Control of stochastic master equation models of genetic regulatory networks by approximating their average behavior

Ranadip Pal, Mehmet Umut Caglar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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) model. The mapping provides a technique to simulate the average behavior of the system in a computationally inexpensive manner and enables us to use existing tools for DE models to control the system. The effectiveness of the mapping and the subsequent intervention policy design was evaluated through a biological example.

Original languageEnglish
Title of host publication2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010
DOIs
StatePublished - 2010
Event2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010 - Cold Spring Harbor, NY, United States
Duration: Nov 10 2010Nov 12 2010

Publication series

Name2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010

Conference

Conference2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010
CountryUnited States
CityCold Spring Harbor, NY
Period11/10/1011/12/10

Keywords

  • Control policy design
  • Differential equation models
  • Stochastic master equation models

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