Fast evaluation of fluctuations in biochemical networks with the linear noise approximation.
Biochemical networks in single cells can display large fluctuations in molecule numbers, making mesoscopic approaches necessary for correct system descriptions. We present a general method that allows rapid characterization of the stochastic properties of intracellular networks. The starting point is a macroscopic description that identifies the system's elementary reactions in terms of rate laws and stoichiometries. From this formulation follows directly the stationary solution of the linear noise approximation (LNA) of the Master equation for all the components in the network. The method complements bifurcation studies of the system's parameter dependence by providing estimates of sizes, correlations, and time scales of stochastic fluctuations. We describe how the LNA can give precise system descriptions also near macroscopic instabilities by suitable variable changes and elimination of fast variables.
Department of Cell & Molecular Biology, Uppsala University, BMC, 751 24 Uppsala, Sweden. Johan.elf@icm.uu.se
Johan
J
Elf
Måns
M
Ehrenberg
D016477:Artifacts; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data*; D042541:Intracellular Space / Q000737:chemistry / Q000378:metabolism; D016014:Linear Models; D008956:Models, Chemical; D008967:Molecular Biology / Q000379:methods* / Q000706:statistics & numerical data*; D016571:Neural Networks, Computer; D013269:Stochastic Processes