Elucidating the structure and/or dynamics of gene regulatory networks from experimental data is a major goal of\r\nsystems biology. Stochastic models have the potential to absorb noise, account for un-certainty, and help avoid\r\ndata overfitting. Within the frame work of probabilistic polynomial dynamical systems, we present an algorithm for\r\nthe reverse engineering of any gene regulatory network as a discrete, probabilistic polynomial dynamical system.\r\nThe resulting stochastic model is assembled from all minimal models in the model space and the probability\r\nassignment is based on partitioning the model space according to the likeliness with which a minimal model\r\nexplains the observed data. We used this method to identify stochastic models for two published synthetic\r\nnetwork models. In both cases, the generated model retains the key features of the original model and compares\r\nfavorably to the resulting models from other algorithms.
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