μL of AmpliTaq master mix, 2.8 μL UltraPure Water and 0.1 μM each of sequence specific primers (Ref. 162). These amplicons are then spliced onto HaloTag sequence via Gibson Assembly, SOE PCR, Infusion cloning or other gene assembly technique through overlapping sequence at the 3’ end of Halo sequence and 5’ end of the VH / VLamplicons to generate HaloTag VH / VL. Next, PCR reaction is performed with primer sets as in Table 2 that incorporate PCMV and IgG1 / IgL constant fragments as sequence tags to the phagemids VH / VLamplicons, for generation of LEC. The reactions are prepared as follows; master mix of 10 μL / well contained 2 μL of Gibson Assembly reaction, 5 μL of AmpliTaq master mix, 2.8 μL UltraPure Water and 0.1 μM each of forward and reverse primers (Table 2; Ref. 162). The PCR condition include 95°C for 5 minutes followed by 35 cycles of 95°C for 30 seconds, 58°C for 30 seconds, 72°C for 1 minute, and a final elongation step at 72°C for 7 minutes. Purification and Validation of constructs: The amplified PCR products are visualized using 2% agarose 96-well E-gel (Thermo Fisher Scientific, G720802) with a 100-base pair (bp) ladder or other gel visualization methods, and purified using the enzymatic method with ExoSAP-IT (Thermo Fisher Scientific, 78200) for 1 minute at 37°C and 15 minutes at 85°C prior to storage at 4°C or −20°C (Ref.162). Excess primers from the PCR reaction are removed with ExoSAP-IT. The DNA concentrations of purified products are determined by nanodrop1000 spectrometer (ThermoFisher Scientific) and adjusted to an optimal concentration of 10 ng / μl for gene assembly (FIG. 63A-63B). Subsequent Sanger sequencing confirms the constructs. Sequence Assembly of VH / VLwith Plasmid-derived DNA fragments to form the LECs: VH and VL amplicons from both SBC and phage are amplified with tagged primers to allow addition of short DNA tag sequences to 5’ and 3’ ends of Ig VH / VLPCR products. These sequence tags were complementary to PCMV fragment or Ig constant region DNA fragments (Table 3) for creation of human IgG1 / κ or IgG1 / λ whole or Fab fragments, mouse IgG1 / κ constructs. Table 3: Primers for DNA fragments and overlapping PCR (Ref. 162; SEQ ID NOs: 71-86 in descending order). Atty Dkt. No.155529.00024 sequence (5’-ctctggctccctgataccaccggt- 3’ SEQ ID NO: 87) at the 3’ end of the PCMVfragment. The sequence tag at the 3’ end of VH / VL PCR products was designed to have one of the following five sequence tags depending on whether it was a mouse or human heavy / light chain LEC: 1. 22-nucleotide sequence (5’- gagttagtttgggcagcagatc-3’SEQ ID NO: 88) at the 3’end of VH which overlaps with 5’ end of muIgG1_CH for creating H_LEC coding for mouse IgG1; 2. 27 nucleotides sequence tag (5’- gaaagacagaaggtcccttggttgaag-3’ SEQ ID NO: 89) at the 3’ end of Ig VH PCR fragment complementary with 5’ end of huIgG1_CH / huIgG1_Fab fragment for the construction of H_LEC coding either a full-length or Fab human IgG1 heavy chains; 3. 21 nucleotide sequence (5’-ggatggtgggaagatggatac-3’ SEQ ID NO: 90) at the 3’end of VL PCR fragment complementary with 5’ end of muIgCκ.3’ for making L_LEC coding for Cκ chains; 4. 29 nucleotides sequence (5’- gataaacacagaaggggctgccacagtgc-3’ SEQ ID NO: 91) at the 3’ end of Ig Vκ PCR fragment complementary with 5’ end of huIgC for L_LEC coding for human Cκ; 5. 25 nucleotides sequence (5’-ggtgctgctttgggctggcctgcag-3’ SEQ ID NO: 92) at the 3’ end of Ig Vλ PCR fragment complementary with 5’ end of huIgCλ for building L_LEC coding Cλ. Using overlapping PCR or other gene assembly technique, such as Gibson assembly, InFusion cloning, these plasmid-derived fragments are assembled with purified amplicons of Ig VH and VL (Ref. 162). Using overlapping PCR, a 50 μl PCR reaction consisted of 5–20 ng of purified VH or VL amplicons, 1 unit of KOD Hot Start DNA polymerase (Millipore Sigma, Atty Dkt. No.155529.00024 71086), 10 μmol of dNTP, 50 μmol of MgSO4, 10 pmol of each primer, 10 ng of PCMV fragment, 10 ng of CHfragment or CLfragment (FIG. 63A-63B). CHfragments were used to assemble H_LECs for expression of mouse IgG1, huIgG1, or huIgG1_Fab (FIG.63A-63B). PCR cycles consisted of one cycle at 95°C for 2 minutes, 27 cycles of 95°C for 20 seconds, 62°C for 12 seconds, 70°C for 70 seconds, and a final elongation step at 70°C for 2 minutes. The assembled LECs are confirmed by agarose gels, and the LECs were purified using MinElute 96 UF PCR Purification Kit, or other linear DNA purification kit and eluted in 50 μl of ultrapure H2O. DNA concentration is determine using nanodrop1000 (Thermo Fisher Scientific) and adjusted to 100 ng / μl or optimal concentration ideal for in situ cell-free expression. DNA fragments required for the construction of heavy chain VHand light chain VLLECs are amplified from template plasmids pTT5-mIgG1H, pTT5-mIg kappa, pTT5- HuIgG1H, pTT5-HuIg kappa, pTT5-HuIg lambda, and pTT5-HuIgG1Fab (Sanofi, US). i. PCMV DNA fragment: consisting of CMV promoter and Ig leader sequence (human IgκV3 leader: MEAPAQLLFLLLLWLPDTTG; SEQ ID NO: 93) was amplified from plasmid pTT5-mIgG1H with primers F_CMV (5’-gaccgagcgcagcgagtc-3’; SEQ ID NO: 94), and R_CMV (5’-cggtggtatcagggagcc-3’; SEQ ID NO: 95) using AccuPrime™ Pfx DNA Polymerase (Thermo Fisher Scientific, 12344032). The PCMV fragment is occurs in both the VH-LEC and VL-LEC (FIG.63A-63B). ii. The DNA fragment containing mouse IgG1 constant region, stop codon and rabbit beta globin (rbGlob) poly (A) tail (muIgG1_CH fragment) was amplified from the plasmid, pTT5-mIgG1H, using the primers, muIgHC_F (5’- ggatctgctgcccaaactaactc-3’; SEQ ID NO: 96), and IgHLC_R (5’- tctccgagggatctcgacc-3’; SEQ ID NO: 97). iii. The DNA fragment of mouse Igκ constant region and rbGlob poly (A) tail (muIgCκ fragment) was amplified from the plasmid of pTT5_mIg kappa using the primers muIgκC_F (5’-caactgtatccatcttcccaccatc-3’; SEQ ID NO: 98), and IgHLC_R (5’- tctccgagggatctcgacc-3’; SEQ ID NO: 99). iv. The DNA fragment containing human IgG1 constant region or human IgG1 Fab constant region and rbGlob poly (A) tail (huIgG1_CH fragment or huIgG1_Fab fragment) were amplified from the plasmid of pTT5-huIgG1H or pTT5- huIgG1_Fab with the same primer pair, huIgHC_F (5’-cttcaaccaagggaccttctg-3’; SEQ ID NO: 100), and IgHLC_R (5’- tctccgagggatctcgacc-3’; SEQ ID NO: 101). v. The DNA fragment containing human Igκ constant region, stop codon and rbGlob poly (A) tail (huIgCκ fragment) was amplified from the plasmid of pTT5_HuIg Atty Dkt. No.155529.00024 kappa with the primers, huIgκC_F (5’-gcactgtggcagccccttctg-3’; SEQ ID NO: 102), and IgHL(k)C_R (5’- tctccgagggatctcgacc-3’; SEQ ID NO: 103). vi. The DNA fragment containing human Igλ constant region, stop codon and rbGlob poly (A) tail (huIgCλ fragment) was amplified from the plasmid of pTT5_HuIg Lambda with the primers huIgλC_F (5’-ctgcaggccagcccaaagcag-3’; SEQ ID NO: 104), and IgHLC_R (5’- tctccgagggatctcgacc-3’SEQ ID NO: 105). The PCR was carried out in a total volume of 50 μl with 1 unit of AccPrime Pfx DNA Polymerase, 5 μl of 10X Pfx AccuPrime PfxReaction mix, 1 ng of plasmid, 10 pmol of each primer at 95°C for 3 minutes followed by 27 cycles at 95°C for 17 seconds, 61°C for 30 seconds, 68°C for 80 seconds, and a final elongation step at 68°C for 2 minutes. The DNA fragments were purified by gel electrophoresis (ThermoFisher) and QIAquick Gel Extraction Kit (Qiagen). The primers F_CMV (5’-gaccgagcgcagcgagtc-3’; SEQ ID NO: 106) and IgHLC_R (5’- tctccgagggatctcgacc-3’; SEQ ID NO: 107) were also used for constructing VH / VL_LECs (Zhang et al., 2021). 2. SPOC DNA Library Preparation using Cell-free Expression optimized plasmids In a separate library preparation from the LEC approach above, SPOC antibody fragment DNA library can be prepared using any highly optimized mammalian, plant, insect, cell-free in vitro translation plasmids systems. This embodiment also includes preparation of linear expression fragments library using plasmid components such as 5’---T7 promoter sequence, IREV, start codon, signal peptide (if required), TEV site, capture Tag, MCS, stop codon, T7 terminator ---3’, etc. derived from highly optimized in vitro translation plasmid systems. The method for isolating and amplifying the Ig genes such as VH, VL from SBC and phage has been described above. Afterwards, the resulting antibody amplicon is further amplified to generate a construct for assembly with plasmid-derived DNA fragments. Using the pT7CFE1 plasmid as example (FIG.64), two DNA fragments will be amplified with PCR primers – (1) DNA fragment 1 (or leading fragment) will consist of T7 promoter sequence, IREV, start codon, signal peptide, capture tag, (2) DNA fragment 2 (tailing fragment) will consist of stop codon, terminator sequence. As alternative, DNA fragments 1 and 2 can be generated by custom DNA synthesis. The antibody amplicons from SBC and Phage clones are column-purified and used for another round of PCR, this time with Forward and Reverse primers that incorporate complementary sequence tags from 3’ end of DNA fragment 1 and 5’ end of DNA fragment for overlapping PCR extension. Once all three fragments have been Atty Dkt. No.155529.00024 obtained, sequence assembly is performed using overlapping PCR extension or other gene assembly method such as Gibson Assembly and InFusion cloning to obtain the linear expression fragments assembly is completed, consisting DNA fragment 1 + Antibody amplicon + DNA fragment 2. The assembled construct is verified by agarose gel, purified, and sequence validated ( FIG.9A-9D). Alternatively, antibody fragment amplicons from SBC and Phage can be directly cloned onto the multiple cloning site of circular plasmids using the commonly used restriction digestion method to generate recombinant plasmids library expressing antibody fragments as fusion tag proteins. In both the linear expression fragment and recombinant plasmid libraries, DNA concentrations are determined using nanodrop1000 (Thermo Fisher Scientific) and adjusted to 100 ng / μl or optimal concentration ideal for piezo printing, in situ cell-free expression in nanowells and subsequent capture on biosensor chip as described below (FIG. 65A-65B). 3. Printing of DNA constructs in silicon nanowell slide and cell-free production and capture on sensor chip Linear Expression Cassettes and circular plasmid DNA encoding rmAbs are printed in nanowell slides using the RainMaker3 system. The technique employs high-speed piezo printing with an au302 piezo dispense system, featuring an integrated alignment system designed for microwell, micrometer angular alignment fixture, look down camera, transfer arm, and a vacuum tray (Ref. 60). First, silicon nanowell substrates prepared for DNA printing by vapor-phase deposition of (3-Aminopropyl)-triethoxysilane (APTES) on the surface using an internally optimized protocol, were placed and aligned on the vacuum tray using the robotic transfer arm, micrometer angular alignment fixture, and look down camera. Print mix containing HaloTag chloro-alkane linker (O4) amine, which was varied from 0 to 1 mM (Promega, P6741), 1.46 mg / ml BS3 in DMSO, and 7.4 mg / mL BSA, in nuclease-free water was then printed in picoliter quantities “on-the-fly” using a multi-dispense head into each nanowell, followed immediately by printing of picoliter quantities of DNAs from the dispense plates. The addition of HaloTag chloro-alkane linker into the print