Using a state-space model with hidden variables to infer transcription factor activities

Zheng Li, Stephen M. Shaw, Matthew J. Yedwabnick, Christina Chan

    Research output: Contribution to journalArticle

    • 32 Citations

    Abstract

    Motivation: In a gene regulatory network, genes are typically regulated by transcription factors (TFs). Transcription factor activity (TFA) is more difficult to measure than gene expression levels are. Other models have extracted information about TFA from gene expression data, but without explicitly modeling feedback from the genes. We present a state-space model (SSM) with hidden variables. The hidden variables include regulatory motifs in the gene network, such as feedback loops and auto-regulation, making SSM a useful complement to existing models. Results: A gene regulatory network incorporating, for example, feed-forward loops, auto-regulation and multiple-inputs was constructed with an SSM model. First, the gene expression data were simulated by SSM and used to infer the TFAs. The ability of SSM to infer TFAs was evaluated by comparing the profiles of the inferred and simulated TFAs. Second, SSM was applied to gene expression data obtained from Escherichia coli K12 undergoing a carbon source transition and from the Saccharomyces cerevisiae cell cycle. The inferred activity profile for each TF was validated either by measurement or by activity information from the literature. The SSM model provides a probabilistic framework to simulate gene regulatory networks and to infer activity profiles of hidden variables.

    Original languageEnglish (US)
    Pages (from-to)747-754
    Number of pages8
    JournalBioinformatics
    Volume22
    Issue number6
    DOIs
    StatePublished - Mar 15 2006

    Profile

    State-space model
    Space Simulation
    Transcription Factors
    Anthralin
    Transcription factor
    Gene Regulatory Networks
    Gene Expression
    Hidden variables
    Model
    Cyclic AMP Receptor Protein
    Genes
    Transcription factors
    Gene regulatory network
    Gene expression data
    Gene expression
    Gene
    Feedback
    Escherichia coli K12
    Regulator Genes
    Saccharomyces cerevisiae

    ASJC Scopus subject areas

    • Clinical Biochemistry
    • Computer Science Applications
    • Computational Theory and Mathematics

    Cite this

    Using a state-space model with hidden variables to infer transcription factor activities. / Li, Zheng; Shaw, Stephen M.; Yedwabnick, Matthew J.; Chan, Christina.

    In: Bioinformatics, Vol. 22, No. 6, 15.03.2006, p. 747-754.

    Research output: Contribution to journalArticle

    Li, Zheng; Shaw, Stephen M.; Yedwabnick, Matthew J.; Chan, Christina / Using a state-space model with hidden variables to infer transcription factor activities.

    In: Bioinformatics, Vol. 22, No. 6, 15.03.2006, p. 747-754.

    Research output: Contribution to journalArticle

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    abstract = "Motivation: In a gene regulatory network, genes are typically regulated by transcription factors (TFs). Transcription factor activity (TFA) is more difficult to measure than gene expression levels are. Other models have extracted information about TFA from gene expression data, but without explicitly modeling feedback from the genes. We present a state-space model (SSM) with hidden variables. The hidden variables include regulatory motifs in the gene network, such as feedback loops and auto-regulation, making SSM a useful complement to existing models. Results: A gene regulatory network incorporating, for example, feed-forward loops, auto-regulation and multiple-inputs was constructed with an SSM model. First, the gene expression data were simulated by SSM and used to infer the TFAs. The ability of SSM to infer TFAs was evaluated by comparing the profiles of the inferred and simulated TFAs. Second, SSM was applied to gene expression data obtained from Escherichia coli K12 undergoing a carbon source transition and from the Saccharomyces cerevisiae cell cycle. The inferred activity profile for each TF was validated either by measurement or by activity information from the literature. The SSM model provides a probabilistic framework to simulate gene regulatory networks and to infer activity profiles of hidden variables.",
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