160 results about "In silico" patented technology

“In silico” nucleic acid recombination methods, related integrated systems utilizing genetic operators and libraries made by in silico shuffling methods are provided.

The present invention relates to methods for achieving an optimal function of a biochemical reaction network. The methods can be performed in silico using a reconstruction of a biochemical reaction network of a cell and iterative optimization procedures. The methods can further include laboratory culturing steps to confirm and possibly expand the determinations made using the in silico methods, and to produce a cultured cell, or population of cells, with optimal functions. The current invention includes computer systems and computer products including computer-readable program code for performing the in silico steps of the invention.

Materials and methods for identifying unique sites in bacterial 16S and 23S rDNA are provided, as well as specific unique sequences of 16S rDNA in select bacteria. The distinguishing moieties will enable rapid differentiation between families, genera, groups, species, strains, subspecies, and isolates of microorganisms. Such differentiation can be performed by using rapid screening kits in combination with in silico analysis for diagnostic, prognastic, epidemiologic, phylogenetic, and other purposes.

The invention provides a non-naturally occurring microorganism comprising one or more gene disruptions encoding an enzyme associated with growth-coupled production of succinate when an activity of the enzyme is reduced, whereby the one or more gene disruptions confers stable growth-coupled production of succinate onto the non- naturally occurring microorganism. Also provided is a non-naturally occurring microorganism comprising a set of metabolic modifications obligatory coupling succinate production to growth of the microorganism, the set of metabolic modifications comprising disruption of one or more genes selected from the set of genes comprising: (a) adhE, ldhA; (b) adhE, ldhA, acka-pta; (c) pfl, ldhA; (d) pfl, ldhA, adhE; (e) acka-pta, pykF, atpF, sdhA; (f) acka-pta, pykF, ptsG, or (g) acka-pta, pykF, ptsG, adhE, ldhA, or an ortholog thereof, wherein the microorganism exhibits stable growth-coupled production of succinate. Additionally provided is a non-naturally occurring microorganism having the genes encoding the metabolic modification (e) acka-pta, pykF, atpF, sdhA that further includes disruption of at least one gene selected from pyka, atpH, sdhB or dhaKLM; a non-naturally occurring microorganism having the genes encoding the metabolic modification (f) ackA-pta, pykF, ptsG that further includes disruption of at least one gene selected from pykA or dhaKLM, or a non-naturally occurring microorganism having the genes encoding the metabolic modification (g) ackA-pta, pykF, ptsG, adhE, ldhA that further includes disruption of at least one gene selected from pykA or dhaKLM. The disruptions can be complete gene disruptions and the non-naturally occurring organisms can include a variety of prokaryotic or eukaryotic microorganisms. A method of producing a non-naturally occurring microorganism having stable growth-coupled production of succinate also is provided. The method includes: (a) identifying in silico a set of metabolic modifications requiring succinate production during exponential growth, and (b) genetically modifying a microorganism to contain the set of metabolic modifications requiring succinate production.

“In silico” nucleic acid recombination methods, related integrated systems utilizing genetic operators and libraries made by in silico shuffling methods are provided.

The invention provides a non-naturally occurring microorganism having one or more gene disruptions, the one or more gene disruptions occurring in genes encoding an enzyme obligatory coupling 3-hydroxypropionic acid production to growth of the microorganism when the gene disruption reduces an activity of the enzyme, whereby the one or more gene disruptions confers stable growth-coupled production of 3-hydroxypropionic acid onto the non-naturally occurring microorganism. Also provided is a non-naturally occurring microorganism comprising a set of metabolic modifications obligatory coupling 3-hydroxypropionic acid production to growth of the microorganism, the set of metabolic modifications having disruption of one or more genes including: (a) the set of genes selected from: (1) adhE, ldhA, pta-ackA; (2) adhE, ldhA, frdABCD; (3) adhE, ldhA, frdABCD, ptsG; (4) adhE, ldhA, frdABCD, pntAB; (5) adhE, ldhA, fumA, fumB, fumC; (6) adhE, ldhA, fumA, fumB, fumC, pntAB; (7) pflAB, ldhA, or (8) adhE, ldhA, pgi in a microorganism utilizing an anaerobic β-alanine 3-HP precursor pathway; (b) the set of genes selected from: (1) tpi, zwf; (2) tpi, ybhE; (3) tpi, gnd; (4) fpb, gapA; (5) pgi, edd, or (6) pgi, eda in a microorganism utilizing an aerobic glycerol 3-HP precursor pathway; (c) the set of genes selected from: (1) eno; (2) yibO; (3) eno, atpH, or other atp subunit, or (4) yibO, atpH, or other atp subunit, in a microorganism utilizing a glycerate 3-HP precursor pathway, or an ortholog thereof, wherein the microorganism exhibits stable growth-coupled production of 3-hydroxypropionic acid. The disruptions can be complete gene disruptions and the non-naturally occurring organisms can include a variety of prokaryotic or eukaryotic microorganisms. A method of producing a non-naturally occurring microorganism having stable growth-coupled production of 3-hydroxypropionic acid is further provided. The method includes: (a) identifying in silico a set of metabolic modifications requiring 3-hydroxypropionic acid production during exponential growth, and (b) genetically modifying a microorganism to contain the set of metabolic modifications requiring 3-hydroxypropionic acid production.

