Projects per year
Abstract
Parasitism emerges readily in models and laboratory experiments of RNA world and would lead to extinction unless prevented by compartmentalization or spatial patterning. Modelling replication as an active computational process opens up many degrees of freedom that are exploited to meet environmental challenges, and to modify the evolutionary process itself. Here, we use automata chemistry models and spatial RNA-world models to study the emergence of parasitism and the complexity that evolves in response. The system is initialized with a hand-designed replicator that copies other replicators with a small chance of point mutation. Almost immediately, short parasites arise; these are copied more quickly, and so have an evolutionary advantage. The replicators also become shorter, and so are replicated faster; they evolve a mechanism to slow down replication, which reduces the difference of replication rate of replicators and parasites. They also evolve explicit mechanisms to discriminate copies of self from parasites; these mechanisms become increasingly complex. New parasite species continually arise from mutated replicators, rather than from evolving parasite lineages. Evolution itself evolves, e.g. by effectively increasing point mutation rates, and by generating novel emergent mutational operators. Thus, parasitism drives the evolution of complex replicators and complex ecosystems.
Original language | English |
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Article number | 210441 |
Number of pages | 15 |
Journal | Royal Society Open Science |
Volume | 8 |
Issue number | 8 |
DOIs | |
Publication status | Published - 4 Aug 2021 |
Bibliographical note
© 2021 The AuthorsKeywords
- evolution
- automata chemistry
- parasite
- complexity
Projects
- 2 Finished
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PLAZZMID: Evolutionary algorithms from bacterial
2/06/08 → 30/11/11
Project: Research project (funded) › Research
Datasets
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Simulation and experiment data for the paper "On the Open-Endendness of Detecting Open-Endedness"
Hickinbotham, S. J. (Creator) & Stepney, S. (Creator), University of York, 21 Jul 2022
DOI: 10.15124/88a8bad0-b23f-4afc-80a6-77e6358b7a8f
Dataset