{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Weidner FM"],"funding":["TRANSCAN VI-PMTR-pNET","TRANSCAN VI—PMTR-pNET","German Science Foundation","German Federal Ministry of Education and Research"],"pagination":["3530-3537"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8545349"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["37(20)"],"pubmed_abstract":["<h4>Motivation</h4>Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that are based on interaction graphs allow to investigate the dynamics of biological systems. However, since dynamic complexity of these models grows exponentially with their size, exhaustive analyses of the dynamics and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach to identify dynamically relevant compounds based on the static network topology.<h4>Results</h4>Here, we present a method only based on static properties to identify dynamically influencing nodes. Coupling vertex betweenness and determinative power, we could capture relevant nodes for changing dynamics wi"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["Capturing dynamic relevance in Boolean networks using graph theoretical measures."],"pmcid":["PMC8545349"],"funding_grant_id":["217328187","SFB 1074","01ZX1708C","01KT1901B","288342734"],"pubmed_authors":["Werle SD","Ikonomi N","Lausser L","Kestler HA","Schwab JD","Weidner FM"],"additional_accession":[]},"is_claimable":false,"name":"Capturing dynamic relevance in Boolean networks using graph theoretical measures.","description":"<h4>Motivation</h4>Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that are based on interaction graphs allow to investigate the dynamics of biological systems. However, since dynamic complexity of these models grows exponentially with their size, exhaustive analyses of the dynamics and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach to identify dynamically relevant compounds based on the static network topology.<h4>Results</h4>Here, we present a method only based on static properties to identify dynamically influencing nodes. Coupling vertex betweenness and determinative power, we could capture relevant nodes for changing dynamics wi","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2025-04-26T16:51:59Z","creation":"2025-04-06T15:21:04.419Z"},"accession":"S-EPMC8545349","cross_references":{"pubmed":["33983406"],"doi":["10.1093/bioinformatics/btab277"]}}