Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns
Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns
复制标题
DOI：
10.1073/pnas.0609152103
复制
发表时间：
2006-12-12
影响因子：
11.1
通讯作者：
Fedoroff, Nina V.
中科院分区：
文献类型：
--
作者：
Lezon, Timothy R.;Banavar, Jayanth R.;Fedoroff, Nina V.
我们描述了一种基于熵最大化原理的方法，用于识别最有可能产生实验观察到的转录谱的基因相互作用网络。在其最简单的形式下，该方法产生成对基因相互作用网络，但它也可以扩展以推断高阶相互作用。对来自酿酒酵母恒化器培养物中表现出能量代谢振荡的基因的微阵列数据的分析，确定了一个基因相互作用网络，该网络反映了细胞内的通讯途径，这些途径根据触发代谢振荡的限制性营养条件来调节细胞代谢活动和细胞分裂。目前这种提取有意义的基因联系的方法的成功表明，最大熵原理对于理解生命系统是一个有用的概念，就像它对于其他复杂的非平衡系统一样。
We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems.