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Reconciling qualitative, abstract, and scalable modeling of biological networks

Author

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  • Loïc Paulevé

    (Université Bordeaux, Bordeaux INP, CNRS, LaBRI, UMR5800
    Université Paris-Sud, CNRS, Université Paris-Saclay)

  • Juri Kolčák

    (CNRS (UMR 8643) and ENS Paris-Saclay, Université Paris-Saclay)

  • Thomas Chatain

    (CNRS (UMR 8643) and ENS Paris-Saclay, Université Paris-Saclay)

  • Stefan Haar

    (CNRS (UMR 8643) and ENS Paris-Saclay, Université Paris-Saclay)

Abstract

Predicting biological systems’ behaviors requires taking into account many molecular and genetic elements for which limited information is available past a global knowledge of their pairwise interactions. Logical modeling, notably with Boolean Networks (BNs), is a well-established approach that enables reasoning on the qualitative dynamics of networks. Several dynamical interpretations of BNs have been proposed. The synchronous and (fully) asynchronous ones are the most prominent, where the value of either all or only one component can change at each step. Here we prove that, besides being costly to analyze, these usual interpretations can preclude the prediction of certain behaviors observed in quantitative systems. We introduce an execution paradigm, the Most Permissive Boolean Networks (MPBNs), which offers the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.

Suggested Citation

  • Loïc Paulevé & Juri Kolčák & Thomas Chatain & Stefan Haar, 2020. "Reconciling qualitative, abstract, and scalable modeling of biological networks," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18112-5
    DOI: 10.1038/s41467-020-18112-5
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    Cited by:

    1. Martina Prugger & Lukas Einkemmer & Samantha P Beik & Perry T Wasdin & Leonard A Harris & Carlos F Lopez, 2021. "Unsupervised logic-based mechanism inference for network-driven biological processes," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-30, June.

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