A new probabilistic generative model of parameter inference in biochemical networks
Proceedings of the 2009 ACM symposium on Applied Computing
Simulated annealing algorithm with biased neighborhood distribution for training profile models
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Transient Dynamics of Reduced-Order Models of Genetic Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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We discuss several issues pertaining to the use of stochastic biochemical systems for modeling transcriptional regulation in single cells. By appropriately choosing the system state, we can model transcriptional regulation by a hidden Markov model (HMM). This opens the possibility of using well-known techniques for the statistical analysis and stochastic control of HMMs to mathematically and computationally study transcriptional regulation in single cells. Unfortunately, in all but a few simple cases, analytical characterization of the statistical behavior of the proposed HMM is not possible. Moreover, analysis by Monte Carlo simulation is computationally cumbersome. We discuss several techniques for approximating the HMM by one that is more tractable. We employ simulations, based on a biologically relevant transcriptional regulatory system, to show the relative merits and limitations of various approximation techniques and provide general guidelines for their use.