Uncovering the general principles that govern the structure of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries can help illuminate this problem by enabling the exploration of chemical reaction universes that are constrained by general mathematical rules. Here, we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We developed a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET), to study string chemistries using tools from the field of stoichiometric constraint-based modeling. In addition to exploring the topological characteristics of different string chemistry networks, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental nutrients. We found that the composition of these minimal metabolic networks was influenced more strongly by the metabolites in the biomass reaction than the identities of the environmental nutrients. This finding has important implications for the reconstruction of organismal metabolic networks and could help us better understand the rise and evolution of biochemical organization. More generally, our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.

揭示控制代谢网络结构的一般原理是理解生命系统出现和进化的关键。人工化学可以通过探索受一般数学规则约束的化学反应领域来帮助阐明这个问题。在这里，我们专注于人工化学，其中字符串表示简化的分子，字符串连接和拆分表示可能的化学反应。我们开发了一个新颖的 Python 包，即人工化学网络工具箱 (ARCHNET)，以使用基于化学计量约束的建模领域的工具研究弦化学。除了探索不同弦化学网络的拓扑特征，我们开发了一种网络修剪算法，该算法可以生成最小代谢网络，能够从给定的环境营养素分类中产生一组指定的生物质前体。我们发现这些最小代谢网络的组成受生物质反应中代谢物的影响比环境养分的特征更强烈。这一发现对生物体代谢网络的重建具有重要意义，可以帮助我们更好地理解生化组织的兴起和进化。更一般地说，我们的工作在人工化学和化学计量模型之间架起了一座桥梁，这有助于解决广泛的开放性问题，从有组织的新陈代谢的自发出现到微生物群落的结构。