Predicting species emergence in simulated complex pre-biotic networks
Open accessAbstract
An intriguing question in evolution is what would happen if one could "replay" life's tape. Here, we explore the following hypothesis: when replaying the tape, the details ("decorations") of the outcomes would vary but certain "invariants" might emerge across different life-tapes sharing similar initial conditions. We use large-scale simulations of an in silico model of pre-biotic evolution called GARD (Graded Autocatalysis Replication Domain) to test this hypothesis. GARD models the temporal evolution of molecular assemblies, governed by a rates matrix (i.e. network) that biases different molecules' likelihood of joining or leaving a dynamically growing and splitting assembly. Previous studies have shown the emergence of so called compotypes, i.e., species capable of replication and selection response. Here, we apply networks' science to ascertain the degree to which invariants emerge across different life-tapes under GARD dynamics and whether one can predict these invariant from the chemistry specification alone (i.e. GARD's rates network representing initial conditions). We analysed the (complex) rates' network communities and asked whether communities are related (and how) to the emerging species under GARD's dynamic, and found that the communities correspond to the species emerging from the simulations. Importantly, we show how to use the set of communities detected to predict species emergence without performing any simulations. The analysis developed here may impact complex systems simulations in general.
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Composomes are much more likely to be RAFs because compounds belonging to a composome overlap with communities within β-matrix-defined subsets of more tightly linked network nodes.
“On the other hand, as the compounds belonging to a composome show overlap with communities within the β-matrix-defined subsets of more tightly linked network nodes [27,92] (see §8), it follows that composomes are much more likely to be RAFs.”
From Systems protobiology: origin of life in lipid catalytic networks (2018) · cites this paper as [92]
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- Authors
- Markovitch, O., & Krasnogor, N.
- Affiliations
- Newcastle University (GB)
- Corresponding author
- Krasnogor, Natalio
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-02-15
- Volume
- 13
- Issue
- 2
- Pages
- e0192871
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5813963
- OpenAlex
- W2793430884
- Cited by
- 19
- References
- 79
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2022: 2 · 2021: 2 · 2020: 3 · 2019: 1 · 2018: 4
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/J004111/2, EP/N031962/1)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Origins and Evolution of Life
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- autocatalysis, network communities, in silico evolution, molecular assembly, origin of life
- MeSH terms
- Algorithms, Computer Simulation, Models, Theoretical, Evolution, Chemical
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