Complexity reduction of stochastic master equation simulation based on Kronecker product analysis

Mehmet Umut Caglar, Ranadip Pal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The fine-scale stochastic behavior of genetic regulatory networks is often modeled using stochastic master equations. The inherently high computational complexity of the stochastic master equation simulation presents a challenge in its application to biological system modeling even when the model parameters can be properly estimated. In this article, we present a new approach to stochastic model simulation based on Kronecker product analysis and approximation of Zassenhaus formula for matrix exponentials. Simulation results on model biological systems illustrate the comparative performance of our modeling approach to stochastic master equations with significantly lower computational complexity. We also provide a stochastic upper bound on the deviation of the steady state distribution of our model from the steady state distribution of the stochastic master equation.

Original languageEnglish
Title of host publication2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012
Pages186-193
Number of pages8
DOIs
StatePublished - 2012
Event2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012 - Orlando, FL, United States
Duration: Oct 7 2012Oct 10 2012

Publication series

Name2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012

Conference

Conference2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012
CountryUnited States
CityOrlando, FL
Period10/7/1210/10/12

Keywords

  • Complexity reduction
  • Stochastic master equation simulation

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