8,319 research outputs found
The Effect of Recombination on the Neutral Evolution of Genetic Robustness
Conventional population genetics considers the evolution of a limited number
of genotypes corresponding to phenotypes with different fitness. As model
phenotypes, in particular RNA secondary structure, have become computationally
tractable, however, it has become apparent that the context dependent effect of
mutations and the many-to-one nature inherent in these genotype-phenotype maps
can have fundamental evolutionary consequences. It has previously been
demonstrated that populations of genotypes evolving on the neutral networks
corresponding to all genotypes with the same secondary structure only through
neutral mutations can evolve mutational robustness [Nimwegen {\it et al.}
Neutral evolution of mutational robustness, 1999 PNAS], by concentrating the
population on regions of high neutrality. Introducing recombination we
demonstrate, through numerically calculating the stationary distribution of an
infinite population on ensembles of random neutral networks that mutational
robustness is significantly enhanced and further that the magnitude of this
enhancement is sensitive to details of the neutral network topology. Through
the simulation of finite populations of genotypes evolving on random neutral
networks and a scaled down microRNA neutral network, we show that even in
finite populations recombination will still act to focus the population on
regions of locally high neutrality.Comment: Accepted for publication in Math. Biosci. as part of the proceedings
of BIOCOMP 200
Phenotypic robustness can increase phenotypic variability after non-genetic perturbations in gene regulatory circuits
Non-genetic perturbations, such as environmental change or developmental
noise, can induce novel phenotypes. If an induced phenotype confers a fitness
advantage, selection may promote its genetic stabilization. Non-genetic
perturbations can thus initiate evolutionary innovation. Genetic variation that
is not usually phenotypically visible may play an important role in this
process. Populations under stabilizing selection on a phenotype that is robust
to mutations can accumulate such variation. After non-genetic perturbations,
this variation can become a source of new phenotypes. We here study the
relationship between a phenotype's robustness to mutations and a population's
potential to generate novel phenotypic variation. To this end, we use a
well-studied model of transcriptional regulation circuits. Such circuits are
important in many evolutionary innovations. We find that phenotypic robustness
promotes phenotypic variability in response to non-genetic perturbations, but
not in response to mutation. Our work suggests that non-genetic perturbations
may initiate innovation more frequently in mutationally robust gene expression
traits.Comment: 11 pages, 5 figure
Fundamental Properties of the Evolution of Mutational Robustness
Evolution on neutral networks of genotypes has been found in models to
concentrate on genotypes with high mutational robustness, to a degree
determined by the topology of the network. Here analysis is generalized beyond
neutral networks to arbitrary selection and parent-offspring transmission. In
this larger realm, geometric features determine mutational robustness: the
alignment of fitness with the orthogonalized eigenvectors of the mutation
matrix weighted by their eigenvalues. "House of cards" mutation is found to
preclude the evolution of mutational robustness. Genetic load is shown to
increase with increasing mutation in arbitrary single and multiple locus
fitness landscapes. The rate of decrease in population fitness can never grow
as mutation rates get higher, showing that "error catastrophes" for genotype
frequencies never cause precipitous losses of population fitness. The
"inclusive inheritance" approach taken here naturally extends these results to
a new concept of dispersal robustness.Comment: 17 pages, 1 figur
The effect of scale-free topology on the robustness and evolvability of genetic regulatory networks
We investigate how scale-free (SF) and Erdos-Renyi (ER) topologies affect the
interplay between evolvability and robustness of model gene regulatory networks
with Boolean threshold dynamics. In agreement with Oikonomou and Cluzel (2006)
we find that networks with SFin topologies, that is SF topology for incoming
nodes and ER topology for outgoing nodes, are significantly more evolvable
towards specific oscillatory targets than networks with ER topology for both
incoming and outgoing nodes. Similar results are found for networks with SFboth
and SFout topologies. The functionality of the SFout topology, which most
closely resembles the structure of biological gene networks (Babu et al.,
2004), is compared to the ER topology in further detail through an extension to
multiple target outputs, with either an oscillatory or a non-oscillatory
nature. For multiple oscillatory targets of the same length, the differences
between SFout and ER networks are enhanced, but for non-oscillatory targets
both types of networks show fairly similar evolvability. We find that SF
networks generate oscillations much more easily than ER networks do, and this
may explain why SF networks are more evolvable than ER networks are for
oscillatory phenotypes. In spite of their greater evolvability, we find that
networks with SFout topologies are also more robust to mutations than ER
networks. Furthermore, the SFout topologies are more robust to changes in
initial conditions (environmental robustness). For both topologies, we find
that once a population of networks has reached the target state, further
neutral evolution can lead to an increase in both the mutational robustness and
the environmental robustness to changes in initial conditions.Comment: 16 pages, 15 figure
