26 results about "Systems biology" patented technology

Provided herein are systems, devices, and methods for processing, analyzing, and classifying biological data sets and generation of cell profiles. The data sets may include multi-omic data. Some embodiments may include the use of machine learning in training a classifier of raw multi-omic data and incorporating system biology knowledge to understand cellular behavior and cell status at the biomolecular level.

The invention discloses a method for improving yield of branched chain aldehyde of lactococcus lactis based on regulation and control of a luxS gene and application, and belongs to the field of system biology. Gene function verification and genome scale metabolic network model analysis are creatively combined, the phenotypic effect of the luxS gene is confirmed, and more importantly, the internal action mechanism of the luxS gene is disclosed from the system level, that is, the whole metabolic network flow is influenced by regulating and controlling multiple key enzymes such as transaminase, decarboxylase, dehydrogenase and decarboxylase. The invention reveals that the quorum sensing core gene luxS has a new function of regulating and controlling synthesis of branched chain aldehyde in lactococcus lactis for the first time, and an intercellular communication system is directly linked with production of specific flavor metabolites. The invention provides a brand-new and higher-level regulation target and thought for producing flavor substances by microbial fermentation, and gets rid of the limitation that only metabolic pathway terminal enzyme is singly modified traditionally.

This invention belongs to the field of molecular breeding technology and provides a method based on... YIPF1 Molecular breeding methods and applications for inhibiting egg production decline in laying hens using the eQTL gene. This invention combines molecular markers with... YIPF1 Gene expression levels have a regulatory role; the combined molecular markers are located at Chr8:24801464 and Chr8:24802450 loci in the chicken genome, respectively. This invention breaks through the traditional single-trait research paradigm, integrating multidimensional omics and phenotypic data to identify genes from a systems biology perspective. YIPF1 The gene and its regulatory site combination, Chr8:24801464 and Chr8:24802450, serve as key targets in the process of tissue functional decline in laying hens. This gene possesses cross-tissue, dynamically correlated early warning characteristics, providing a reliable tool for early assessment and precise breeding of anti-aging traits in laying hens, and has significant application value for achieving the goal of an ultra-long laying cycle.

This invention discloses a method for constructing and analyzing a genome-scale metabolic network model of *Paragonimus denitrifyingus*, belonging to the field of systems biology. The method includes: whole-genome annotation; obtaining global metabolic response data of *Paragonimus denitrifyingus*; automatically retrieving genomic information and constructing Model 1 based on the species code and genome annotation results of *Paragonimus denitrifyingus* in the KEGG database; constructing Model 2 by identifying homologous proteins in the *Paragonimus denitrifyingus* genome through homology searching of proteins in the target organism based on a pre-trained Hidden Markov Model; and integrating Model 1 and Model 2. This invention allows for the efficient design and modification of denitrification engineering, achieving precise control of nitrogen degradation processes. Compared to existing metabolic engineering methods, this invention effectively reduces the workload of exploratory experiments and greatly advances a deeper understanding of the nitrogen degradation characteristics of *Paragonimus denitrifyingus*.

An ornithine decarboxylase gene related to Auricularia auricula-judae degradation and application thereof belong to the field of bioengineering technology. In order to solve the technical problem that there is lack of key functional genes and molecular targets which can be used for early identification, anti-degradation molecular breeding and strain rejuvenation due to continuous subculture leading to degradation in Auricularia auricula-judae industry, based on the G1, G10 and G20 mycelium transcriptome difference analysis of Auricularia auricula-judae strain Heiwei S1908, the ornithine decarboxylase gene g4162 significantly differentially expressed between normal mycelium and G20 degraded mycelium is screened, the molecular function of the gene in the degradation of Auricularia auricula-judae subculture is determined, and the system biology information prediction of the gene coding protein is completed, which provides core gene resources and theoretical basis for the degradation mechanism research, molecular detection kit development, anti-degradation germplasm screening and gene breeding of Auricularia auricula-judae.

The invention relates to the technical field of screening of pharmacodynamic substances of natural products, and particularly discloses a method for screening fulvic acid angiogenesis promoting core active components and key targets. According to the method, a first key target point set is obtained through system biological analysis, meanwhile, core active ingredients are screened in combination with priori knowledge, and independent target point re-prediction is conducted only on the basis of the active ingredient set to obtain a second key target point set. The method is characterized in that multi-dimensional convergence verification is carried out by comparing results of independent paths of the two methodologies, so that the false positive risk of a single path is overcome. 10 core active components such as lactic acid and 4 key targets such as PTGS2 are finally screened out, and the binding affinity is verified through molecular docking. According to the method, a technical closed loop of mutual independence-mutual verification is constructed, and objectivity and reliability of a screening result are ensured.

