Probabilistic polynomial dynamical systems for reverse engineering of gene regulatory networks
1 Department of Mathematical Sciences, Clemson University, Clemson, SC 29634-0975, USA
2 Sealy Center of Molecular Medicine, University of Texas Medical Branch, Galveston, TX 77550, USA
3 Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061-0477, USA
4 Department of Mathematics and Statistics, American University of Sharjah, Sharjah, UAE
EURASIP Journal on Bioinformatics and Systems Biology 2011, 2011:1 doi:10.1186/1687-4153-2011-1Published: 6 June 2011
Elucidating the structure and/or dynamics of gene regulatory networks from experimental data is a major goal of systems biology. Stochastic models have the potential to absorb noise, account for un-certainty, and help avoid data overfitting. Within the frame work of probabilistic polynomial dynamical systems, we present an algorithm for the reverse engineering of any gene regulatory network as a discrete, probabilistic polynomial dynamical system. The resulting stochastic model is assembled from all minimal models in the model space and the probability assignment is based on partitioning the model space according to the likeliness with which a minimal model explains the observed data. We used this method to identify stochastic models for two published synthetic network models. In both cases, the generated model retains the key features of the original model and compares favorably to the resulting models from other algorithms.