Critical mutation rate has an exponential dependence on population size in haploid and diploid populations
Understanding the effect of population size on the key parameters of evolution is particularly important for populations nearing extinction. There are evolutionary pressures to evolve sequences that are both fit and robust. At high mutation rates, individuals with greater mutational robustness can outcompete those with higher fitness. This is survival-of-the-flattest, and has been observed in digital organisms, theoretically, in simulated RNA evolution, and in RNA viruses. We introduce an algorithmic method capable of determining the relationship between population size, the critical mutation rate at which individuals with greater robustness to mutation are favoured over individuals with greater fitness, and the error threshold. Verification for this method is provided against analytical models for the error threshold. We show that the critical mutation rate for increasing haploid population sizes can be approximated by an exponential function, with much lower mutation rates tolerated by small populations. This is in contrast to previous studies which identified that critical mutation rate was independent of population size. The algorithm is extended to diploid populations in a system modelled on the biological process of meiosis. The results confirm that the relationship remains exponential, but show that both the critical mutation rate and error threshold are lower for diploids, rather than higher as might have been expected. Analyzing the transition from critical mutation rate to error threshold provides an improved definition of critical mutation rate. Natural populations with their numbers in decline can be expected to lose genetic material in line with the exponential model, accelerating and potentially irreversibly advancing their decline, and this could potentially affect extinction, recovery and population management strategy. The effect of population size is particularly strong in small populations with 100 individuals or less; the exponential model has significant potential in aiding population management to prevent local (and global) extinction events
Selective pressures on genomes in molecular evolution
We describe the evolution of macromolecules as an information transmission
process and apply tools from Shannon information theory to it. This allows us
to isolate three independent, competing selective pressures that we term
compression, transmission, and neutrality selection. The first two affect
genome length: the pressure to conserve resources by compressing the code, and
the pressure to acquire additional information that improves the channel,
increasing the rate of information transmission into each offspring. Noisy
transmission channels (replication with mutations) gives rise to a third
pressure that acts on the actual encoding of information; it maximizes the
fraction of mutations that are neutral with respect to the phenotype. This
neutrality selection has important implications for the evolution of
evolvability. We demonstrate each selective pressure in experiments with
digital organisms.Comment: 16 pages, 3 figures, to be published in J. theor. Biolog
Degeneracy: a link between evolvability, robustness and complexity in biological systems
A full accounting of biological robustness remains elusive; both in terms of the mechanisms by which robustness is achieved and the forces that have caused robustness to grow over evolutionary time. Although its importance to topics such as ecosystem services and resilience is well recognized, the broader relationship between robustness and evolution is only starting to be fully appreciated. A renewed interest in this relationship has been prompted by evidence that mutational robustness can play a positive role in the discovery of adaptive innovations (evolvability) and evidence of an intimate relationship between robustness and complexity in biology.
This paper offers a new perspective on the mechanics of evolution and the origins of complexity, robustness, and evolvability. Here we explore the hypothesis that degeneracy, a partial overlap in the functioning of multi-functional components, plays a central role in the evolution and robustness of complex forms. In support of this hypothesis, we present evidence that degeneracy is a fundamental source of robustness, it is intimately tied to multi-scaled complexity, and it establishes conditions that are necessary for system evolvability
Degeneracy: a design principle for achieving robustness and evolvability
Robustness, the insensitivity of some of a biological system's
functionalities to a set of distinct conditions, is intimately linked to
fitness. Recent studies suggest that it may also play a vital role in enabling
the evolution of species. Increasing robustness, so is proposed, can lead to
the emergence of evolvability if evolution proceeds over a neutral network that
extends far throughout the fitness landscape. Here, we show that the design
principles used to achieve robustness dramatically influence whether robustness
leads to evolvability. In simulation experiments, we find that purely redundant
systems have remarkably low evolvability while degenerate, i.e. partially
redundant, systems tend to be orders of magnitude more evolvable. Surprisingly,
the magnitude of observed variation in evolvability can neither be explained by
differences in the size nor the topology of the neutral networks. This suggests
that degeneracy, a ubiquitous characteristic in biological systems, may be an
important enabler of natural evolution. More generally, our study provides
valuable new clues about the origin of innovations in complex adaptive systems.Comment: Accepted in the Journal of Theoretical Biology (Nov 2009
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