The extended TILAR approach: a novel tool for dynamic modeling of the transcription factor network regulating the adaption to in vitro cultivation of murine hepatocytes

BMC Systems Biology

Rolf Gebhardt⁴  Reinhard Guthke²  Sebastian Zellmer¹  Anke Meyer-Baese³  Jörg Linde²  Eugenia Marbach⁴  Madlen Matz-Soja⁴  Wolfgang Schmidt-Heck²  Sebastian Vlaic² 

[1] , GermanFederal Institute for Risk Assessment, Max-Dohrn Str. 8-10, D-10589 Berlin, Germany;, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, D-07745 Jena, Germany;Department of Scientific Computing, Florida State University, Florida 32310-4120, Tallahassee, USA;Institute for Biochemistry, Faculty of Medicine, University of Leipzig, Johannesallee 30, D-04103 Leipzig, Germany

关键词: Tgif1 - TGFB-induced factor homeobox 1;    Dbp - D site albumin promoter binding protein;    Atf3 - activating transcription factor 3;    Liver;    Hepatocytes;    Least angle regression;    Linear modeling;    Key regulator identification;    Transcription factor networks;    Dynamic network inference;    Gene regulation;   

Others  :  1143454
DOI  :  10.1186/1752-0509-6-147

 received in 2012-07-31, accepted in 2012-11-12,  发布年份 2012

【 摘 要 】

Background

Network inference is an important tool to reveal the underlying interactions of biological systems. In the liver, a complex system of transcription factors is active to distribute signals and induce the cellular response following extracellular stimuli. Plenty of information is available about single transcription factors important for the different functions of the liver, but little is known about their causal relations to each other.

Results

Given a DNA microarray time series dataset of collagen monolayers cultured murine hepatocytes, we identified 22 differentially expressed genes for which the corresponding protein is known to exhibit transcription factor activity. We developed the Extended TILAR (ExTILAR) network inference algorithm based on the modeling concept of the previously published TILAR algorithm. Using ExTILAR, we inferred a transcription factor network based on gene expression data which puts these important genes into a functional context. This way, we identified a previously unknown relationship between Tgif1 and Atf3 which we validated experimentally. Beside its known role in metabolic processes, this extends the knowledge about Tgif1 in hepatocytes towards a possible influence of processes such as proliferation and cell cycle. Moreover, two positive (i.e. double negative) regulatory loops were predicted that could give rise to bistable behavior. We further evaluated the performance of ExTILAR by systematic inference of an in silico network.

Conclusions

We present the ExTILAR algorithm, which combines the advantages of the regression based inference algorithm TILAR, like large network sizes processable and low computational costs, with the advantages of dynamic network models based on ordinary differential equation (i.e. in silico knock-down simulations). Like TILAR, ExTILAR makes use of various prior-knowledge types such as transcription factor binding site information and gene interaction knowledge to infer biologically meaningful gene regulatory networks. Therefore, ExTILAR is especially useful when a large number of genes is modeled using a small number of experimental data points.

【 授权许可】

   
2012 Vlaic et al.; licensee BioMed Central Ltd.

【 图 表 】

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