22,821 research outputs found
Evolutionary Dynamics in a Simple Model of Self-Assembly
We investigate the evolutionary dynamics of an idealised model for the robust
self-assembly of two-dimensional structures called polyominoes. The model
includes rules that encode interactions between sets of square tiles that drive
the self-assembly process. The relationship between the model's rule set and
its resulting self-assembled structure can be viewed as a genotype-phenotype
map and incorporated into a genetic algorithm. The rule sets evolve under
selection for specified target structures. The corresponding, complex fitness
landscape generates rich evolutionary dynamics as a function of parameters such
as the population size, search space size, mutation rate, and method of
recombination. Furthermore, these systems are simple enough that in some cases
the associated model genome space can be completely characterised, shedding
light on how the evolutionary dynamics depends on the detailed structure of the
fitness landscape. Finally, we apply the model to study the emergence of the
preference for dihedral over cyclic symmetry observed for homomeric protein
tetramers
Coalescence, genetic diversity in sexual populations under selection
In sexual populations, selection operates neither on the whole genome, which
is repeatedly taken apart and reassembled by recombination, nor on individual
alleles that are tightly linked to the chromosomal neighborhood. The resulting
interference between linked alleles reduces the efficiency of selection and
distorts patterns of genetic diversity. Inference of evolutionary history from
diversity shaped by linked selection requires an understanding of these
patterns. Here, we present a simple but powerful scaling analysis identifying
the unit of selection as the genomic "linkage block" with a characteristic
length determined in a self-consistent manner by the condition that the rate of
recombination within the block is comparable to the fitness differences between
different alleles of the block. We find that an asexual model with the strength
of selection tuned to that of the linkage block provides an excellent
description of genetic diversity and the site frequency spectra when compared
to computer simulations. This linkage block approximation is accurate for the
entire spectrum of strength of selection and is particularly powerful in
scenarios with many weakly selected loci. The latter limit allows us to
characterize coalescence, genetic diversity, and the speed of adaptation in the
infinitesimal model of quantitative genetics
A simple model of unbounded evolutionary versatility as a largest-scale trend in organismal evolution
The idea that there are any large-scale trends in the evolution of biological organisms is highly controversial. It is commonly believed, for example, that there is a large-scale trend in evolution towards increasing complexity, but empirical and theoretical arguments undermine this belief. Natural selection results in organisms that are well adapted to their local environments, but it is not clear how local adaptation can produce a global trend. In this paper, I present a simple computational model, in which local adaptation to a randomly changing environment results in a global trend towards increasing evolutionary versatility. In this model, for evolutionary versatility to increase without bound, the environment must be highly dynamic. The model also shows that unbounded evolutionary versatility implies an accelerating evolutionary pace. I believe that unbounded increase in evolutionary versatility is a large-scale trend in evolution. I discuss some of the testable predictions about organismal evolution that are suggested by the model
Avida: a software platform for research in computational evolutionary biology
Avida is a software platform for experiments with self-replicating and evolving computer programs. It provides detailed control over experimental settings and protocols, a large array of measurement tools, and sophisticated methods to analyze and post-process experimental data. We explain the general principles on which Avida is built, as well as its main components and their interactions. We also explain how experiments are set up, carried out, and analyzed
Origin of life in a digital microcosm
While all organisms on Earth descend from a common ancestor, there is no
consensus on whether the origin of this ancestral self-replicator was a one-off
event or whether it was only the final survivor of multiple origins. Here we
use the digital evolution system Avida to study the origin of self-replicating
computer programs. By using a computational system, we avoid many of the
uncertainties inherent in any biochemical system of self-replicators (while
running the risk of ignoring a fundamental aspect of biochemistry). We
generated the exhaustive set of minimal-genome self-replicators and analyzed
the network structure of this fitness landscape. We further examined the
evolvability of these self-replicators and found that the evolvability of a
self-replicator is dependent on its genomic architecture. We studied the
differential ability of replicators to take over the population when competed
against each other (akin to a primordial-soup model of biogenesis) and found
that the probability of a self-replicator out-competing the others is not
uniform. Instead, progenitor (most-recent common ancestor) genotypes are
clustered in a small region of the replicator space. Our results demonstrate
how computational systems can be used as test systems for hypotheses concerning
the origin of life.Comment: 20 pages, 7 figures. To appear in special issue of Philosophical
Transactions of the Royal Society A: Re-Conceptualizing the Origins of Life
from a Physical Sciences Perspectiv
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