mix was used for limited capture of expressed HaloTagged proteins at the bottom of nanowells for expression validation using anti-HaloTag antibody via immunofluorescence. After the piezo printing process, the nanowell slides were stored in an autodesiccator until they were ready for use (Ref.2). SPOC arrays were produced via automated IPC using a custom AutoCap instrument designed and built in-house. Nanowells printed with DNA plasmids of interest were filled via Atty Dkt. No.155529.00024 centrifugation in 50 mL conical tubes containing water for 4 min on each end at 1,400 x g, followed by incubation in Superblock (Thermofisher Scientific) for 30 min with gentle rocking. SPOC arrays were blocked for 30 min in 0.5 M ethanolamine, pH 8.5 to quench any free ester groups. After blocking, both the nanowell and SPOC slides were rinsed at least 3 times with water and dried gently with compressed air. Appropriate cell lysates such as Expi293F cell lysate in the case of VH / VLLECs described in FIG.63A-63B, or any other suitable cell lysate for antibody fragment expression was prepared and centrifuged for 20 min at 19,000 RCF at 4˚ C to remove particulates. The supernatant was gently removed to a fresh tube and stored at 4˚ C until injection into the instrument. The automated isolated protein capture instrument contains four stations, each one capable of holding one 25 mm x 75 mm nanowell slide. Each station contains a hydraulic actuator-driven base that holds a nanowell slide surrounded by a silicone gasket. A hinged lid designed to fit the SPOC arrays in a precise location above the nanowell slide is fitted onto the base of each station. Automated software was designed in- house using LabView to control all mechanical parts of the instrument. For protein expression and capture, the nanowell slide was placed on the base and the SPOC slide was mounted to the lid. The lid was then affixed firmly to the base using wingnuts and, using the automated software, low pressure (20 psi) was applied via the actuator, creating an airtight chamber containing the two slides with the SPOC array positioned immediately above the nanowell slide with a small gap in between. Following application of low pressure, the chamber and fluidic lines were vacuumed for a total of 12 min, with the goal of evacuating air from the nanowells and chamber. Using a primed 500 µL Hamilton syringe, the lysate was aspirated into the chamber via vacuum following automated valve switching from vacuum line to the injection line. This process results in the nanowells and the small gap between the slides being immediately filled with the lysate mixture. Once the slide space is filled (less than 10 sec), high pressure (220 psi) is applied via the actuator to tightly press-seal the two slides together. The instrument space was closed and brought to 30˚C for 2-4 h for in vitro translation and expression (IVTT), with the nanowells serving as nanoreactors for IVTT and the resulting HaloTagged protein being concurrently captured onto the SPOC array. Following IVTT, the pressure is removed, and the slides are immediately washed in cold 1X PBST 3 times prior to assay to remove any unbound IVTT components and incompletely expressed protein (Ref.2). Machine Learning Model Implementation Referring now to FIG.76, a flowchart is illustrated as setting forth the steps of an example method for generating classified feature data using a suitably trained machine learning Atty Dkt. No.155529.00024 model, which as described above may include an SVM, a CNN or other neural network, a GMM, a K-means clustering model, an autoencoder-based model (e.g., a GMVA model), a GAN-based model (e.g., a DASC model), and so on. As will be described, the machine learning model takes kinetic binding parameter data (e.g., kinetic trace signature or specific kinetic parameters extracted and / or determined from patient specific AAb binding profiles) as input data and generates classified feature data as output data. As an example, the classified feature data can be indicative of a diagnosis, stratification, and / or prognosis of one or more autoimmune diseases, including, but not limited to, systemic autoimmune rheumatic diseases (SARDs). The method includes accessing kinetic parameter data with a computer system, as indicated at step 902. Accessing the kinetic parameter data may include retrieving such data from a memory or other suitable data storage device or medium. Additionally or alternatively, accessing the kinetic parameter data may include acquiring such data using the methods described in the present disclosure and transferring or otherwise communicating the data to the computer system. A machine learning model is then accessed with the computer system, as indicated at step 904. As described above, in some implementation the machine learning model may include a pretrained machine learning model. Accessing the machine learning model may include accessing model parameters (e.g., weights, biases, or other suitable model parameters) that have been optimized or otherwise estimated by training the machine learning model on training data. In some instances, retrieving the machine learning model can also include retrieving, constructing, or otherwise accessing the particular model architecture to be implemented. For instance, data pertaining to the layers in a neural network architecture (e.g., number of layers, type of layers, ordering of layers, connections between layers, hyperparameters for layers) may be retrieved, selected, constructed, or otherwise accessed. In general, the machine learning model is trained, or has been trained, on training data in order to generate classified feature data