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Stochastic master equation (SME) models can provide detailed representation of genetic regulatory system but their use is restricted by the large data requirements for parameter inference and inherent computational complexity involved in its simulation. In this paper, we approximate the expected value of the output distribution of the SME by the output of a deterministic Differential Equation (DE)...
Context-sensitive probabilistic Boolean networks (PBN) have been recently introduced as a paradigm for modeling genetic regulatory networks and have served as the main model for the application of intervention methods, including optimal control strategies, to favorably effect system dynamics. Since it is believed that the steady state behavior of a context-sensitive PBN is indicative of the phenotype,...
Probabilistic Boolean Networks have served as the main model for studying the application of optimal intervention strategies to favorably affect system dynamics. The errors originating in the data extraction or network inference process prevent the accurate estimation of the state transition probabilities of the network. The mathematical characterization of the uncertainties will enable us to analyze...
An important preliminary goal in learning biological network models from experimental data is to study the plausibility of different types of regulatory mechanisms in living organisms. In addition to providing important biological insight, the knowledge of abundance of some specific regulatory rules in nature helps the computational problems by restricting the space of possible models to be learned...
Owing to computational complexity, it is sometimes necessary to reduce the size of a gene regulatory network. This paper proposes a strategy to reduce the size of a probabilistic Boolean network (PBN) while preserving its dynamical structure, a crucial requirement for the development of intervention strategies based on control theory. In particular, we focus on the following two issues when deleting...
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