Pharmacophore models to be used in drug design and discovery are provided. An in silico protocol and in vitro assays are presented. Compounds and their pharmaceutically acceptable salts with HIV-1 integrase inhibitory and anti-HIV activity and use thereof in the treatment of HIV/AIDS and related infections either alone or in combination with all the known antiretroviral therapeutics are described.

A microfluidic device for culturing cells, termed a microscale cell culture analog (μCCA), is provided. The microfluidic device allows multiple cell or tissue types to be cultured in a physiologically relevant environment, facilitates high-throughput operation and can be used for drug discovery. The microfluidic device uses gravity-induced fluidic flow, eliminating the need for a pump and preventing formation of air bubbles. Reciprocating motion between a pair of connected reservoirs is used to effect the gravity-induced flow in microfluidic channels. Bacterial contamination is reduced and high throughput enabled by eliminating a pump. The microfluidic device integrates a pharmacokinetic-pharmacodynamic (PK-PD) model to enable PK-PD analyses on-chip. This combined in vitro/in silico system enables prediction of drug toxicity in a more realistic manner than conventional in vitro systems.

Methods are provided for designing and selecting antibodies against human antigens with high affinity and specificity in silico and in vitro. In some particular embodiments, methods are provided for designing and selecting humanized or fully human antibodies against vascular endothelial growth factor (VEGF) with high affinity and specificity. In another aspect of the invention, monoclonal antibodies against VEGF are provided. In particular, humanized or human anti-VEGF monoclonal antibodies are provided with ability to bind to human VEGF with high affinity, inhibit VEGF-induced proliferation of endothelial cells in vitro and inhibit VEGF-induced angiogenesis in vivo. These antibodies and their derivative can be used in a wide variety of applications such as diagnosis, prevention, and treatment of diseases such as cancer, AMD, diabetic retinopathy, and other diseases derived from pathological angiogenesis.

This invention is to use knowledge pattern learning and search system for selecting microorganisms to produce useful materials and to generate clean energy from wastes, wastewaters, biomass or from other inexpensive sources. The method starts with an in silico screening platform which involves multiple steps. First, the organisms' profiles are compiled by linking the massive genetic and chemical fingerprints in the metabolic and energy-generating biological pathways (e.g. codon usages, gene distributions in function categories, etc.) to the organisms' biological behaviors. Second, a machine learning and pattern recognition system is used to group the organism population into characteristic groups based on the profiles. Lastly, one or a group of microorganisms are selected based on profile match scores calculated from a defined metabolic efficiency measure, which, in term, is a prediction of a desired capability in real life based on an organism's profile. In the example of recovering clean energy from treating wastewaters from food process industries, domestic or municipal wastes, animal or meat-packing wastes, microorganisms' metabolic capabilities to digest organic matter and generate clean energy are assessed using the invention, and the most effective organisms in terms of waste reduction and energy generation are selected based on the content of a biowaste input and a desired clean energy output. By selecting a microorganism or consortia of multiply microorganisms using this method, one can clean the water and also directly generate electricity from Microbial Fuel Cells (MFC), or hydrogen, methane or other biogases from microorganism fermentation. In addition, using similar screening method, clean hydrogen can be recovered first from an anaerobic fermentation process accompanying the wastewater treatment, and the end products from the fermentation process can be fed into a Microbial Fuel Cell (MFC) process to generate clean electricity and at the same time treat the wastewater. The invention can be used to first select the hydrogenic microorganisms to efficiently generate hydrogen and to select electrogenic organisms to convert the wastes into electricity. This method can be used for converting wastes to one or more forms of renewable energies.

The present invention provides a methodology for efficiently generating and screening protein libraries for optimized proteins with desirable biological functions, such as improved binding affinity towards biologically and/or therapeutically important target molecules. The process is carried out computationally in a high throughput manner by mining the ever-expanding databases of protein sequences of all organisms, especially human. In one embodiment, a method for constructing a library of designed proteins, comprising the steps of: providing an amino acid sequence derived from a lead protein, the amino acid sequence being designated as a lead sequence; comparing the lead sequence with a plurality of tester protein sequences; and selecting from the plurality of tester protein sequences at least two peptide segments that have at least 15% sequence identity with the lead sequence, the selected peptide segments forming a hit library; and forming a library of designed proteins by substituting the lead sequence with the hit library. The library of designed proteins can be expressed in vitro or in vivo to produce a library of recombinant proteins that can be screened for novel or improved function(s) over the lead protein, such as an antibody against therapeutically important target.

