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
1 , Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, D-07745 Jena, Germany
2 Institute for Biochemistry, Faculty of Medicine, University of Leipzig, Johannesallee 30, D-04103 Leipzig, Germany
3 Department of Scientific Computing, Florida State University, Florida 32310-4120, Tallahassee, USA
4 , GermanFederal Institute for Risk Assessment, Max-Dohrn Str. 8-10, D-10589 Berlin, Germany
Citation and License
BMC Systems Biology 2012, 6:147 doi:10.1186/1752-0509-6-147Published: 29 November 2012
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.
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.
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.