AI 中文总结
本研究对10912个跨三域生物的代谢网络开展综合分析，提出贴合代谢通路的新构建方法，采用Surprise函数研究其群落结构，识别出进化压力导致的拓扑与群落特征，强调进化模型需纳入额外机制。
AI 中文摘要
代谢网络在理解生物进化、微生物组动态以及疾病预防与治疗中发挥关键作用。本研究对涵盖细菌域、古菌域和真核域的10912个生物的代谢网络属性开展综合分析，提出一种新的网络构建方法，重点关注代谢物的化学转化，该方法与传统方法不同，传统方法会将所有化学反应中的每个底物与每个产物相连，而本方法更贴合代谢通路，仅将产物与其生成底物相连。研究重点调查代谢网络内的群落结构，采用Surprise质量函数，该函数能更好地解决先前方法的一些局限。通过对比真实网络与随机化网络，本研究识别出无法仅用网络度分布解释的特征，从而确定由额外进化压力产生的拓扑属性和群落结构，这表明进化模型需要纳入超出度分布复制的额外机制。
英文摘要
Metabolic networks play pivotal roles in understanding the evolution of organisms, microbiome dynamics and disease prevention and treatment. This study presents a comprehensive analysis of metabolic network properties across 10912 organisms spanning Bacteria, Archaea, and Eukarya domains. A novel method for the network construction is introduced, emphasizing the chemical transformations of metabolites. Unlike conventional approaches that link every substrate to every product in all chemical reactions, this method aligns more closely with metabolic pathways, linking products only to their generating substrates. Emphasis is placed on investigating the community structure within metabolic networks, employing the Surprise quality function that better addresses some limitations of previous approaches. Through comparisons between real and randomized versions of the networks, we identify characteristics that cannot be explained solely by the network degree distribution, thus identifying topological properties and community structures that arise from additional evolutionary pressures. This highlights the need for evolutionary models to incorporate additional mechanisms beyond the replication of the degree distribution.
Comments29 pages, 10 figures, 7 tables
