Inferring transcription factor activities with a probabilistic framework of Hidden Markov Model

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

Research output: ResearchConference contribution

Abstract

Recent advances in high throughput technologies have generated a tremendous amount of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different levels of cellular response to environmental factors and contain within it information about the underlying regulatory network structure. Coupling genome-wide protein-DNA and protein-protein interaction information with gene expression data permits the reconstruction of gene regulatory [1] and signal networks [2]. In a gene regulatory network, genes are typically regulated by transcription factors. The activity of transcription factors, i.e. the fraction of transcription factor that binds to DNA is more difficult to measure as compared to gene expression. Models such as Network component analysis (NCA) have been applied to extract this information from expression data [3,4]. However, these approaches are limited by the types of network motifs that may be incorporated in the analysis. In order to overcome this limitation, we present a Hidden Markov Model (HMM) that represents gene networks with many different regulatory motifs, such as feed-back and autoregulation, making HMM a useful complement to existing approaches. In this paper, a gene regulatory network incorporating motifs such as feed-forward, auto-regulation, and multiple input was constructed as well as simulated with an HMM model. The simulated gene expression data was used to infer the transcription factor activities. The ability of HMM to infer transcription factor activities was evaluated by comparing the inferred and simulated transcription factor activity profiles. Second, HMM was applied to gene expression data obtained from E. Coli and Saccharomyces cerevisiae cell cycle. The results were compared to that of NCA and further validated based upon the binding motifs of the transcription factors. In summary, the HMM model provides a probabilistic framework to simulate gene regulatory networks and to infer activity profiles of hidden variables.

LanguageEnglish (US)
Title of host publicationAIChE Annual Meeting, Conference Proceedings
Pages11699
Number of pages1
StatePublished - 2005
Externally publishedYes
Event05AIChE: 2005 AIChE Annual Meeting and Fall Showcase - Cincinnati, OH, United States
Duration: Oct 30 2005Nov 4 2005

Other

Other05AIChE: 2005 AIChE Annual Meeting and Fall Showcase
CountryUnited States
CityCincinnati, OH
Period10/30/0511/4/05

Profile

Transcription factors
Hidden Markov models
Genes
Gene expression
Proteins
Network components
DNA
Yeast
Escherichia coli
Data acquisition
Cells
Throughput
Feedback

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Li, Z., Shaw, S. M., Yedwabnick, M. J., & Chan, C. (2005). Inferring transcription factor activities with a probabilistic framework of Hidden Markov Model. In AIChE Annual Meeting, Conference Proceedings (pp. 11699)

Inferring transcription factor activities with a probabilistic framework of Hidden Markov Model. / Li, Zheng; Shaw, Stephen M.; Yedwabnick, Matthew J.; Chan, Christina.

AIChE Annual Meeting, Conference Proceedings. 2005. p. 11699.

Research output: ResearchConference contribution

Li, Z, Shaw, SM, Yedwabnick, MJ & Chan, C 2005, Inferring transcription factor activities with a probabilistic framework of Hidden Markov Model. in AIChE Annual Meeting, Conference Proceedings. pp. 11699, 05AIChE: 2005 AIChE Annual Meeting and Fall Showcase, Cincinnati, OH, United States, 10/30/05.
Li Z, Shaw SM, Yedwabnick MJ, Chan C. Inferring transcription factor activities with a probabilistic framework of Hidden Markov Model. In AIChE Annual Meeting, Conference Proceedings. 2005. p. 11699.
Li, Zheng ; Shaw, Stephen M. ; Yedwabnick, Matthew J. ; Chan, Christina. / Inferring transcription factor activities with a probabilistic framework of Hidden Markov Model. AIChE Annual Meeting, Conference Proceedings. 2005. pp. 11699
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