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Journal Articles PLoS Computational Biology Year : 2022

Discrete modeling for integration and analysis of large-scale signaling networks

Abstract

The computation of sets of biological entities implicated in phenotypes is hampered by the complex nature of controllers acting in competitive or cooperative combinations. These biological mechanisms are underlied by chains of reactions involving interactions between biomolecules (DNA, RNA, proteins, lipids, complexes, etc.), all of which form complex networks. Hence, the identification of controllers relies on computational methods for dynamical systems, which require the biological information about the interactions to be translated into a formal language. The BioPAX standard is a reference ontology associated with a description language to describe biological mechanisms, which satisfies the Linked Open Data initiative recommendations for data interoperability. Although it has been widely adopted by the community to describe biological pathways, no computational method is able of studying the dynamics of the networks described in the BioPAX large-scale resources. To solve this issue, our Cadbiom framework was designed to automatically transcribe the biological systems knowledge of large-scale BioPAX networks into discrete models. The framework then identifies the trajectories that explain a biological phenotype (e.g., all the biomolecules that are activated to induce the expression of a gene).Here, we created Cadbiom models from three biological pathway databases (KEGG, PID and ACSN). The comparative analysis of these models highlighted the diversity of molecules in sets of biological entities that can explain a same phenotype. The application of our framework to the search of biomolecules regulating the epithelial-mesenchymal transition not only confirmed known pathways in the control of epithelial or mesenchymal cell markers but also highlighted new pathways for transient states
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hal-03693653 , version 1 (14-09-2023)

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Pierre Vignet, Jean Coquet, Sébastien Aubert, Matéo Boudet, Anne Siegel, et al.. Discrete modeling for integration and analysis of large-scale signaling networks. PLoS Computational Biology, 2022, 18 (6), pp.e1010175. ⟨10.1371/journal.pcbi.1010175⟩. ⟨hal-03693653⟩
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