Department of Statistics, Pennsylvania State University, Hershey, PA 17033, USA.
Brief Bioinform. 2012 Mar;13(2):162-74. doi: 10.1093/bib/bbr032. Epub 2011 Jul 10.
Organisms usually cope with change in the environment by altering the dynamic trajectory of gene expression to adjust the complement of active proteins. The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanistic base of gene-environment interactions essential for organismic development. We describe a computational framework for clustering the dynamics of gene expression in distinct environments through Gaussian mixture fitting to the expression data measured at a set of discrete time points. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The future directions of gene clustering in terms of incorporations of the latest biological discoveries and statistical innovations are discussed. We provide a set of computational tools that are applicable to modeling and analysis of dynamic gene expression data measured in multiple environments.
生物体通常通过改变基因表达的动态轨迹来应对环境变化，从而调节活性蛋白的互补。识别特定的基因集，其表达是适应性的，以响应环境变化，有助于理解基因-环境相互作用的机制基础，这对于生物体的发育是必不可少的。我们描述了一种计算框架，通过在一组离散时间点测量的表达数据的高斯混合拟合，对不同环境中基因表达的动态进行聚类。我们概述了一些关于在不断变化的环境中动态基因表达模式和导致发育分化的基因-环境相互作用的定量可检验假设。讨论了在纳入最新生物学发现和统计创新方面，基因聚类的未来方向。我们提供了一组计算工具，适用于在多个环境中测量的动态基因表达数据的建模和分析。
