Modeling stochasticity and variability in gene regulatory networks

David Murrugarra, Alan Veliz-Cuba, Boris Aguilar, Seda Arat, Reinhard Laubenbacher

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53- mdm2 complex.

Original languageEnglish
Article number5
JournalEurasip Journal on Bioinformatics and Systems Biology
Issue number1
StatePublished - 2012

Bibliographical note

Funding Information:
DM and RL were partially supported by NSF grant CMMI-0908201. RL and DM thank Ilya Shmulevich for helpful suggestions. The authors thank the anonymous reviewers for many suggestions that improved the article.

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology
  • Computer Science Applications
  • Computational Mathematics


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