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Deterministic and stochastic regimes of asexual evolution on rugged fitness landscapes
- Source :
- Genetics. 175(3)
- Publication Year :
- 2006
-
Abstract
- We study the adaptation dynamics of an initially maladapted asexual population with genotypes represented by binary sequences of length $L$. The population evolves in a maximally rugged fitness landscape with a large number of local optima. We find that whether the evolutionary trajectory is deterministic or stochastic depends on the effective mutational distance $d_{\mathrm{eff}}$ upto which the population can spread in genotype space. For $d_{\mathrm{eff}}=L$, the deterministic quasispecies theory operates while for $d_{\mathrm{eff}} < 1$, the evolution is completely stochastic. Between these two limiting cases, the dynamics are described by a local quasispecies theory below a crossover time $T_{\times}$ while above $T_{\times}$, the population gets trapped at a local fitness peak and manages to find a better peak either via stochastic tunneling or double mutations. In the stochastic regime $d_\mathrm{eff} < 1$, we identify two subregimes associated with clonal interference and uphill adaptive walks, respectively. We argue that our findings are relevant to the interepretation of evolution experiments with microbial populations.<br />Revised version, to appear in Genetics. Note on the role of selection in defining d_eff added; new figure 4 included
- Subjects :
- Fitness landscape
Crossover
Population
Adaptation, Biological
FOS: Physical sciences
Stochastic tunneling
Biology
Investigations
Local optimum
Reproduction, Asexual
Genetics
Quantitative Biology::Populations and Evolution
Computer Simulation
Statistical physics
Selection, Genetic
Quantitative Biology - Populations and Evolution
education
Condensed Matter - Statistical Mechanics
education.field_of_study
Stochastic Processes
Statistical Mechanics (cond-mat.stat-mech)
Models, Genetic
Stochastic process
Clonal interference
Populations and Evolution (q-bio.PE)
Biological Evolution
Genetics, Population
Evolutionary biology
FOS: Biological sciences
Mutation (genetic algorithm)
Mutation
Subjects
Details
- ISSN :
- 00166731
- Volume :
- 175
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Genetics
- Accession number :
- edsair.doi.dedup.....616900eaaf563abf72a38f5ade122019