Simulated maximum likelihood method for estimating kinetic rates in gene expression
Simulated maximum likelihood method for estimating kinetic rates in gene expression
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DOI：
10.1093/bioinformatics/btl552
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发表时间：
2007-01-01
期刊：
影响因子：
5.8
通讯作者：
Burrage, Kevin
中科院分区：
文献类型：
--
作者：
Tian, Tianhai;Xu, Songlin;Burrage, Kevin
动机：基因表达动力学速率是衡量基因产物稳定性的重要指标，为基因调控网络的重建提供了重要信息。实验技术的最新发展使测量单细胞中转录本和蛋白质分子的数量成为可能。虽然已经提出了基于确定性模型的估计方法，旨在从实验观察中评估动力学速率，但这些方法无法解决基因表达中的噪声，这些噪声可能来自基因表达的离散过程、少量mRNA转录物、转录因子活性的波动和实验环境的可变性。在本文中，我们开发了有效的方法来估计基因调控网络中的动力学速率。模拟最大似然法被用来评估随机微分方程或离散生化反应所描述的随机模型中的参数。不同类型的非参数密度函数被用来测量实验观测的转移概率。对于由生化反应描述的随机模型，我们建议使用模拟的频率分布来评估基于随机模拟的离散性的过渡密度。遗传优化算法被用作搜索最优反应速率的有效工具。数值结果表明，所提出的方法能够给出具有良好精度的动力学速率的稳健估计.
Motivation: Kinetic rate in gene expression is a key measurement of the stability of gene products and gives important information for the reconstruction of genetic regulatory networks. Recent developments in experimental technologies have made it possible to measure the numbers of transcripts and protein molecules in single cells. Although estimation methods based on deterministic models have been proposed aimed at evaluating kinetic rates from experimental observations, these methods cannot tackle noise in gene expression that may arise from discrete processes of gene expression, small numbers of mRNA transcript, fluctuations in the activity of transcriptional factors and variability in the experimental environment.Results: In this paper, we develop effective methods for estimating kinetic rates in genetic regulatory networks. The simulated maximum likelihood method is used to evaluate parameters in stochastic models described by either stochastic differential equations or discrete biochemical reactions. Different types of non-parametric density functions are used to measure the transitional probability of experimental observations. For stochastic models described by biochemical reactions, we propose to use the simulated frequency distribution to evaluate the transitional density based on the discrete nature of stochastic simulations. The genetic optimization algorithm is used as an efficient tool to search for optimal reaction rates. Numerical results indicate that the proposed methods can give robust estimations of kinetic rates with good accuracy.>