%0 Journal Article
%T 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
%A Sebastian Vlaic
%A Wolfgang Schmidt-Heck
%A Madlen Matz-Soja
%A Eugenia Marbach
%A J£¿rg Linde
%A Anke Meyer-Baese
%A Sebastian Zellmer
%A Reinhard Guthke
%A Rolf Gebhardt
%J BMC Systems Biology
%D 2012
%I BioMed Central
%R 10.1186/1752-0509-6-147
%X 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.One of the aims in systems biology is to reveal functions and uncover causalities in the behavior of biological systems. As these systems are usually a composition of multiple processes, mathematical modeling is often applied to investigate processes of interest. The understanding of the parts contributes to the unders
%K Gene regulation
%K Dynamic network inference
%K Transcription factor networks
%K Key regulator identification
%K Linear modeling
%K Least angle regression
%K Hepatocytes
%K Liver
%K Atf3 - activating transcription factor 3
%K Dbp - D site albumin promoter binding protein
%K Tgif1 - TGFB-induced factor homeobox 1
%U http://www.biomedcentral.com/1752-0509/6/147