The application provides a traditional Chinese medicine classical prescription system for analyzing extracellular vesicles based on multi-omics integration, comprising: a data acquisition module for acquiring biopsy sample information of different groups of individuals based on related experiments and literature of traditional Chinese medicine classical prescription treatment; a data processing module for obtaining and preprocessing extracellular vesicle data and intestinal flora data based on the biopsy sample information; a multi-omics integration module for obtaining multi-omics integration data; a discovery and verification module for obtaining potential classical prescription efficacy evaluation biomarkers and experimentally verifying the potential classical prescription efficacy evaluation biomarkers to obtain verification results; and a systems biology application module for obtaining the regulation mechanism of extracellular vesicles in a biological system based on the construction of a gene-protein-metabolite interaction network. The application can comprehensively reveal the efficacy mechanism of traditional Chinese medicine classical prescriptions and provide strong technical support and application prospects for the modernization, scientification and internationalization development of traditional Chinese medicines.

The invention discloses a composition for enhancing memory and improving cognitive function and application. The composition disclosed by the invention comprises acanthopanax, ginseng, Chinese wolfberry fruits, gastrodia elata, spina date seeds, poria cocos, Chinese yams, lotus seeds, gordon euryale seeds, mulberries, longan pulp, fresh ginger, walnut powder, egg protein powder, blueberry powder, raspberry powder, pumpkin seed powder, oat powder, spirulina powder, dark chocolate powder, a vitamin B group compound, gamma-aminobutyric acid, galactooligosaccharide and citric acid. According to the invention, a multi-channel and multi-target collaborative network is constructed by taking acanthopanax senticosus as a core, through multiple channels such as nerve regeneration, energy metabolism, oxidative stress, signal transduction, endocrine regulation and the like, different nodes such as neurons, neuroglial cells, cerebrovascular endothelium and the like are targeted, the overall optimization of the memory function is realized on the system level, and the memory function is optimized. The core idea of systematic biology of overall regulation and control is reflected, and the composition prepared from the composition has the effects of improving memory, learning ability and cognitive ability.

The invention belongs to the field of bioengineering, and particularly relates to a construction method and application of a transgenic mouse for screening tissue cell membrane protein in vivo. Aiming at the problems of low in-vivo research flux, unstable library, limited tissue applicability and the like of the traditional membrane protein gene, sgRNA library plasmids are constructed by designing sgRNA of a membrane protein target gene and a control gene; three-positive-mouse containing Cas9, UBC-CreERT2 and sgRNA libraries is obtained through embryo microinjection and hybridization, genotype identification, inducer regulation and control, disease modeling, multi-tissue sample treatment and NGS analysis technical systems are established in a matched mode, and membrane protein gene in-vivo high-throughput screening is achieved. The model can guarantee library stability and screening reliability, covers multi-tissue research scenes, links in-vitro screening and in-vivo verification and is suitable for membrane protein function analysis and disease target mining, the technical process can be popularized to other gene families, and systematic biological research is promoted.

This invention relates to the field of biomedical technology, and particularly to protein assemblies, predictive models, and applications for predicting the incidence of chronic liver diseases based on proteomics. This invention integrates Olink proteomics technology, machine learning algorithms, and a discovery-internal validation research strategy to successfully screen and validate a set of predictive models for chronic liver diseases containing 10 plasma protein biomarkers (VCAM1, CD48, NUDT5, MILR1, TNFSF8, DDAH1, IL17RB, NFASC, FCN2, and RTN4R). Through the cross-integration of systems biology and artificial intelligence technologies, this invention develops a new approach to full-cycle prediction from early disease screening to dynamic risk monitoring, providing an innovative solution for the precise prevention and control of chronic liver diseases.

