基于近似贝叶斯计算的基因调控网络逆向工程
Reverse engineering gene regulatory networks using approximate Bayesian computation
摘要：
基因调控网络是基因的集合，这些基因彼此相互作用，并与细胞中的其他物质相互作用。通过使用高通量技术测量随时间推移的基因表达，可以反向工程或推断特定细胞过程中涉及的基因网络的结构。这些基因表达数据通常具有高维度和有限数量的生物学重复和时间点。由于这些问题和生物系统的复杂性，从基因表达数据逆向工程网络的问题需要一套专门的统计工具和方法。我们提出了一个非标准的适应称为近似贝叶斯计算基于马尔可夫链蒙特卡罗采样的模拟为基础的方法。这种方法特别适合于从纵向数据推断基因调控网络。这种方法的性能进行了研究，通过模拟和使用纵向表达数据从大肠杆菌中的遗传修复系统。
英文摘要：
Gene regulatory networks are collections of genes that interact with one other and with other substances in the cell. By measuring gene expression over time using high-throughput technologies, it may be possible to reverse engineer, or infer, the structure of the gene network involved in a particular cellular process. These gene expression data typically have a high dimensionality and a limited number of biological replicates and time points. Due to these issues and the complexity of biological systems, the problem of reverse engineering networks from gene expression data demands a specialized suite of statistical tools and methodologies. We propose a non-standard adaptation of a simulation-based approach known as Approximate Bayesian Computing based on Markov chain Monte Carlo sampling. This approach is particularly well suited for the inference of gene regulatory networks from longitudinal data. The performance of this approach is investigated via simulations and using longitudinal expression data from a genetic repair system in Escherichia coli.
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