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- W32995569 abstract "Present life portrays a two-tier phenomenology: molecules compose supramolecular structures, such as cells or organisms, which in turn portray population behaviors, including selection, evolution and ecological dynamics. Prebiotic models have often focused on evolution in populations of self-replicating supramolecules, without explicitly invoking the intermediate molecular-to-supramolecular stage. We explore a prebiotic model that allows one to relate parameters of chemical interaction networks within molecular assemblies to emergent ecological and evolutionary properties in populations of such assemblies. We use the graded autocatalysis replication domain (GARD) model, which simulates the network dynamics of amphipile-containing molecular assemblies, and exhibits quasistationary compositional states termed compotypes. These grow by catalyzed accretion, divide and propagate their compositional information to progeny in a replication-like manner. The model allows us to ask how molecular network parameters influence assembly evolution and population ecology, analyzable by a multi species logistic (r-K) model for population ecology (Lotka-Volterra competition model). We found that compotypes with a larger intrinsic molecular repertoire show a higher intrinsic growth (r) and lower carrying capacity (K), as well as lower replication fidelity. This supports a prebiotic scenario initiated by fast-replicating assemblies with a high molecular diversity, evolving into more faithful replicators with narrower molecular repertoires. A main difference from classical ecology is that in GARD species inter convert into each other rather than consume each other or compete on resources, thus representing ‘fast forward’ of speciation." @default.
- W32995569 created "2016-06-24" @default.
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- W32995569 date "2013-09-02" @default.
- W32995569 modified "2023-09-26" @default.
- W32995569 title "Prebiotic Evolution of Molecular Assemblies: From Molecules to Ecology" @default.
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- W32995569 doi "https://doi.org/10.7551/978-0-262-31709-2-ch020" @default.
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