Department of Physics, Columbia University, New York, NY 10027, USA.
Proc Natl Acad Sci U S A. 2011 Jan 11;108(2):446-51. doi: 10.1073/pnas.1008898108. Epub 2010 Dec 23.
Over the past decade, a number of researchers in systems biology have sought to relate the function of biological systems to their network-level descriptions--lists of the most important players and the pairwise interactions between them. Both for large networks (in which statistical analysis is often framed in terms of the abundance of repeated small subgraphs) and for small networks which can be analyzed in greater detail (or even synthesized in vivo and subjected to experiment), revealing the relationship between the topology of small subgraphs and their biological function has been a central goal. We here seek to pose this revelation as a statistical task, illustrated using a particular setup which has been constructed experimentally and for which parameterized models of transcriptional regulation have been studied extensively. The question "how does function follow form" is here mathematized by identifying which topological attributes correlate with the diverse possible information-processing tasks which a transcriptional regulatory network can realize. The resulting method reveals one form-function relationship which had earlier been predicted based on analytic results, and reveals a second for which we can provide an analytic interpretation. Resulting source code is distributed via http://formfunction.sourceforge.net.
在过去的十年中，许多系统生物学的研究人员试图将生物系统的功能与其网络层次的描述联系起来——列出最重要的参与者及其两两相互作用的列表。无论是对于大型网络（在这种网络中，统计分析通常是根据重复出现的小子图的丰度来进行的）还是可以更详细地分析的小型网络（甚至可以在体内合成并进行实验），揭示小子图的拓扑结构与其生物学功能之间的关系一直是一个核心目标。在这里，我们试图将这种揭示作为一个统计任务来提出，使用一个特定的设置来说明，该设置已经通过实验构建，并且已经对转录调控的参数化模型进行了广泛的研究。通过确定与转录调控网络可以实现的各种可能的信息处理任务相关的拓扑属性，“功能如何遵循形式”这个问题在这里被数学化了。由此产生的方法揭示了一种之前基于分析结果预测的形式-功能关系，并揭示了第二种形式，我们可以为其提供分析解释。生成的源代码通过 http://formfunction.sourceforge.net 分发。