The invention discloses an AI-driven traditional Chinese medicine-glioma target point synergistic effect network prediction system, which comprises a multi-mode biomedical data acquisition and preprocessing module, an AI-driven traditional Chinese medicine-glioma target point network prediction module and a target point prediction module, the data acquisition module is used for acquiring heterogeneous biomedical data associated with traditional Chinese medicine components and biological activity thereof, glioma related targets and molecular characteristics thereof, and traditional Chinese medicine-target interaction from a plurality of data sources; and the multi-level biological network construction module is electrically connected with the multi-modal biomedical data acquisition and preprocessing module. The invention relates to the technical field of bioinformatics. According to the AI-driven traditional Chinese medicine-glioma target point synergistic effect network prediction system, massive heterogeneous biomedical data are effectively integrated and standardized through the multi-mode biomedical data acquisition and preprocessing module, the defects that a data source is single and complex association is difficult to capture in a traditional method are overcome, and the multi-level biological network construction module has the advantages of high efficiency, high reliability and the like. A multi-dimensional and multi-scale biological network is constructed from the perspective of system biology.

The invention belongs to the field of bioengineering, and particularly relates to a construction method and application of a transgenic mouse for in-vivo screening of a T cell membrane protein gene. Aiming at the problems of low in-vivo screening research efficiency, library instability, tissue applicability limitation and the like of traditional T cell membrane protein genes, sgRNA library plasmids are constructed by designing sgRNA of a T cell membrane protein target gene and a control gene; three positive mice containing Cas9, Ubc-CreERT2 and sgRNA libraries are obtained through embryo microinjection and hybridization, genotype identification, inducer regulation and control, disease modeling, multi-tissue sample treatment and NGS analysis technical systems are established in a matched mode, and in-vivo multi-tissue sample high-throughput screening of T cell membrane protein genes is achieved. The model can guarantee library stability and screening reliability, covers multi-tissue research scenes, links in-vitro screening and in-vivo verification and is suitable for T cell membrane protein gene function analysis and disease target mining, the technical process can be popularized to other gene families, and systematic biological research is promoted.

The application discloses a bacillus amyloliquefaciens genome-scale metabolic network model, a construction method and application, and belongs to the field of system biology. The method comprises the following steps: firstly, a rough model of bacillus amyloliquefaciens is automatically constructed according to protein sequencing results of bacillus amyloliquefaciens in a Uniport database; secondly, the rough model is manually refined and the synthesis reaction of acetoin is added according to a literature database, a metabolic pathway diagram and a biochemical database, so as to construct a genome-scale metabolic network model; thirdly, the genome-scale metabolic network model is converted into a computer-readable mathematical model; and finally, the mathematical model is analyzed by using a flux balance analysis method, and compared with experimental values in the literature. The method has the advantages of short construction time, simple operation and high accuracy, can systematically predict the nutritional conditions for the growth of bacillus amyloliquefaciens, and provides a feasible basis for improving the yield of acetoin and industrial production.

The application belongs to the field of biological medicine, and particularly relates to a functionalized magnetic microsphere of Raphani Semen components, a preparation method and application thereof. Based on photo-crosslinking technology, the application constructs multi-component modified magnetic microspheres, solves the problems existing in traditional single-component probes, and simultaneously fixes multiple active components in a traditional Chinese medicine complex system on the surface of the magnetic microspheres. On this basis, the application integrates high-throughput identification technology of chemical proteomics and cell function verification experiments, systematically captures and identifies a target group acted on by a complex component system of traditional Chinese medicine, constructs an overall regulation network of components, targets and pathways, and analyzes the action mechanism of traditional Chinese medicine from the system biology level.

The present application relates to the technical field of immune repertoire sequencing, and in particular to a sequence data analysis method and device for biological system state modeling and a storage medium. The present application obtains high-throughput sequencing data of B / T cell receptors, performs nonlinear gain correction and V-J gene combination preference feature extraction, constructs a structured feature node, encodes the features using a Transformer model, generates a system state vector by combining weighted average pooling, introduces adversarial training to realize feature decoupling, suppresses signal interference between functional categories, calculates a multi-dimensional immune intensity score based on the system state vector, and quantifies the activity level of immune responses of different functional categories. The method realizes high-sensitivity, multi-dimensional parallel modeling and dynamic evaluation of the immune system state, is suitable for large-scale population immune monitoring and systems biology research, and significantly improves the analysis capability and application value of immune repertoire data.

