Tian Tianhai, Xu Songlin, Gao Junbin, Burrage Kevin
Advanced Computational Modelling Centre, University of Queensland Brisbane, QLD 4072, Australia.
Bioinformatics. 2007 Jan 1;23(1):84-91. doi: 10.1093/bioinformatics/btl552. Epub 2006 Oct 26.
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.
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.
基因表达中的动力学速率是基因产物稳定性的关键度量，为遗传调控网络的重建提供重要信息。实验技术的最新发展使得测量单细胞中的转录本和蛋白质分子数量成为可能。尽管已经提出了基于确定性模型的估计方法，旨在从实验观测中评估动力学速率，但这些方法无法处理基因表达中可能因基因表达的离散过程、少量mRNA转录本、转录因子活性波动以及实验环境变异性而产生的噪声。
在本文中，我们开发了用于估计遗传调控网络中动力学速率的有效方法。模拟最大似然法用于评估由随机微分方程或离散生化反应描述的随机模型中的参数。使用不同类型的非参数密度函数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，我们建议基于随机模拟的离散性质，使用模拟频率分布来评估转移密度。遗传优化算法被用作搜索最佳反应速率的有效工具。数值结果表明，所提出的方法能够以良好的精度对动力学速率进行稳健估计。
