Monitoring the change in expression patterns over time provides the distinct possibility of unraveling the mechanistic drivers characterizing cellular responses. Gene arrays measuring the level of mRNA expression of thousands of genes simultaneously provide a method of high-throughput data collection necessary for obtaining the scope of data required for understanding the complexities of living organisms. Unraveling the coherent complex structures of transcriptional dynamics is the goal of a large family of computational methods aiming at upgrading the information content of time-course gene expression data. In this review, we summarize the qualitative characteristics of these approaches, discuss the main challenges that this type of complex data present, and, finally, explore the opportunities in the context of developing mechanistic models of cellular response.

中文翻译：

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时序基因表达数据分析：方法，挑战和机遇。

监测表达模式随时间的变化提供了揭示表征细胞应答的机械驱​​动因子的独特可能性。同时测量成千上万个基因的mRNA表达水平的基因阵列提供了一种高通量数据收集方法，该数据收集方法对于获得了解活生物体复杂性所需的数据范围是必不可少的。揭示转录动力学的连贯复杂结构是旨在提高时程基因表达数据信息内容的众多计算方法的目标。在这篇综述中，我们总结了这些方法的定性特征，讨论了这类复杂数据所面临的主要挑战，最后，