Parameter estimation in stochastic biochemical reactions
Parameter estimation in stochastic biochemical reactions
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DOI：
10.1049/ip-syb:20050105
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发表时间：
2006-07-01
期刊：
影响因子：
--
通讯作者：
Timmer, J.
中科院分区：
文献类型：
--
作者：
Reinker, S.;Altman, R. M.;Timmer, J.
基因调控、信号转导和代谢网络是系统生物学的重要研究领域。在活细胞中，随机动力学起着重要的作用;然而，模拟这些过程所需的生化反应的动力学参数往往不能直接通过实验获得。考虑了从离散时间点的分子计数测量数据中估计随机反应常数的问题。为了对系统进行建模，使用了隐马尔可夫过程，其中隐藏状态是真实的分子计数，并且这些状态之间的转换对应于分子碰撞之后的反应事件。提出了两种不同的算法来估计未知的模型参数。第一种方法是近似最大似然法，该方法在每个采样间隔中具有很少可能的反应的系统中给出反应参数的良好估计。第二种算法，将数据视为精确测量，通过求解一个简单的线性方程来近似每个采样间隔中的反应数量。即使在复杂的反应系统中，基于这些近似的可能性最大化也可以提供良好的结果。
Gene regulatory, signal transduction and metabolic networks are major areas of interest in the newly emerging field of systems biology. In living cells, stochastic dynamics play an important role; however, the kinetic parameters of biochemical reactions necessary for modelling these processes are often not accessible directly through experiments. The problem of estimating stochastic reaction constants from molecule count data measured, with error, at discrete time points is considered. For modelling the system, a hidden Markov process is used, where the hidden states are the true molecule counts, and the transitions between those states correspond to reaction events following collisions of molecules. Two different algorithms are proposed for estimating the unknown model parameters. The first is an approximate maximum likelihood method that gives good estimates of the reaction parameters in systems with few possible reactions in each sampling interval. The second algorithm, treating the data as exact measurements, approximates the number of reactions in each sampling interval by solving a simple linear equation. Maximising the likelihood based on these approximations can provide good results, even in complex reaction systems.