Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure.
Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure.
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DOI：
10.1007/s00239-021-10018-0
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发表时间：
2021-08
影响因子：
3.9
通讯作者：
Segrè D
中科院分区：
文献类型：
--
作者：
Moyer D;Pacheco AR;Bernstein DB;Segrè D
揭示控制代谢网络结构的一般原则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宇宙来帮助解决这个问题。在这里，我们专注于人工化学，其中字符串代表简化的分子，字符串连接和分裂代表可能的化学反应。我们开发了一个新的Python包，人工化学网络（ARCHNET），研究字符串化学使用的工具，从化学计量的约束为基础的建模领域。除了探索不同的字符串化学网络的拓扑特征，我们开发了一种网络修剪算法，可以生成最小的代谢网络，能够从给定的各种环境营养物质中产生一组指定的生物质前体。我们发现，这些最小的代谢网络的组成更强烈地影响生物质反应中的代谢物比环境营养物质的身份。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物化学组织的兴起和进化。更一般地说，我们的工作提供了人工化学和化学计量建模之间的桥梁，这可以帮助解决广泛的开放问题，从自发出现的有组织的新陈代谢到微生物群落的结构。在线版本包含补充材料，可通过10.1007/s00239 - 021 - 10018 - 0获得。
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. The online version contains supplementary material available at 10.1007/s00239-021-10018-0.
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DOI：
10.1021/ci0200570
发表时间：
2003-07-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Benkö, G;Flamm, C;Stadler, PF
通讯作者：
Stadler, PF
影响因子：
5.8
作者：
Hucka, M;Finney, A;Wang, J
通讯作者：
Wang, J
影响因子：
8.6
作者：
Arus-Pous, Josep;Johansson, Simon Viet;Engkvist, Ola
通讯作者：
Engkvist, Ola
影响因子：
14.8
作者：
Heirendt, Laurent;Arreckx, Sylvain;Fleming, Ronan M. T.
通讯作者：
Fleming, Ronan M. T.
DOI：
10.1098/rsif.2016.0627
发表时间：
2016-11
期刊：
Journal of the Royal Society, Interface
影响因子：
--
作者：
Gottstein W;Olivier BG;Bruggeman FJ;Teusink B
通讯作者：
Teusink B