Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models

@article{Golightly2006BayesianSI,
  title={Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models},
  author={Andrew Golightly and Darren J. Wilkinson},
  journal={Journal of computational biology : a journal of computational molecular cell biology},
  year={2006},
  volume={13 3},
  pages={
          838-51
        }
}
  • A. Golightly, D. Wilkinson
  • Published 17 May 2006
  • Computer Science
  • Journal of computational biology : a journal of computational molecular cell biology
As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we explore the Bayesian estimation of stochastic kinetic rate constants governing dynamic models of intracellular processes. The underlying model is replaced by a diffusion approximation where a noise term represents intrinsic stochastic behavior and the model is identified using discrete-time (and often incomplete) data that… 
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References

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This paper explores how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model.
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The Bayesian estimation of stochastic rate constants in the context of dynamic models of intracellular processes is concerned with the estimation of parameters in a prokaryotic autoregulatory gene network.
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This study addresses the problem of estimating the parameters of regulatory networks and provides the first application of Markov chain Monte Carlo (MCMC) methods to experimental data and develops an estimation algorithm using MCMC techniques which are flexible enough to allow for the imputation of latent data on a finer time scale.
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