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

  • 34 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.

LanguageEnglish (US)
Pages747-754
Number of pages8
JournalBioinformatics
Volume22
Issue number6
DOIs
StatePublished - Mar 15 2006

Profile

Space Simulation
Hidden Variables
Transcription factors
State-space Model
Transcription Factor
Transcription Factors
Gene Regulatory Networks
Gene Regulatory Network
Gene Expression Data
Genes
Gene Expression
Gene expression
Gene
Escherichia coli K12
Gene Networks
Cell Cycle
Feedback Loop
Saccharomyces Cerevisiae
Regulator Genes
Feedforward

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. 2006 ; Vol. 22, No. 6. pp. 747-754
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