The present invention provides methods for efficiently generating and screening protein libraries for optimal proteins with desired biological functions, such as improved binding affinity for biologically and/or therapeutically important target molecules. The method is performed in silico in a high-throughput fashion by mining the ever-expanding database of protein sequences in all living things, especially humans. In one embodiment, a method of constructing a designer protein library comprises the steps of: providing an amino acid sequence derived from a lead protein, referred to as a lead sequence; comparing the lead sequence with a plurality of test protein sequences; Select at least two peptide fragments having at least 15% sequence identity with the leader sequence from each test protein sequence, and the selected peptide fragments form a selection library; a library of designed proteins is formed by replacing the leader sequence with the selection library. Libraries of designed proteins can be expressed in vitro or in vivo to generate libraries of recombinant proteins that can be screened for new or improved functions relative to lead proteins, such as antibodies against a therapeutically important target.

Described herein are methods and systems to measure dynamics of disease progression, including cancer growth and response, at multiple scales by multiple techniques on the same biologic system. Methods and systems according to the invention permit personalized virtual disease models. Moreover, the invention allows for the integration of previously unconnected data points into an in silico disease model, providing for the prediction of disease progression with and without therapeutic intervention.

Featured are methods for assessing hemostatic risk including the risk for ACS. Such methods include acquiring blood/plasma composition based on a biological sample obtained from a subject, determining parameters associated with blood clotting, simulating in silico blood clotting using the determined parameters and comparing the results of such simulation to a reference and to determine the hemostatic risk from said comparing. In further embodiments, such methods further include selecting a treatment regime or protocol based on the results of such comparing. In yet further embodiments, such methods further include assessing the efficacy of medicants, drugs and the like of a given treatment protocol such as by simulating in silico the application of such medicants, drugs and the like.

The present invention discloses a method for rapid assessment of lung cancer therapy efficacy in a few days instead of weeks by conventional imaging methods. This method can also be used to detect relapse of the cancer and to improve the current TNM cancer staging method for more accurate prognosis. The rapid assessment of therapy efficacy is based on detecting circulating cancer cells in body fluid with high positive detection rate. The high positive detection rate is achieved by using PCR amplification of multiple marker genes identified by in silico search of DNA sequence database. This invention also discloses a scoring method to calculate the cancer cell load based on PCR results to correlate the amount of circulating cancer cells in lung cancer patients with their treatment outcomes.

Embodiments describe computer systems and computer programs for implementing BioCAD methods comprising one or more data models and one or more BioCAD tools, wherein the BioCAD tools enable users to design or refactor a biomolecule or to conduct a biological experiment in silico by user input of one or more components selected by the user from a database populated with information on components and scientific data of existing biomolecules and experiments. Data models of the programs are operable to manage development of the new biomolecule or experiment based on information in the databases. Computer programs also provide an output with information that enables the users to determine in silico if the newly designed or refactored molecule or biological experiment is satisfactory or not for its intended purpose in vitro and also provides the user the capability to re-design the biomolecule or experiment till it is satisfactory.

The present invention is related to a method for improving a strain on the basis of in silico analysis, in which it compares the genomic information of a target strain for producing a useful substance to the genomic information of a strain overproducing the useful substance so as to primarily screen genes unnecessary for the overproduction of the useful substance, and then to secondarily screen genes to be deleted through performing simulation with metabolic flux analysis. According to the present invention, an improved strain can be effectively constructed by the metabolic and genetic engineering approach comprising comparatively analyzing the genomic information of a target strain for producing a useful substance and the genomic information of a strain producing a large amount of the useful substance to screen candidate genes and performing in silico simulation on the screened candidate genes to select a combination of genes to be deleted, which shows an improvement in the production of the useful substance. Accordingly, the time, effort and cost required for an actual wet test can be significantly reduced.

This invention relates to methods and systems for in silico or bioinformatic modeling of cellular metabolism. The invention includes methods and systems for modeling cellular metabolism of an organism, comprising constructing a flux balance analysis model, and applying constraints to the flux balance analysis model, the constraints selected from the set consisting of: qualitative kinetic information constraints, qualitative regulatory information constraints, and differential DNA microarray experimental data constraints. In addition, the present invention provides for computational procedures for solving metabolic problems.

The present invention provides a set of in vitro and in silico methodologies for predicting in vivo pharmacokinetics and pharmacodynamics of multiple components; the methodologies comprise mathematical models for solving multiple unknowns which are linearly independent and/or interacting with each other. The present invention can be applied to develop phytomedicines which contain multiple active ingredients without prior identification, isolation and purification of these components.

