Mathematics and Computer Science Department, University of the Balearic Islands, Palma, Spain.
Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca' Foscari Venezia, Venice, Italy.
PLoS One. 2023 Feb 9;18(2):e0281047. doi: 10.1371/journal.pone.0281047. eCollection 2023.
Metabolism is characterised by chemical reactions linked to each other, creating a complex network structure. The whole metabolic network is divided into pathways of chemical reactions, such that every pathway is a metabolic function. A simplified representation of metabolism, which we call an abstract metabolic network, is a graph in which metabolic pathways are nodes and there is an edge between two nodes if their corresponding pathways share one or more compounds. The abstract metabolic network of a given organism results in a small network that requires low computational power to be analysed and makes it a suitable model to perform a large-scale comparison of organisms' metabolism. To explore the potentials and limits of such a basic representation, we considered a comprehensive set of KEGG organisms, represented through their abstract metabolic network. We performed pairwise comparisons using graph kernel methods and analyse the results through exploratory data analysis and machine learning techniques. The results show that abstract metabolic networks discriminate macro evolutionary events, indicating that they are expressive enough to capture key steps in metabolism evolution.
新陈代谢的特点是彼此关联的化学反应，形成了复杂的网络结构。整个代谢网络被划分为化学反应途径，使得每条途径都是一种代谢功能。我们称之为抽象代谢网络的新陈代谢的简化表示是一个图，其中代谢途径是节点，如果它们对应的途径共享一个或多个化合物，则两个节点之间存在边。给定生物体的抽象代谢网络导致一个小网络，需要低计算能力来分析，并且使其成为执行生物体代谢大规模比较的合适模型。为了探索这种基本表示的潜力和局限性，我们考虑了一组全面的 KEGG 生物体，通过它们的抽象代谢网络来表示。我们使用图核方法进行成对比较，并通过探索性数据分析和机器学习技术分析结果。结果表明，抽象代谢网络可区分宏观进化事件，表明它们具有足够的表达能力，可以捕获代谢进化中的关键步骤。