from input kinetic binding parameter data, and optionally additional information as discussed above, where the classified feature data are indicative of a diagnosis, stratification, and / or prognosis of one or more autoimmune diseases, including, but not limited to, systemic autoimmune rheumatic diseases (SARDs). As described above, in some examples the machine learning model may be an artificial neural network, such as a CNN. An artificial neural network generally includes an input layer, one or more hidden layers (or nodes), and an output layer. Typically, the input layer includes as many nodes as inputs provided to the artificial neural network. The number (and the type) Atty Dkt. No.155529.00024 of inputs provided to the artificial neural network may vary based on the particular task for the artificial neural network. The input layer connects to one or more hidden layers. The number of hidden layers varies and may depend on the particular task for the artificial neural network. Additionally, each hidden layer may have a different number of nodes and may be connected to the next layer differently. For example, each node of the input layer may be connected to each node of the first hidden layer. The connection between each node of the input layer and each node of the first hidden layer may be assigned a weight parameter. Additionally, each node of the neural network may also be assigned a bias value. In some configurations, each node of the first hidden layer may not be connected to each node of the second hidden layer. That is, there may be some nodes of the first hidden layer that are not connected to all of the nodes of the second hidden layer. The connections between the nodes of the first hidden layers and the second hidden layers are each assigned different weight parameters. Each node of the hidden layer is generally associated with an activation function. The activation function defines how the hidden layer is to process the input received from the input layer or from a previous input or hidden layer. These activation functions may vary and be based on the type of task associated with the artificial neural network and also on the specific type of hidden layer implemented. Each hidden layer may perform a different function. For example, some hidden layers can be convolutional hidden layers which can, in some instances, reduce the dimensionality of the inputs. Other hidden layers can perform statistical functions such as max pooling, which may reduce a group of inputs to the maximum value; an averaging layer; batch normalization; and other such functions. In some of the hidden layers each node is connected to each node of the next hidden layer, which may be referred to then as dense layers. Some neural networks including more than, for example, three hidden layers may be considered deep neural networks. The last hidden layer in the artificial neural network is connected to the output layer. Similar to the input layer, the output layer typically has the same number of nodes as the possible outputs. In an example in which the artificial neural network generates classified feature data from kinetic parameter data, the output layer may include, for example, a number of different nodes, where each different node corresponds to a different diagnosis, stratification, and / or prognosis of one or more disease, such as autoimmune and related diseases, including, but not limited to, systemic autoimmune rheumatic diseases (SARDs). The kinetic binding parameter data are then input to the machine learning model, generating classified feature data as an output, as indicated at step 906. As one example, the classified feature data may indicate the probability for a particular classification. For instance, Atty Dkt. No.155529.00024 the classified feature data may indicate the probability that the kinetic parameter data include patterns, features, or characteristics indicative of detecting, differentiating, and / or determining the severity of one or more medical conditions, such as one or more disease, such as autoimmune and related diseases, including, but not limited to, systemic autoimmune rheumatic diseases (SARDs). Additionally or alternatively, the classified feature data may classify the kinetic parameter data as indicating a particular medical condition. In these instances, the classified feature data can differentiate between different medical conditions, such as one or more autoimmune and related diseases, including, but not limited to, systemic autoimmune rheumatic diseases (SARDs). The classified feature data may additionally or alternatively indicate a prognosis for the medical condition(s). In another example, the classified feature data can stratify one or more autoimmune and related diseases, including but not limited to one or more SARDs. In still other examples, the classified feature data may indicate a severity of a medical condition. For example, the classified feature data may include a severity score that quantifies a severity of a medical condition. The classified feature data generated by inputting the kinetic binding parameter data, and optionally additional information as discussed above, to the machine learning model can then be displayed to a user, stored for later use or further processing, or both, as indicated at step 908. Machine Learning Model Training Referring now to FIG. 77, a flowchart is illustrated as setting forth the steps of an example method for training one or more machine learning models on training data, such that the one or more machine learning models are trained to receive kinetic binding parameter data as input data in order to generate classified feature data as output