A qualitative analysis of ubiquitous regulatory motifs in Saccharomyces cerevisiae genetic networks

    Research output: Contribution to journalArticle

    Abstract

    This work examines bistability and multistability within a Recurrent Neural Network model (RNN) for a 2-node and 3-node system under many different regulation scenarios. We determine parameter regions where there is bistability, multistability, or other stable modes in the expression states of the systems described by this network model. Our results show that although bistability can be generated with autoregulation it is also the case that both autorepression or no autoregulation can yield bistability as long as a sigmoidal behavior is present. Additionally, our results show the importance of considering more than a single connection when inferring a network as the observed biological result is averaged over many outcomes, which has implications for many algorithms that infer gene regulatory networks using the RNN models.

    Original languageEnglish (US)
    Pages (from-to)148-167
    Number of pages20
    JournalCommunications in Nonlinear Science and Numerical Simulation
    Volume69
    DOIs
    StatePublished - Apr 1 2019

    Fingerprint

    Genetic Network
    Bistability
    Saccharomyces Cerevisiae
    Qualitative Analysis
    Yeast
    Multistability
    Recurrent neural networks
    Recurrent Neural Networks
    Neural Network Model
    Gene Regulatory Network
    Vertex of a graph
    Network Model
    Genes
    Scenarios

    Keywords

    • Bifurcation
    • Gene network
    • Recurrent neural network

    ASJC Scopus subject areas

    • Numerical Analysis
    • Modeling and Simulation
    • Applied Mathematics

    Cite this

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    title = "A qualitative analysis of ubiquitous regulatory motifs in Saccharomyces cerevisiae genetic networks",
    abstract = "This work examines bistability and multistability within a Recurrent Neural Network model (RNN) for a 2-node and 3-node system under many different regulation scenarios. We determine parameter regions where there is bistability, multistability, or other stable modes in the expression states of the systems described by this network model. Our results show that although bistability can be generated with autoregulation it is also the case that both autorepression or no autoregulation can yield bistability as long as a sigmoidal behavior is present. Additionally, our results show the importance of considering more than a single connection when inferring a network as the observed biological result is averaged over many outcomes, which has implications for many algorithms that infer gene regulatory networks using the RNN models.",
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