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Issue No. 04 - July-Aug. (2012 vol. 9)
ISSN: 1545-5963
pp: 1230-1244
R. Pal , Dept. of Electr. & Comput. Eng., Texas Tech Univ., Lubbock, TX, USA
S. Bhattacharya , Dept. of Electr. & Comput. Eng., Texas Tech Univ., Lubbock, TX, USA
In systems biology, a number of detailed genetic regulatory networks models have been proposed that are capable of modeling the fine-scale dynamics of gene expression. However, limitations on the type and sampling frequency of experimental data often prevent the parameter estimation of the detailed models. Furthermore, the high computational complexity involved in the simulation of a detailed model restricts its use. In such a scenario, reduced-order models capturing the coarse-scale behavior of the network are frequently applied. In this paper, we analyze the dynamics of a reduced-order Markov Chain model approximating a detailed Stochastic Master Equation model. Utilizing a reduction mapping that maintains the aggregated steady-state probability distribution of stochastic master equation models, we provide bounds on the deviation of the Markov Chain transient distribution from the transient aggregated distributions of the stochastic master equation model.
transient analysis, computational complexity, genetics, Markov processes, master equation, physiological models, probability, transient aggregated distributions, transient dynamics, genetic regulatory network model, reduced-order Markov chain model, gene expression, fine-scale dynamics, sampling frequency, parameter estimation, computational complexity, coarse-scale behavior, reduction mapping, aggregated steady-state probability distribution, stochastic master equation model, Markov chain transient distribution, Mathematical model, Computational modeling, Biological system modeling, Steady-state, Markov processes, Transient analysis, Markov chains., Genetic regulatory network modeling robustness, transient analysis
R. Pal, S. Bhattacharya, "Transient Dynamics of Reduced-Order Models of Genetic Regulatory Networks", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 1230-1244, July-Aug. 2012, doi:10.1109/TCBB.2012.37
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