Evidence map›Paper›PMID 34386257›Full record

ArticleRoyal Society open science2021

Nothing in evolution makes sense except in the light of parasitism: evolution of complex replication strategies.

Simon J Hickinbotham, Susan Stepney, Paulien Hogeweg

Abstract read
In one paragraph

Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. A Mutation Threshold for Cooperative Takeover.Life (Basel, Switzerland) · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Simon J HickinbothamDepartment of Electronic Engineering, University of York, York, UK.ORCID 0000-0003-0880-4460
Susan StepneyDepartment of Computer Science, University of York, York, UK.ORCID 0000-0003-3146-5401
Paulien HogewegTheoretical Biology and Bioinformatics Group, Utrecht University, Utrecht, The Netherlands.ORCID 0000-0003-3392-9839

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial lifeautomata chemistryparasitereplicatorRNA world

Identifiers

PMID34386257
PMCPMC8334846

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.