Abstract
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.
Highlights
The enormous accumulation of experimental data on the activities of the living cell has triggered an increasing interest in uncovering the biological networks behind the observed data
Identifying dynamic models for gene regulatory networks from transcriptome data is the topic of numerous published articles, and methods have been proposed within different computational frameworks, such as continuous models using differential equations [1,2], discrete models using Boolean networks [3], Petri nets [4,5,6], or Logical models [7,8], and statistical models using dynamic Baysein networks [9,10], among many other methods
The set of possible states of each node is a finite set, and once the mathematical structure of finite fields is imposed on that set, the transition function of each node is necessarily a polynomial. As this framework is rooted in computational algebra and algebraic geometry, results from these fields are used for the reverse engineering of dynamic and static biological networks [34,35,36,37], as well as for analyzing model dynamics [34,38], which usually is a challenge
Summary
The enormous accumulation of experimental data on the activities of the living cell has triggered an increasing interest in uncovering the biological networks behind the observed data. The set of possible states of each node is a finite set, and once the mathematical structure of finite fields is imposed on that set, the transition function of each node is necessarily a polynomial As this framework is rooted in computational algebra and algebraic geometry, results from these fields are used for the reverse engineering of dynamic and static biological networks [34,35,36,37], as well as for analyzing model dynamics [34,38], which usually is a challenge. We first introduce a stochastic generalization of polynomial dynamical systems, namely, probabilistic polynomial dynamical systems, which is a generalization of the above-mentioned probabilistic Boolean networks to multistate models Using this framework, we present a novel method for the reverse engineering of multistate gene regulatory networks from limited and noisy data. We demonstrate this method using the yeast cell cycle model in [17], as well as the synthetic network of the yeast cell cycle in [40]
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