<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>10(1)</volume><submitter>Cole J</submitter><pubmed_abstract>Biological signal transduction networks are central to information processing and regulation of gene expression across all domains of life. Dysregulation is known to cause a wide array of diseases, including cancers. Here I introduce self-consistent signal transduction analysis, which utilizes genome-scale -omics data (specifically transcriptomics and/or proteomics) in order to predict the flow of information through these networks in an individualized manner. I apply the method to the study of endocrine therapy in breast cancer patients, and show that drugs that inhibit estrogen receptor α elicit a wide array of antitumoral effects, and that their most clinically-impactful ones are through the modulation of proliferative signals that control the genes GREB1, HK1, AKT1, MAPK1, AKT2, and NQ</pubmed_abstract><journal>NPJ systems biology and applications</journal><pagination>78</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11271576</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations.</pubmed_title><pmcid>PMC11271576</pmcid><pubmed_authors>Cole J</pubmed_authors></additional><is_claimable>false</is_claimable><name>Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations.</name><description>Biological signal transduction networks are central to information processing and regulation of gene expression across all domains of life. Dysregulation is known to cause a wide array of diseases, including cancers. Here I introduce self-consistent signal transduction analysis, which utilizes genome-scale -omics data (specifically transcriptomics and/or proteomics) in order to predict the flow of information through these networks in an individualized manner. I apply the method to the study of endocrine therapy in breast cancer patients, and show that drugs that inhibit estrogen receptor α elicit a wide array of antitumoral effects, and that their most clinically-impactful ones are through the modulation of proliferative signals that control the genes GREB1, HK1, AKT1, MAPK1, AKT2, and NQ</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Jul</publication><modification>2026-06-01T10:01:13.646Z</modification><creation>2025-04-19T13:12:14.915Z</creation></dates><accession>S-EPMC11271576</accession><cross_references><pubmed>39030258</pubmed><doi>10.1038/s41540-024-00404-x</doi></cross_references></HashMap>