data, where the classified feature data are indicative of a diagnosis, stratification, and / or prognosis of one or more disease, such as autoimmune and related diseases, including, but not limited to, systemic autoimmune rheumatic diseases (SARDs). In general, the machine learning model(s) can implement any number of different model architectures. For example, the machine learning model(s) may include an SVM, a neural network, a GMM, a K-means clustering model, an autoencoder-based model (e.g., a GMVA model), a GAN-based model (e.g., a DASC model), and so on. As described above, in some examples the machine learning model may be a neural network. In these instances, the neural network may implement a convolutional neural network, a residual neural network, or the like. Atty Dkt. No.155529.00024 The method includes accessing training data with a computer system, as indicated at step 1002. Accessing the training data may include retrieving such data from a memory or other suitable data storage device or medium. Alternatively, accessing the training data may include acquiring such data and transferring or otherwise communicating the data to the computer system. In general, the training data can include kinetic binding parameter data, such as kinetic trace signature or specific kinetic parameters extracted from patient specific AAb binding data. Additionally or alternatively, the training data may include additional data, such as identification of the isotype of the detected immunoglobulin autoantibodies, identification of specific antigens and / or biomolecules bound by the patient sample. The additional data or information can include the presence of one or more immunological co-factors and / or signaling molecules in the patient sample including but not limited to the presence of one or more cytokines, which may be detected via the use of one or analyte detection molecules as discussed above. In some examples, the training data may include kinetic parameter data that have been labeled (e.g., labeled as containing patterns, features, or characteristics indicative of one or more of a plurality of autoimmune diseases including, but not limited to, systemic autoimmune rheumatic diseases (SARDs); and the like). One or more machine learning models are trained on the training data, as indicated at step 1004. In general, the machine learning model can be trained by optimizing model parameters (e.g., weights, biases, or both) based on minimizing a loss function. As one non- limiting example, the loss function may be a mean squared error loss function. As a non-limiting example, training a machine learning model may include initializing the machine learning model, such as by computing, estimating, or otherwise selecting model network parameters (e.g., weights, biases, or both). During training, a machine learning model receives the inputs for a training example and generates an output based on the initialized model parameters (e.g., for a neural network using the bias for each node, and the connections between each node and the corresponding weights). For instance, training data can be input to the initialized machine learning model, generating output as classified feature data. The machine learning model then compares the generated output with the actual output of the training example in order to evaluate the quality of the machine learning model. For instance, the classified feature data can be passed to a loss function to compute an error. The current machine learning model can then be updated based on the calculated error (e.g., using backpropagation methods based on the calculated error). For instance, the current machine learning model can be updated by updating the model parameters (e.g., weights, biases, or both) in order to Atty Dkt. No.155529.00024 minimize the loss according to the loss function. The training continues until a training condition is met. The training condition may correspond to, for example, a predetermined number of training examples being used, a minimum accuracy threshold being reached during training and validation, a predetermined number of validation iterations being completed, and the like. When the training condition has been met (e.g., by determining whether an error threshold or other stopping criterion has been satisfied), the current machine learning model and its associated model parameters represent the trained machine learning model. Different types of training processes can be used to adjust the bias values and the weights of the node connections based on the training examples. The training processes may include, for example, gradient descent, Newton's method, conjugate gradient, quasi-Newton, Levenberg-Marquardt, among others. The machine learning model can be constructed or otherwise trained based on training data using one or more different learning techniques as described above, including but not limited to supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, ensemble learning, active learning, transfer learning, or other suitable learning techniques. The one or more trained machine learning models are then stored for later use, as indicated at step 1006. Storing the machine learning model(s) may include storing model parameters (e.g., weights, biases, or both), which have been computed or otherwise estimated by training the machine learning model(s) on the training data. Storing the machine learning model(s) may also include storing the particular model architecture to be implemented. For instance, data pertaining to the layers in a neural network architecture (e.g., number of layers, type of layers, ordering of layers, connections between layers, hyperparameters for