Computation of steady-state probability distributions in stochastic models of cellular networks.

Published

Journal Article

Cellular processes are "noisy". In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry.

Full Text

Duke Authors

Cited Authors

  • Hallen, M; Li, B; Tanouchi, Y; Tan, C; West, M; You, L

Published Date

  • October 13, 2011

Published In

Volume / Issue

  • 7 / 10

Start / End Page

  • e1002209 -

PubMed ID

  • 22022252

Pubmed Central ID

  • 22022252

Electronic International Standard Serial Number (EISSN)

  • 1553-7358

International Standard Serial Number (ISSN)

  • 1553-734X

Digital Object Identifier (DOI)

  • 10.1371/journal.pcbi.1002209

Language

  • eng