An electronic system is provided that simulates a glucose-insulin metabolic system of a T2DM or prediabetic subject, wherein the system includes a subsystem that models dynamic glucose concentration in a T2DM or prediabetic subject, including an electronic module that models endogenous glucose production (EGP(t)), or meal glucose rate of appearance (Ra(t>>, or glucose utilization (U(t)), or renal excretion of glucose (B(t)), a subsystem that models dynamic insulin concentration in said T2DM or prediabetic subject, including an electronic module that models insulin secretion (S(t)), an electronic database containing a population of virtual T2DM or prediabetic subjects, each virtual subject having a plurality of metabolic parameters, and a processing module that calculates an effect of variation of at least one metabolic parameter value on the glucose insulin metabolic system of a virtual subject by inputting the plurality of metabolic parameter values.

The present invention reveals a method for developing agent-based simulation systems that are capable for the modeling and simulation of a systematic and analytical approach to a host-pathogen interaction. This methodology may enable users to explore “what if” scenarios and develop predictive data that may be used in experimental design and the testing of hypotheses of disease states. Using an in silico testing environment, the in silico approach helps combine knowledge of disease states that may be obtained from various sources, such as in vitro studies and in vivo studies, without requiring users to perform all necessary experimentation.

This patent application claims processes and compositions of matter that enable the discovery of single nucleotide polymorphisms (SNPs) that distinguish the genomes of two individual organisms in the same species, as well as that distinguish the paternal and maternal genetic inheritance of a single individual, as well as distinguish the genomes of cells in special tissues (e.g. cancer tissues) within an individual from the genomes of the standard cells in the same individuals, as well as the SNPs that are discovered using these processes and compositions. Two steps are essential to the invention disclosed in this application. The first step provides four sets of primers, which are designated “T-extendable”, “A-extendable”, “C-extendable”, and “G-extendable”. These primers, when targeted against a reference genome as a template, add (respectively) T, A, C, and G to their 3′-ends in a template-directed primer extension reaction. The second step presents these four primer sets, separately, to a sample of the target genome DNA under conditions where they bind to their complementary segments within the target DNA. Once bound, members of each primer set serve as primers for a template-directed primer extension reaction using the target genome as the template. If the template from the target genome presents the same templating nucleotide for the first nucleotide added in the extension reaction as the reference genome, then the T-extendable, A-extendable, C-extendable, and G-extendable primers will be extended (respectively) by T, A, C, and G. If, however, the template from the target genome presents a nucleotide different from the reference genome, then the T-extendable, A-extendable, C-extendable, and G-extendable primers will be extended (respectively) by not T, not A, not C, and not G (referred to here as “3N” or “3”, to indicate the other three nucleotides, where which of the other three is understood by context). In these cases, the primers have discovered a SNP, a difference between the target and reference genomes. Then, the T-extendable, A-extendable, C-extendable, and G-extendable primers that add (respectively) not-T, not-A, not-C, and not-G are separated or made otherwise physically distinct (through, for example, the use of irreversible terminators, such as 2′,3′-dideoxynucleosides) from those that added T, A, C, and G (respectively). Those that added T, A, C, and G (respectively) did not discover a SNP, and are discarded. The primers that added “not-T”, “not-A”, “not-C”, and “not-G” discovered a SNP, and presented in a mixture enriched (relative to those primers that did not discover a SNP) in a useful deliverable. Following these steps, the SNPs discoveries are realized by sequencing the extracted species. The information obtained from this sequencing allows the identification of the locus of the SNP in the in silico genome.

The present disclosure provides compositions and methods for the generation of an antibody or immunogenic composition, such as a vaccine, through epitope focusing by variable effective antigen surface concentration. Generally, the composition and methods of the disclosure comprise three steps: a “design process” comprising one or more in silico bioinformatics steps to select and generate a library of potential antigens for use in the immunogenic composition; a “formulation process”, comprising in vitro testing of potential antigens, using various biochemical assays, and further combining two or more antigens to generate one or more immunogenic compositions; and an “administering” step, whereby the immunogenic composition is administered to a host animal, immune cell, subject or patient. Further steps may also be included, such as the isolation and production of antibodies raised by host immune response to the immunogenic composition.

The attached disclosure provides a systems approach based on mathematical modelling of the kinetics of essential biochemical pathways involved in organ homeostasis. When this in silico model is coupled with in-vitro and/or in-vivo measurements to quantify drug-induced perturbations, a powerful platform that allows accurate and mechanistic-level prediction of drug-induced organ injury can be generated. The method described in this disclosure demonstrates that several physiological situations can also be accurately modelled in addition to the effect of perturbations induced by drugs. It can also be used along with high-throughput “omics” data to generate testable hypotheses leading to informed decision-making in drug development.