Moment fitting for parameter inference in repeatedly and partially observed stochastic biological models.

ABSTRACT: The inference of reaction rate parameters in biochemical network models from time series concentration data is a central task in computational systems biology. Under the assumption of well mixed conditions the network dynamics are typically described by the chemical master equation, the Fokker Planck equation, the linear noise approximation or the macroscopic rate equation. The inverse problem of estimating the parameters of the underlying network model can be approached in deterministic and stochastic ways, and available methods often compare individual or mean concentration traces obtained from experiments with theoretical model predictions when maximizing likelihoods, minimizing regularized least squares functionals, approximating posterior distributions or sequentially processing the d

SUBMITTER: Kugler P 

PROVIDER: S-EPMC3416831 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature