遗传网络的信息理论研究
Inferring genetic networks: An information theoretic approach
摘要：
在后基因组时代，许多努力致力于系统地阐明相互作用的基因和蛋白质的复杂网络。这些努力包括实验和计算方法。微阵列技术为在基因组水平上监测基因表达水平提供了机会。本研究利用信息论的方法，从高通量的基因表达数据出发，提出了一种在粗粒度水平上重建基因调控网络的数学方法。该方法提供了给定基因正调节、负调节或不调节网络基因中的每一个的{ \it后验}概率。这种方法还允许引入先验知识和量化的信息增益从实验数据中使用的推理过程。该信息增益可用于选择在随后的实验中要扰动的基因，以精炼关于基础基因调控网络的结构的知识。所提出的方法的性能进行了研究{ \it在numero}实验。我们的研究结果表明，该方法是适合于集中在大小有限的问题，如，恢复一个小的子网络的利益，通过执行扰动选定的基因。
英文摘要：
In the postgenome era many efforts have been dedicated to systematically elucidate the complex web of interacting genes and proteins. These efforts include experimental and computational methods. Microarray technology offers an opportunity for monitoring gene expression level at the genome scale. By recourse to information theory, this study proposes a mathematical approach to reconstruct gene regulatory networks at coarse-grain level from high throughput gene expression data. The method provides the {\it a posteriori} probability that a given gene regulates positively, negatively or does not regulate each one of the network genes. This approach also allows the introduction of prior knowledge and the quantification of the information gain from experimental data used in the inference procedure. This information gain can be used to chose genes to be perturbed in subsequent experiments in order to refine the knowledge about the architecture of an underlying gene regulatory network. The performance of the proposed approach has been studied by {\it in numero} experiments. Our results suggest that the approach is suitable for focusing on size-limited problems, such as, recovering a small subnetwork of interest by performing perturbation over selected genes.
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