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Record W2021764551 · doi:10.1086/319928

Microevolution in an Electronic Microcosm

2001· article· en· W2021764551 on OpenAlexaff
Gabriel Yedid, Graham Bell

Bibliographic record

VenueThe American Naturalist · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicroevolutionBiologyAdaptation (eye)PopulationMicrocosmReplication (statistics)Natural selectionEvolutionary biologyReplicatePhylogeneticsSelection (genetic algorithm)GenomeGeneticsEcologyGeneComputer scienceStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

The evolution of microbial populations in simple environments such as chemostats is still not fully understood. The classical interpretation of adaptation involves a process of successive substitution whereby a new dominant genotype arises by mutation from the genotype previously dominant and spreads more or less rapidly through the population until it is nearly fixed. The population is, thus, nearly uniform most of the time. Some observations suggest that the process may be more complicated, but it remains formidably difficult to assemble the phylogeny of an evolving culture in sufficient detail to be sure. We report experiments with an electronic microcosm inhabited by self-replicating computer programs whose phylogeny can be rendered completely transparent. The physiology of these programs is different in many respects from that of organic creatures, but their population biology has many features in common, including a very extensive, if not unbounded, range of variation. Experimental populations evolved through point mutations (many of which were quasi-neutral when they were viable) and through rearrangements that led to a change in genome size and often had large effects on fitness. As a general rule, smaller genomes execute fewer instructions in order to replicate, the rate of replication increases as the number of instructions executed declines, and the rate of replication in pure culture is a good predictor of success in mixture. When cultured with CPU (central processing unit) time as the sole limiting resource, smaller genomes, therefore, evolve as a correlated response to natural selection for faster replication. The genetic basis of adaptation was highly contingent and always differed in replicate experiments. The pattern of evolution depends on mutation rate. At low mutation rates of 0.01 per genome per generation or less, we observed classic periodic selection, with each dominant genotype descending from the previous dominant and rising to a frequency of 0.8 or more. At higher mutation rates of about 0.1 per genome per generation, the most abundant genotypes rarely exceeded a frequency of about 0.4, and rare genotypes present in a few copies comprised a large part of the population. New dominant genotypes did not usually descend directly from previous dominants but, instead, from one of the many rare or moderately abundant genotypes. We suggest that the conventional chemostat paradigm may hold only as a special case at very low mutation rates and that the dynamics and diversity of evolving populations, even in the simplest conditions, may be more complex than is usually recognized. Artificial genetic autoadaptive systems are likely to be useful in constructing theory for situations that lie beyond the boundary of conventional population genetics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.262
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations51
Published2001
Admission routes1
Has abstractyes

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