layers) may be stored. Computer Systems FIG. 78 shows an example of a system 1100 for determining a diagnosis, prognosis, and / or stratification of one or more disease (e.g., autoimmune and related diseases, including, but no limited to, systemic autoimmune rheumatic diseases (SARDs)) in accordance with some examples described in the present disclosure. As shown in FIG. 78, a computing device 1150 can receive one or more types of data (e.g., kinetic binding parameter data) from data source 1102. In some embodiments, computing device 1150 can execute at least a portion of a detection system 1104 to detect the presence of and / or assess the prognosis for one or more disease from data received from the data source 1102. Atty Dkt. No.155529.00024 Additionally or alternatively, in some embodiments, the computing device 1150 can communicate information about data received from the data source 1102 to a server 1152 over a communication network 1154, which can execute at least a portion of the disease detection system 1104. In such embodiments, the server 1152 can return information to the computing device 1150 (and / or any other suitable computing device) indicative of an output of the disease detection system 1104. In some embodiments, computing device 1150 and / or server 1152 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, and so on. The computing device 1150 and / or server 1152 can also reconstruct images from the data. In some embodiments, data source 1102 can be any suitable source of data (e.g., measurement data, kinetic binding parameter data, and optionally additional data including isotype of autoantibodies, identification of antigens and / or biomolecules bound by components of the patient sample, and / or the presence of immunological co-factors and / or signaling molecules in the patient sample, such as cytokines), such as another computing device (e.g., a server storing measurement data, kinetic binding parameter data, and optionally additional data including isotype of autoantibodies, identification of antigens and / or biomolecules bound by components of the patient sample, and / or the presence of immunological co-factors and / or signaling molecules in the patient sample, such as cytokines), or the like. In some embodiments, data source 1102 can be local to computing device 1150. For example, data source 1102 can be incorporated with computing device 1150 (e.g., computing device 1150 can be configured as part of a device for measuring, recording, estimating, acquiring, or otherwise collecting or storing data). As another example, data source 1102 can be connected to computing device 1150 by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, data source 1102 can be located locally and / or remotely from computing device 1150, and can communicate data to computing device 1150 (and / or server 1152) via a communication network (e.g., communication network 1154). In some embodiments, communication network 1154 can be any suitable communication network or combination of communication networks. For example, communication network 1154 can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), other types of Atty Dkt. No.155529.00024 wireless network, a wired network, and so on. In some embodiments, communication network 1154 can be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-private network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Communications links shown in FIG.78 can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, and so on. Referring now to FIG.79, an example of hardware 1200 that can be used to implement data source 1102, computing device 1150, and server 1152 in accordance with some embodiments of the systems and methods described in the present disclosure is shown. As shown in FIG.79, in some embodiments, computing device 1150 can include a processor 1202, a display 1204, one or more inputs 1206, one or more communication systems 1208, and / or memory 1210. In some embodiments, processor 1202 can be any suitable hardware processor or combination of processors, such as a central processing unit (“CPU”), a graphics processing unit (“GPU”), and so on. In some embodiments, display 1204 can include any suitable display devices, such as a liquid crystal display (“LCD”) screen, a light-emitting diode (“LED”) display, an organic LED (“OLED”) display, an electrophoretic display (e.g., an “e-ink” display), a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 1206 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on. In some embodiments, communications systems 1208 can include any suitable hardware, firmware, and / or software for communicating information over communication network 1154 and / or any other suitable communication networks. For example, communications systems 1208 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 1208 can include hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on. In some embodiments, memory 1210 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 1202 to present content using display 1204, to communicate with server 1152 via communications system(s) 1208, and so on. Memory 1210 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 1210 can include random-access memory (“RAM”), read-only memory (“ROM”), Atty Dkt. No.155529.00024 electrically programmable ROM (“EPROM”), electrically erasable ROM (“EEPROM”), other forms of volatile memory, other forms of non-volatile memory, one or more forms of semi- volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 1210 can have encoded thereon, or otherwise stored therein, a computer program for controlling operation of computing device 1150. In such embodiments, processor 120...

Claims

Atty Dkt. No.155529.00024 CLAIMS We claim:

2. A system for detecting in a sample one or more analyte associated with one or more clinical feature or outcome, comprising: biosensor surface, the biosensor surface comprising an array of discrete locations, wherein a plurality of biomolecules is captured to the biosensor surface, wherein each discrete location comprises one biomolecule of the plurality of biomolecules captured to the biosensor surface, and an instrument for measuring analyte binding or interactions with biomolecules on the biosensor surface, wherein at least a portion of the plurality of biomolecules is generated on the biosensor surface from nucleic acid sequences using in vitro cell-free or cell-based expression system, wherein the plurality of biomolecules is associated with one or more clinical feature or outcome, and wherein the array of discrete locations comprises at least 10 discrete locations 3. The system of claim 1, wherein a portion of the plurality of biomolecules are purified and captured on the biosensor surface.

4. The system of claim 1, wherein a portion of the plurality of biomolecules is generated on the biosensor surface from nucleic acid sequences by in vitro transcription, in vitro translation, and in situ cell-free expression using a cell-free lysate.

5. The system of claim 1, wherein the plurality of biomolecules comprises one or more analyte detection molecule, one or more antigen, or both.

6. The system of claim 4, wherein the one or more analyte detection molecule and / or antigen comprises an antibody, antibody fragment, single chain variable region (scFv), nanobody, antibody mimetic, interacting protein, or a combination thereof.

7. The system of claim 4, wherein the one or more analyte detection molecule and / or antigen is adapted to bind to one or more analyte in a sample, wherein the analyte is selected from cytokines, chemokines, peptide, protein, protein complexes, serum factors, signaling molecules, nucleic acid, autoantibody, cellular component, vesicles, whole or partial cells, and combinations thereof.Atty Dkt. No.155529.00024 8. The system of claim 1, wherein the plurality of biomolecules comprises one or more of synthetic or natural nucleic acid, peptide, polypeptide, protein, protein domain, phospholipid, immunoglobulin-like protein, small molecule, micelle, liposome, nanoparticle, polymersome, mesoporous particle, dendrimer, and aptamer, or a combination thereof.

9. The method of claim 7, wherein the plurality of biomolecules comprises one or more antibody selected from monoclonal antibodies, polyclonal antibodies, scFvs, nanobodies, bispecific antibodies, neutralizing antibodies, antibody conjugates, recombinant antibodies, therapeutic antibodies, Fab fragments, Fv fragments, bivalent antibodies, minibodies, chimeric antibodies, tetrabodies, trimeric Fabs, V domains, C domains, fusion antibody, diabody, tandem antibody, and intrabody.

10. The system of claim 7, wherein the peptide is a cyclic citrullinated peptide (CCP).

11. The system of claim 1, wherein the nucleic acid is selected from DNA, RNA, ssDNA, dsDNA, and / or ssRNA.

12. The system of claim 1, wherein the biomolecule on the array contains one or more post-translational modification selected from glycosylation, phosphorylation, methylation, citrullination, sumolyation, disulfide bond formation, acetylation, lipidation, methylation, ubiquitination, S-nitrosylation, hydroxylation, amide formation, sulfation, myristoylation, deamidation, palmitoylation, carboxylation, formylation, O-linked glycosylation, S- palmitoylation, N-acetylation, N-myristoylation, and cysteine modification.

13. The system of claim 1, wherein the cell-free expression system is human cell-free lysate.

14. The system of claim 12, wherein the clinical feature is an autoimmune or related disease.

15. The system of claim 13, wherein the autoimmune or related disease comprises one or more Systemic Autoimmune Rheumatic Disease (SARD), Type I diabetes, Multiple Sclerosis, Systemic Sclerosis, Sjögren’s Syndrome, Rheumatoid Arthritis, Polymyositis, Systemic LupusAtty Dkt. No.155529.00024 Erythematosus, Addison’s Disease, Celiac Disease, Dermatomyositis, Graves Disease, Hashimotos Thyroiditis, Myasthenia Gravis, Pernicious Anemia, Reactive Arthritis, and combinations thereof.

16. The system of claim 6, wherein the analyte is a therapeutic agent or a target protein associated with one or more clinical feature, binding parameter or binding epitope, or outcome.

17. The system of claim 15, wherein the therapeutic agent is a protein ligand or a mutational variant of a protein ligand.

