Lezon Timothy R, Banavar Jayanth R, Cieplak Marek, Maritan Amos, Fedoroff Nina V
Department of Physics, 104 Davey Laboratory, Pennsylvania State University, University Park, PA 16802, USA.
Proc Natl Acad Sci U S A. 2006 Dec 12;103(50):19033-8. doi: 10.1073/pnas.0609152103. Epub 2006 Nov 30.
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.
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测转录谱的基因相互作用网络。该方法最简单的形式可生成成对基因相互作用网络，但也可扩展以推导高阶相互作用。对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进行分析，确定了一个基因相互作用网络，该网络反映了细胞内通讯途径，这些途径可根据引发代谢振荡的限制营养条件来调节细胞代谢活性和细胞分裂。本方法在提取有意义的遗传联系方面的成功表明，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。