The invention belongs to the technical field of biological medicine, and discloses a method for analyzing an action target of artemisinin for treating renal clear cell carcinoma based on network pharmacology and a molecular docking technology, and the method comprises the following steps: S1, screening the action target of an artemisinin active component; s2, identifying candidate disease targets; s3, acquiring an intersection target point; s4, constructing a protein-protein interaction network and performing network topology analysis; s5, core target biological function and molecular action signal path analysis and visualization; s6, constructing an artemisinin-renal clear cell carcinoma-target spot-pathway network diagram; s7, exploring an action mechanism of artemisinin for improving the renal clear cell carcinoma based on a molecular docking technology; s8, carrying out survival analysis on the core target spot based on the TCGA database, and drawing a Kaplan-Meier curve; and S9, observing the influence of artemisinin and derivatives thereof on the activity of renal clear cell carcinoma 786-O cells by adopting a CCK-8 method. Network pharmacology and molecular docking technologies are applied, multiple omics data are integrated from the perspective of system biology, a drug-target-disease network is constructed, the interaction between artemisinin and renal clear cell carcinoma related targets is deeply analyzed, and the action mechanism of artemisinin on the aspects of genes, proteins, signal channels and the like is comprehensively revealed.

The invention relates to the technical field of bioinformatics, in particular to a method for constructing a gene network based on two-stage Granger causality and application, and the method comprises the following steps: firstly, carrying out global Granger causality test based on gene expression time sequence data, and constructing an initial candidate network containing all potential regulation and control relationships; and then, refining the candidate network by adopting a dynamic pruning strategy based on event driving. And before the target gene expression has significant fluctuation, detecting whether the candidate regulatory gene has synchronous significant expression change or not, and verifying the causal driving force of the candidate regulatory gene. And only the regulation and control relationship passing the verification is reserved, so that wrong causal association is effectively removed, and optimization of the network structure is realized. The network constructed by the invention better conforms to a real biological regulation rule, and can better reflect the dynamics and directivity of a regulation relationship in a biological system; the method is especially suitable for the research fields of plant stress response, development regulation and control, system biology and the like, and has wide application prospects and practical values.

The invention belongs to the technical field of system biology, and discloses a genome scale metabolic network model construction method, which comprises the following steps: constructing a biochemical reaction data set, a metabolite data set and a gene-protein-reaction rule data set; performing gene function annotation and screening; performing biochemical reaction mapping, constructing a biochemical reaction set, and adding related metabolite data set information; mapping and adding local biomass equations corresponding to similar species into a biochemical reaction set to generate an initial genome scale metabolic network model; and sequentially carrying out vacancy filling, redundant reaction removal and annotation information addition processing on the initial genome scale metabolic network model to obtain a final genome scale metabolic network model. According to the method, the defects of an existing GEMs construction tool in the aspects of model coverage and prediction precision are overcome, the automatic de novo construction process from the genome sequence to the GEMs is developed, the time-consuming manual correction process is reduced, and the construction efficiency is improved.

The invention discloses a spinosad synthetic strain metabolism model reconstruction method integrating protein structure comparison and application, and belongs to the field of system biology. The streptomyces albus mature GEMs are taken as a template, protein sequence comparison and structure comparison are comprehensively utilized, the gene (protein) homologous relationship of the streptomyces albus and the saccharopolyspora spinosa is established, and the high-quality saccharopolyspora spinosa GEMs are obtained by combining additional gene function annotation, gap filling and the like. The GEMs can systematically represent all metabolic enzyme functions of saccharopolyspora spinosa, accurate prediction from genotype to strain phenotype is realized, and systematic design research of saccharopolyspora spinosa cell factories is facilitated.

The application relates to the technical field of bioinformatics and system biology, and particularly relates to a path enrichment analysis method and device based on Moran's I. The method comprises the following steps: acquiring biological sample data, extracting molecular identifiers and corresponding expression values; mapping the molecular identifiers to nodes of a path network in a preset path database, and constructing a spatial weight matrix based on a topological structure of the path network; performing mask processing on nodes that are not mapped to expression data, and marking observation nodes through a mask vector; extracting a subgraph based on the mask vector, calculating global spatial autocorrelation of the path by using a global Moran's I statistic, evaluating significance by using a Monte Carlo permutation test, calculating a p value corresponding to the global Moran's I statistic, and determining an enrichment path list. By introducing the Moran's I index in spatial statistics, the application can comprehensively consider expression changes of molecules in a path and topological adjacent relationships, and can make up for defects of traditional enrichment analysis methods that cannot reflect upstream and downstream regulation relationships.

