{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["10(1)"],"submitter":["Cole J"],"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"],"journal":["NPJ systems biology and applications"],"pagination":["78"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11271576"],"repository":["biostudies-literature"],"pubmed_title":["Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations."],"pmcid":["PMC11271576"],"pubmed_authors":["Cole J"],"additional_accession":[]},"is_claimable":false,"name":"Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations.","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","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2026-06-01T10:01:13.646Z","creation":"2025-04-19T13:12:14.915Z"},"accession":"S-EPMC11271576","cross_references":{"pubmed":["39030258"],"doi":["10.1038/s41540-024-00404-x"]}}