Inference of Molecular Networks from High-Throughput Data
The inference of signal transduction and gene regulatory networks (GRN) from data is a major challenge in systems biology. The scientists involved in this project are developing algorithms for the inference of networks from high-throughput experimental data using a variety of mathematical and computational tools and based on different types of experimental data. Two example applications include the reconstruction of genetic regulatory networks from time series gene expression data, and the reconstruction of cellular signal transduction networks from perturbation data.
Simple maximization of the posterior distribution is not sufficient for network reconstruction, since several alternative models are often capable of explaining the data observed, and one would like confidence on model parameters and probability distributions over alternative models. The scientists are therefore using a Markov chain Monte Carlo approach to sample the posterior distribution and derive probabilities for alternative models. These can then be used for experimental design, to find the most informative experiment to refine the network topology further, in an iterative procedure.
J. Mazur; D. Ritter; G. Reinelt; L. Kaderali (2009). Reconstructing Nonlinear Dynamic Models of Gene Regulation using Stochastic Sampling. BMC Bioinformatics 10:448.
L. Kaderali; E. Dazert; U. Zeuge; M. Frese; R. Bartenschlager (2009). Recontructing Signaling Pathways from RNAi Data using Probabilistic Boolean Threshold Networks. Bioinformatics, 25(17), 2229-2235.
N. Radde; L. Kaderali (2009). Inference of an Oscillating Model for the Yeast Cell Cylce. Discrete Applied Mathematics 157, 2285-2295.
Prof. Gerhard Reinelt
Dr. Lars Kaderali
Prof. Dr. Ralf Bartenschlager - Molecular Virology, Heidelberg University
Prof. Dr. Stefan Kramer
Prof. Dr. Soichi Ogishima - Tokyo Dental and Medical University