18. A method for detecting one or more analyte in a sample using a biomolecule array, comprising: capturing on a biosensor surface of the biomolecule array a plurality of biomolecules by spotting one or more of the plurality of biomolecules to a substrate, or generating at least a portion of the plurality of biomolecules on the substrate, wherein the portion of the plurality of biomolecules is generated from nucleic acid sequences using in vitro cell-free or cell based expression system, wherein the substrate is connected to the biosensor surface of the biomolecule array; exposing the sample to the biosensor surface of the biomolecule array, the biomolecule array comprising the plurality of biomolecules coupled to the biosensor surface at discrete locations, wherein each discrete location comprises one biomolecule of the plurality of biomolecules captured to the biosensor surface, and detecting one or more binding event involving at least a portion of the one or more analyte in the sample to a biomolecule.

19. The method of claim 17, wherein a portion of the plurality of biomolecules are purified and captured on the biosensor surface.

20. The method of claim 17, wherein a portion of the plurality of biomolecules is generated on the biosensor surface from nucleic acid sequences by in vitro transcription, in vitro translation, and in situ cell-free expression using a cell-free lysateAtty Dkt. No.155529.00024 21. The method of claim 17, wherein the one or more analyte in the sample is selected from cytokines, chemokines, peptide, protein, protein complexes, serum factors, signaling molecules, nucleic acid, antibody, cellular component, vesicles, whole or partial cells, and combinations thereof.

22. The method of claim 17, wherein the nucleic acid is selected from DNA, RNA, ssDNA, dsDNA, and / or ssRNA.

23. The method of claim 17, wherein the plurality of biomolecules comprises one or more of nucleic acids, peptide, polypeptides, protein, protein domain, phospholipid, immunoglobulin-like protein, small molecule, micelle, liposome, nanoparticle, polymersome, mesoporous particle, dendrimer, and aptamer, or a combination thereof.

24. The method of claim 20, wherein the plurality of biomolecules comprises one or more antibody selected from monoclonal antibodies, polyclonal antibodies, scFvs, nanobodies, bispecific antibodies, neutralizing antibodies, antibody conjugates, recombinant antibodies, therapeutic antibodies, Fab fragments, Fv fragments, bivalent antibodies, minibodies, chimeric antibodies, tetrabodies, trimeric Fabs, V domains, C domains, fusion antibody, diabody, tandem antibody, and intrabody.

25. The method of claim 20, wherein the peptide is a cyclic citrullinated peptide (CCP).

26. The method of claim 17, wherein detecting one or more binding event comprises utilizing a label-free detection technique.

27. The method of claim 17, further comprising, subsequent to the exposing a sample to a biosensor surface of a biomolecule array, exposing one or more secondary antibody or agent to the biosensor surface of the biomolecule array.

28. The method of claim 26, further comprising detecting one or more binding event of the one or more secondary antibody or agent to the one or more analyte bound to a biomolecule to identify an isotype of the one or more analyte.Atty Dkt. No.155529.00024 29. The method of claim 27, further comprising, subsequent to the exposing a sample to a biosensor surface of a biomolecule array and one or more secondary antibody, exposing one or more tertiary antibody or agent to the biosensor surface of the biomolecule array.

30. The method of claim 28, further comprising detecting one or more binding event of the one or more tertiary antibody or agent to the one or more analyte or the secondary antibody bound to a biomolecule to identify a specific isotype of the one or more analyte or secondary antibody.

31. The method of claim 27 or 29, wherein detecting one or more binding event identifiesthe isotype as IgM, IgG, IgE, and / or IgA and determines the isotype response to multiple infectious disease targets.

32. The method of claim 30, wherein the IgG is IgG1, IgG2, IgG3, or IgG4.

33. The method of claim 17, further comprising determining one or more kinetic parameter associated with the one or more binding event.

34. The method of claim 32, wherein the one or more kinetic parameter comprises a kinetic dissociation rate, a kinetic association rate, a kinetic affinity, a residence time, a binding half- life, a maximum binding signal, or a combination thereof.

35. The method of claim 32, wherein the plurality of biomolecules is captured under low density conditions.

36. The method of claim 34, wherein an avidity affect from a multi-valent analyte is reduced or eliminated thereby improving accuracy of kinetic binding rate.

37. The method of claim 32, further comprising using the one or more kinetic parameter as an input in one or more machine learning algorithm to identify at least one association between the one or more kinetic parameter and one or more clinical feature or therapeutic disease modifying feature.Atty Dkt. No.155529.00024 38. The method of claim 36, further comprising using at least one isotype of the one or more analyte as an input in the one or more machine learning algorithm.

39. The system of claim 17, further comprising determining specific biomolecule epitopes associated with the one or more binding event.

40. The method of claim 38, further comprising specific biomolecule binding epitopes as an input in one or more machine learning algorithm to identify at least one association between the one or more binding epitopes and one or more clinical feature or therapeutic disease modifying feature.

Images