Abstract
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.
Highlights
Metabolism occupies a central role in the functioning of biological systems, yet much remains unclear about the degree to which basic features of metabolic networks reflect either
In parallel to analyses focused on metabolism as we know it in individual organisms (Machado et al 2018; Henry et al 2010; Borenstein et al 2008) or in the whole biosphere (Barve and Wagner 2013; Raymond and Segrè 2006; Handorf et al 2005), multiple studies have explored the utility of abstract models of chemistry to investigate particular features of chemical networks
We have devised a pruning algorithm for these networks, which identifies minimal metabolic networks necessary for converting a given set of environmental nutrients into a specific combination of biomass precursors
Summary
Metabolism occupies a central role in the functioning of biological systems, yet much remains unclear about the degree to which basic features of metabolic networks reflect eitherHandling Editor: Ashley Teufel.evolutionary accidents or optimal network structures (Pál et al 2006; Barve and Wagner 2013; Noor et al 2010; Ebenhöh and Heinrich 2001). In parallel to analyses focused on metabolism as we know it in individual organisms (Machado et al 2018; Henry et al 2010; Borenstein et al 2008) or in the whole biosphere (Barve and Wagner 2013; Raymond and Segrè 2006; Handorf et al 2005), multiple studies have explored the utility of abstract models of chemistry to investigate particular features of chemical networks These models, known as artificial chemistries, have the benefit of being unconstrained by the limits of what is known about extant metabolism and about its possible intermediate states lost through evolutionary history (Banzhaf and Yamamoto 2015; Benkö et al 2003; Kauffman 1993).
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