{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["Saad C"],"pubmed_abstract":["AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools."],"journal":["Bioinformatics and biology insights"],"pagination":["1177932218818458"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6328952"],"repository":["biostudies-literature"],"pubmed_title":["AutoAnalyze in Systems Biology."],"pmcid":["PMC6328952"],"pubmed_authors":["Saad C","Li J","Bauer B","Mansmann UR"],"additional_accession":[]},"is_claimable":false,"name":"AutoAnalyze in Systems Biology.","description":"AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools.","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019","modification":"2022-02-09T09:49:57.922Z","creation":"2019-03-26T22:38:40Z"},"accession":"S-EPMC6328952","cross_references":{"pubmed":["30670917"],"doi":["10.1177/1177932218818458"]}}