代谢的简单图表示的模型验证
Model validation of simple-graph representations of metabolism
摘要：
化学反应系统的大尺度性质，如新陈代谢，可以用基于图的方法来研究。要做到这一点，需要减少数据库中的信息-化学反应列表。即使对于最简单类型的图表示，这种简化也可以通过几种方式完成。我们通过测试不同的简单网络表征对一个生物学上重要的网络结构--网络模块性（边缘聚集成彼此稀疏连接的密集组的倾向）的信息进行编码的程度来研究它们。为了实现这一目标，我们设计了一个模型的反应系统，网络模块化可以控制和测量如何减少到简单的图形捕捉模型反应系统的模块化结构。我们发现，最能捕捉反应系统模块化结构的网络类型是底物-产物网络（底物与反应产物相连）和物质网络（参与反应的所有物质之间具有边缘）。此外，我们认为，所提出的模型与可调聚类反应系统是一个通用的框架，研究反应系统如何受到模块化。为此，我们调查的模型的统计特性，并发现，除其他事项外，它重建度和分子质量之间的相关性。
英文摘要：
The large-scale properties of chemical reaction systems, such as the metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information -- lists of chemical reactions -- available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure -- network modularity (the propensity for edges to be cluster into dense groups that are sparsely connected between each other). To reach this goal, we design a model of reaction-systems where network modularity can be controlled and measure how well the reduction to simple graphs capture the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate-product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction-systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreate correlations between degree and mass of the molecules.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
