Bayesian sequential inference for stochastic kinetic biochemical network models
Bayesian sequential inference for stochastic kinetic biochemical network models
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DOI：
10.1089/cmb.2006.13.838
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发表时间：
2006-04-01
影响因子：
1.7
通讯作者：
Wilkinson, Darren J.
中科院分区：
文献类型：
--
作者：
Golightly, Andrew;Wilkinson, Darren J.
随着后基因组生物学变得更具预测性，推断遗传和生物化学网络的速率参数的能力将变得越来越重要。在本文中，我们探讨了贝叶斯估计的随机动力学速率常数的动态模型的细胞内过程。底层模型被扩散近似所取代，其中噪声项表示内在随机行为，并且使用受测量误差影响的离散时间（并且通常不完整）数据来识别模型。序列MCMC方法，然后使用在线采样的模型参数在几个数据贫乏的上下文中。该方法是说明通过将其应用到一个简单的原核自动调节基因网络的参数估计。
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 is subject to measurement error. Sequential MCMC methods are then used to sample the model parameters on-line in several data-poor contexts. The methodology is illustrated by applying it to the estimation of parameters in a simple prokaryotic auto-regulatory gene network.