Stochastic modelling for quantitative description of heterogeneous biological systems

Nat Rev Genet. 2009 Feb;10(2):122-33. doi: 10.1038/nrg2509.


Two related developments are currently changing traditional approaches to computational systems biology modelling. First, stochastic models are being used increasingly in preference to deterministic models to describe biochemical network dynamics at the single-cell level. Second, sophisticated statistical methods and algorithms are being used to fit both deterministic and stochastic models to time course and other experimental data. Both frameworks are needed to adequately describe observed noise, variability and heterogeneity of biological systems over a range of scales of biological organization.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Computational Biology / methods*
  • Data Interpretation, Statistical*
  • Gene Expression Regulation
  • Models, Biological*
  • Proto-Oncogene Proteins c-mdm2 / metabolism
  • Stochastic Processes*
  • Systems Biology*
  • Tumor Suppressor Protein p53 / metabolism


  • Tumor Suppressor Protein p53
  • MDM2 protein, human
  • Proto-Oncogene Proteins c-mdm2