Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.

ABSTRACT:

Background

The reconstruction of gene regulatory networks from time series gene expression data is one of the most difficult problems in systems biology. This is due to several reasons, among them the combinatorial explosion of possible network topologies, limited information content of the experimental data with high levels of noise, and the complexity of gene regulation at the transcriptional, translational and post-translational levels. At the same time, quantitative, dynamic models, ideally with probability distributions over model topologies and parameters, are highly desirable.

Results

We present a novel approach to infer such models from data, based on nonlinear differential equations, which we embed into a stochastic Bayesian framework. We thus address both the stoch

SUBMITTER: Mazur J 

PROVIDER: S-EPMC2811124 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature