Capturing dynamic relevance in Boolean networks using graph theoretical measures
Abstract Motivation 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...
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Published in | Bioinformatics (Oxford, England) Vol. 37; no. 20; pp. 3530 - 3537 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
25.10.2021
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Subjects | |
Online Access | Get full text |
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Summary: | Abstract
Motivation
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.
Results
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 with an accuracy of 75% in a set of 35 published logical models. Further analyses of the selected compounds’ connectivity unravelled a new class of not highly connected nodes with high impact on the networks’ dynamics, which we call gatekeepers. We validated our method’s working concept on logical models, which can be readily scaled up to complex interaction networks, where dynamic analyses are not even feasible.
Availability and implementation
Code is freely available at https://github.com/sysbio-bioinf/BNStatic.
Supplementary information
Supplementary data are available at Bioinformatics online. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Felix M Weidner, Julian D Schwab, Silke D Werle and Nensi Ikonomi authors wish it to be known that these authors contributed equally. |
ISSN: | 1367-4803 1367-4811 |
DOI: | 10.1093/bioinformatics/btab277 |