Capturing dynamic relevance in Boolean networks using graph theoretical measures

• DOI: 10.1093/bioinformatics/btab277
• Publication Date: 2021-04-23T00:51:50Z
• Authors: Felix M Weidner; Julian D Schwab; Silke D Werle; Nensi Ikonomi; Ludwig Lausser; Hans A Kestler
• Keywords: Relevance

Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that based on interaction allow investigate the dynamics of biological systems. However, since dynamic complexity these grows exponentially with their size, exhaustive analyses and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach identify dynamically relevant static network topology.Here, present a method only properties influencing nodes. Coupling vertex betweenness determinative power, could capture nodes for changing accuracy 75% in set 35 published logical models. Further selected compounds' connectivity unravelled new class not highly connected high impact networks' dynamics, which call gatekeepers. We validated our method's working concept models, can be readily scaled up complex networks, where even feasible.Code is freely available at https://github.com/sysbio-bioinf/BNStatic.Supplementary data Bioinformatics online.