Comparison of Parameter Estimation Methods in Stochastic Chemical Kinetic Models: Examples in Systems Biology.

ABSTRACT: Stochastic chemical kinetics has become a staple for mechanistically modeling various phenomena in systems biology. These models, even more so than their deterministic counterparts, pose a challenging problem in the estimation of kinetic parameters from experimental data. As a result of the inherent randomness involved in stochastic chemical kinetic models, the estimation methods tend to be statistical in nature. Three classes of estimation methods are implemented and compared in this paper. The first is the exact method, which uses the continuous-time Markov chain representation of stochastic chemical kinetics and is tractable only for a very restricted class of problems. The next class of methods is based on Markov chain Monte Carlo (MCMC) techniques. The third method, termed conditional

SUBMITTER: Gupta A 

PROVIDER: S-EPMC4946376 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature