Transient dynamics of reduced-order models of genetic regulatory networks

Ranadip Pal, Sonal Bhattacharya

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish
Article number6202798
Pages (from-to)1230-1244
Number of pages15
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number4
StatePublished - 2012


  • Genetic regulatory network modeling robustness
  • Markov chains
  • transient analysis


Dive into the research topics of 'Transient dynamics of reduced-order models of genetic regulatory networks'. Together they form a unique fingerprint.

Cite this