title: Stochastic modelling for quantitative description of heterogeneous biological systems.
abstract: 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.
authors.affiliation: School of Mathematics & Statistics and the Centre for Integrated Systems Biology of Ageing and Nutrition, Newcastle University, Newcastle upon Tyne, Tyne and Wear NE1 7RU, UK. d.j.wilkinson@ncl.ac.uk
authors.forename: Darren J
authors.initials: DJ
authors.lastname: Wilkinson
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D005786:Gene Expression Regulation; D008954:Models, Biological; D051736:Proto-Oncogene Proteins c-mdm2 / Q000378:metabolism; D013269:Stochastic Processes; D049490:Systems Biology; D016159:Tumor Suppressor Protein p53 / Q000378:metabolism
chemical_list: D016159:Tumor Suppressor Protein p53; C497206:MDM2 protein, human; D051736:Proto-Oncogene Proteins c-mdm2