An ecological framework for the analysis of prebiotic chemical reaction networks

Peng, Zhen; Plum, Alex; Gagrani, Praful; Baum, David A.

doi:10.1016/j.jtbi.2020.110451

Cited by 43 publications

(62 citation statements)

References 71 publications

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“…In this way, we may be able to break the complexity of large chemical networks into smaller, more manageable structures (45). Additionally, autocatalytic cores are the building block of evolution in prebiotic chemistries (35); thus their identification paves the way to a systematic exploration of the possible modes of chemical evolution (46,47).…”

Section: Discussionmentioning

confidence: 99%

Universal motifs and the diversity of autocatalytic systems

Blokhuis

Lacoste

Nghe

2020

Proc. Natl. Acad. Sci. U.S.A.

126

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Autocatalysis is essential for the origin of life and chemical evolution. However, the lack of a unified framework so far prevents a systematic study of autocatalysis. Here, we derive, from basic principles, general stoichiometric conditions for catalysis and autocatalysis in chemical reaction networks. This allows for a classification of minimal autocatalytic motifs called cores. While all known autocatalytic systems indeed contain minimal motifs, the classification also reveals hitherto unidentified motifs. We further examine conditions for kinetic viability of such networks, which depends on the autocatalytic motifs they contain and is notably increased by internal catalytic cycles. Finally, we show how this framework extends the range of conceivable autocatalytic systems, by applying our stoichiometric and kinetic analysis to autocatalysis emerging from coupled compartments. The unified approach to autocatalysis presented in this work lays a foundation toward the building of a systems-level theory of chemical evolution.

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Section: Discussionmentioning

confidence: 99%

Universal motifs and the diversity of autocatalytic systems

Blokhuis

Lacoste

Nghe

2020

Proc. Natl. Acad. Sci. U.S.A.

126

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“…each non-food molecule is generated at least as fast as it is used up), by lemma 4.1 of [17]. Other dynamical aspects of RAFs concerning reaction rates have been explored in [15,20], and related dynamics have been explored recently by [25,26].…”

Section: Examplesmentioning

confidence: 99%

The structure of autocatalytic networks, with application to early biochemistry

Steel

Xavier

Huson

2020

J. R. Soc. Interface.

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Metabolism across all known living systems combines two key features. First, all of the molecules that are required are either available in the environment or can be built up from available resources via other reactions within the system. Second, the reactions proceed in a fast and synchronized fashion via catalysts that are also produced within the system. Building on early work by Stuart Kauffman, a precise mathematical model for describing such self-sustaining autocatalytic systems (RAF theory) has been developed to explore the origins and organization of living systems within a general formal framework. In this paper, we develop this theory further by establishing new relationships between classes of RAFs and related classes of networks, and developing new algorithms to investigate and visualize RAF structures in detail. We illustrate our results by showing how it reveals further details into the structure of archaeal and bacterial metabolism near the origin of life, and provide techniques to study and visualize the core aspects of primitive biochemistry.

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“…Several previous studies used artificial chemistry as an avenue for addressing questions related to the origin of life or to general mathematical properties of biochemical networks (Banzhaf and Yamamoto 2015;Kauffman 1993;Benkö et al 2003;Walter Fontana and Buss 1994a, b;Peng et al 2020). Conversely, FBA has been applied mostly to the study of metabolic networks of real organisms (Gu et al 2019;Kauffman et al 2003;Orth et al 2010).…”

Section: Discussionmentioning

confidence: 99%

Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure

et al. 2021

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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.

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An ecological framework for the analysis of prebiotic chemical reaction networks

Cited by 43 publications

References 71 publications

Universal motifs and the diversity of autocatalytic systems

Universal motifs and the diversity of autocatalytic systems

The structure of autocatalytic networks, with application to early biochemistry

Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure

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