The invention relates to the technical field of biomedicine, and particularly discloses a central nervous system drug therapy mechanism analysis method based on system biological analysis. According to the invention, systematic biological analysis including transcriptomics, proteomics and receptor kinetics is adopted to research a therapeutic mechanism of a central nervous system drug. The combination of the three can be used for analyzing an action path through a whole chain of'gene transcription-protein function-receptor interaction ', so that the one-sidedness of a single technology is avoided, and a theoretical support is provided for system construction of central nervous system drug mechanism research. According to the invention, oxiracetam is taken as an example for explaining that the cognitive disorder improvement effect of the medicine is closely related to NMDA receptor related pathways and receptor functions through multi-omics analysis, then receptor kinetic analysis verifies that oxiracetam can inhibit NMDA receptor current and accelerate NMDA receptor desensitization, and finally inhibits NMDA receptor functions, so that the cognitive disorder improvement effect of the medicine is improved. Therefore, cognitive impairment is improved.

The application belongs to the field of biological information, and discloses a feature construction method of synthetic lethal gene combination based on miRNA-mRNA regulation relationship, comprising the following steps: acquiring isomiR sequences, quantifying the uncertainty of the isomiR sequences corresponding to the miRNA by using information entropy as the feature of the miRNA; acquiring the miRNA-mRNA regulation relationship, finding the miRNA having the regulation relationship with the synthetic lethal related genes, and calculating the weighted average value of the miRNA features by using the expression correlation coefficient as the weight; and the feature of the synthetic lethal gene combination is the average value of the calculation results of the two related genes. The application starts from the isomiR, integrates the regulation effect of the miRNA to describe the multi-molecule correlation, constructs the synthetic lethal gene combination feature with the system biology significance, and improves the biological explainability of the model when constructing the synthetic lethal gene combination prediction model.

The application discloses a drug interaction prediction method based on a substructure perception graph Transformer, and relates to the field of cross technology of artificial intelligence and bioinformatics. The method introduces an explicit chemical substructure and combines an attention bias mechanism of molecular topological information on the basis of a graph Transformer architecture by jointly using molecular topology, substructure patterns and drug biological attributes, further integrates multi-modal biological features such as drug-related targets, enzymes and pathways, integrates through a compact fusion scheme, strengthens the extraction of structure-driven signals related to drug-drug interaction prediction to better capture key local chemical semantics, and thus realizes the collaborative modeling of local chemical patterns of drug molecules and the system biology context, and improves the robustness and accuracy of drug-drug interaction discrimination in a complex environment.

The invention relates to the technical field of biological medicine, in particular to a proteome-based protein combination for chronic liver disease incidence prediction, a prediction model and application. According to the invention, an Olink proteomics technology, a machine learning algorithm and a discovery-internal verification research strategy are integrated, and a group of chronic liver disease prediction models containing 10 plasma protein markers (VCAM1, CD48, NUDT5, MILR1, TNFSF8, DDAH1, IL17RB, NFASC, FCN2 and RTN4R) are successfully screened and verified. Through cross fusion of system biology and artificial intelligence technology, a new thought of full-period prediction from early disease screening to dynamic risk monitoring is developed, and an innovative scheme is provided for accurate prevention and control of chronic liver diseases.

The cell cycle is a fundamental process in systems biology, and ordinary differential equations (ODEs) are important tools for describing cell cycle dynamics. A major challenge in this field is estimating unknown parameters of these equations from sparse biological data. Recently, a class of machine learning methods, namely Physical Information Neural Networks (PINNs), has been proposed to solve the ODE and corresponding parameter estimation problems. PINNs incorporate physical laws into the loss function of the neural network, and these losses are evaluated at discrete spatiotemporal locations called residual points. In this work, we propose a novel pointwise weighted invention to improve the accuracy of loss evaluation at these residual points, called Temporal Scaled Gradient Aggregation Self-Attention (TGSA). TGSA captures the internal dependencies between residuals by utilizing the temporal scale relationships between state variables and gradient information, assigning dynamic weights to residual points. Our experimental results demonstrate that TGSA outperforms TGSA in parameter estimation for two different cell cycle models.

The application relates to the field of artificial intelligence medicine and medical health technology, and proposes a prediction method and device for an anti-aging drug combination, equipment and a medium, wherein the method comprises the following steps: acquiring a personal multi-omics expression profile of a target drug use population, wherein the multi-omics comprises a genome, an RNA group, a protein group, a post-translational modification group and a metabolome; according to the personal multi-omics expression profile and a pre-trained multi-omics expression profile perturbation prediction model, the perturbation parameters of the target multi-omics corresponding to the target drug combination under different doses are predicted; and according to the perturbation parameters of the target multi-omics under each dose of the target drug combination, it is determined whether the target drug combination is a potential anti-aging drug combination and the corresponding target dose. Through the technical scheme, an effective anti-aging drug can be comprehensively, efficiently and accurately found from the perspective of systems biology.