Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
External Control in Markovian Genetic Regulatory Networks
Machine Learning
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
System Modeling in Cellular Biology: From Concepts to Nuts and Bolts
System Modeling in Cellular Biology: From Concepts to Nuts and Bolts
A solver for the stochastic master equation applied to gene regulatory networks
Journal of Computational and Applied Mathematics
Optimal infinite-horizon control for probabilistic Boolean networks
IEEE Transactions on Signal Processing - Part II
Transient Dynamics of Reduced-Order Models of Genetic Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Fine-scale models based on stochastic master equations can provide the most detailed description of the dynamics of gene expression and imbed, in principle, all the information about the biochemical reactions involved in gene interactions. However,there is limited time-series experimental data available for inference of such fine-scale models. Furthermore, the computational complexity involved in the design of optimal intervention strategies to favorably effect system dynamics for such detailed models is enormous. Thus, there is a need to design mappings from finescale models to coarse-scale models while maintaining sufficient structure for the problem at hand and to study the effect of intervention policies designed using coarse-scale models when applied to fine-scale models. In this paper, we propose a mapping from a fine-scale model represented by a stochastic master equation to a coarse-scale model represented by a probabilistic Boolean network that maintains the collapsed steady state probability distribution of the detailed model. We also derive bounds on the performance of the intervention strategy designed using the coarse-scale model when applied to the fine-scale model.