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Computing signal transduction in signaling networks modeled as Boolean Networks, Petri Nets and hypergraphs

View ORCID ProfileLuis Sordo Vieira, View ORCID ProfilePaola Vera-Licona
doi: https://doi.org/10.1101/272344
Luis Sordo Vieira
1Center for Quantitative Medicine, UConn Health, Farmington CT.
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  • ORCID record for Luis Sordo Vieira
Paola Vera-Licona
2Center for Quantitative Medicine, Department of Pediatrics, Department of Cell Biology, Institute for Systems Genomics, UConn Health, Farmington CT. .
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  • ORCID record for Paola Vera-Licona
  • For correspondence: veralicona@uchc.edu
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Abstract

Mathematical frameworks circumventing the need of mechanistic detail exist to build models of signal transduction networks: graphs, hypergraphs, Boolean Networks, and Petri Nets. Predicting how a signal transduces in a signaling network is essential to understand cellular functions and disease. Different formalisms exist to describe how a signal transduces in a given intracellular signaling network represented in the aforementioned modeling frameworks: elementary signaling modes, T-invariants, extreme pathway analysis, elementary flux modes and simple paths. While these signal transduction formalisms are broadly used in their respective frameworks, few studies have been done emphasizing how these signal transduction methodologies compare or relate to each other.

We present an overview of how signal transduction networks have been modelled using graphs, hypergraphs, Boolean Networks, and Petri Nets in the literature. We provide a literary review of the different formalisms for capturing signal transduction in a given model of an intracellular signaling network. We also discuss the existing translations between the different modeling frameworks, and the relationships between their corresponding signal transduction representations that have been described in the literature. Furthermore, as a new formalism of signal transduction, we show how minimal functional routes proposed for signaling networks modeled as Boolean Networks can be captured by computing topological factories, a methodology found in the metabolic networks literature. We further show that in the case of an acyclic B-hypergraph, the definitions are equivalent. In directed graphs, it has been shown that computations of elementary modes via its incidence matrix correspond to computations of simple paths and feedback loops. We show that computing elementary modes based on the incidence matrix of a B-hypergraph fails to capture minimal functional routes.

  • Abbreviations

    MPS
    Minimal Path Set
    EMT
    Epithelial to Mesenchymal transition
    MFR
    Minimal Functional Route
    ESM
    Elementary Signaling Mode
    S-factory
    Stoichiometric factory
    T-factory
    Topological factory
    SF (TF)
    Stoichiometric (Topological) factory
    MSF (MTF)
    Minimal SF (TF)
    T-invariant
    Transition invariant
    P-invariant
    Place invariant
    scc
    Strongly Connected Component
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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    Posted July 10, 2018.
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    Computing signal transduction in signaling networks modeled as Boolean Networks, Petri Nets and hypergraphs
    Luis Sordo Vieira, Paola Vera-Licona
    bioRxiv 272344; doi: https://doi.org/10.1101/272344
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    Computing signal transduction in signaling networks modeled as Boolean Networks, Petri Nets and hypergraphs
    Luis Sordo Vieira, Paola Vera-Licona
    bioRxiv 272344; doi: https://doi.org/10.1101/272344

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