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. AVAILABILITY: Supplementary data and Matlab code will be made available at the URL below. SUPPLEMENTARY INFORMATION: http://www.chems.msu.edu/groups/chan/ssm.zip.
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. AVAILABILITY: Supplementary data and Matlab code will be made available at the URL below. SUPPLEMENTARY INFORMATION: http://www.chems.msu.edu/groups/chan/ssm.zip.
Authors: Yi Yang; Andrew Maxwell; Xiaowei Zhang; Nan Wang; Edward J Perkins; Chaoyang Zhang; Ping Gong Journal: BMC Bioinformatics Date: 2013-10-09 Impact factor: 3.169