Identification of bifurcation transitions in biological regulatory networks using Answer-Set Programming

Background Numerous cellular differentiation processes can be captured using discrete qualitative models of biological regulatory networks. These models describe the temporal evolution of the state of the network subject to different competing transitions, potentially leading the system to different...

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Veröffentlicht in:Algorithms for molecular biology Jg. 12; H. 1; S. 19 - 14
Hauptverfasser: Fitime, Louis Fippo, Roux, Olivier, Guziolowski, Carito, Paulevé, Loïc
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 20.07.2017
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Bifurcations
Bioinformatics
Biological evolution
Biomedical and Life Sciences
Boolean algebra
Cellular and Medical Topics
Cellular communication
Computational Biology/Bioinformatics
Computer Science
Constraints in Bioinformatics
Differentiation (biology)
Formal Languages and Automata Theory
Genes
Life Sciences
Logic in Computer Science
Logic programming
Physiological
Semantics
Signal transduction
Static code analysis
Transportation networks
Biological Regulatory Networks
Complete finite Prefix
Bifurcation Transitions
Automata Networks
Bifurcation Identification
ISSN:1748-7188, 1748-7188
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Zusammenfassung:Background Numerous cellular differentiation processes can be captured using discrete qualitative models of biological regulatory networks. These models describe the temporal evolution of the state of the network subject to different competing transitions, potentially leading the system to different attractors. This paper focusses on the formal identification of states and transitions that are crucial for preserving or pre-empting the reachability of a given behaviour. Methods In the context of non-deterministic automata networks, we propose a static identification of so-called bifurcations, i.e., transitions after which a given goal is no longer reachable. Such transitions are naturally good candidates for controlling the occurrence of the goal, notably by modulating their propensity. Our method combines Answer-Set Programming with static analysis of reachability properties to provide an under-approximation of all the existing bifurcations. Results We illustrate our discrete bifurcation analysis on several models of biological systems, for which we identify transitions which impact the reachability of given long-term behaviour. In particular, we apply our implementation on a regulatory network among hundreds of biological species, supporting the scalability of our approach. Conclusions Our method allows a formal and scalable identification of transitions which are responsible for the lost of capability to reach a given state. It can be applied to any asynchronous automata networks, which encompass Boolean and multi-valued models. An implementation is provided as part of the Pint software, available at http://loicpauleve.name/pint .
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ISSN:1748-7188
1748-7188
DOI:10.1186/s13015-017-0110-3