72 research outputs found
Multidimensional epistasis and the transitory advantage of sex
Identifying and quantifying the benefits of sex and recombination is a long
standing problem in evolutionary theory. In particular, contradictory claims
have been made about the existence of a benefit of recombination on high
dimensional fitness landscapes in the presence of sign epistasis. Here we
present a comparative numerical study of sexual and asexual evolutionary
dynamics of haploids on tunably rugged model landscapes under strong selection,
paying special attention to the temporal development of the evolutionary
advantage of recombination and the link between population diversity and the
rate of adaptation. We show that the adaptive advantage of recombination on
static rugged landscapes is strictly transitory. At early times, an advantage
of recombination arises through the possibility to combine individually
occurring beneficial mutations, but this effect is reversed at longer times by
the much more efficient trapping of recombining populations at local fitness
peaks. These findings are explained by means of well established results for a
setup with only two loci. In accordance with the Red Queen hypothesis the
transitory advantage can be prolonged indefinitely in fluctuating environments,
and it is maximal when the environment fluctuates on the same time scale on
which trapping at local optima typically occurs.Comment: 34 pages, 9 figures and 8 supplementary figures; revised and final
versio
Evolutionary accessibility of modular fitness landscapes
A fitness landscape is a mapping from the space of genetic sequences, which
is modeled here as a binary hypercube of dimension , to the real numbers. We
consider random models of fitness landscapes, where fitness values are assigned
according to some probabilistic rule, and study the statistical properties of
pathways to the global fitness maximum along which fitness increases
monotonically. Such paths are important for evolution because they are the only
ones that are accessible to an adapting population when mutations occur at a
low rate. The focus of this work is on the block model introduced by A.S.
Perelson and C.A. Macken [Proc. Natl. Acad. Sci. USA 92:9657 (1995)] where the
genome is decomposed into disjoint sets of loci (`modules') that contribute
independently to fitness, and fitness values within blocks are assigned at
random. We show that the number of accessible paths can be written as a product
of the path numbers within the blocks, which provides a detailed analytic
description of the path statistics. The block model can be viewed as a special
case of Kauffman's NK-model, and we compare the analytic results to simulations
of the NK-model with different genetic architectures. We find that the mean
number of accessible paths in the different versions of the model are quite
similar, but the distribution of the path number is qualitatively different in
the block model due to its multiplicative structure. A similar statement
applies to the number of local fitness maxima in the NK-models, which has been
studied extensively in previous works. The overall evolutionary accessibility
of the landscape, as quantified by the probability to find at least one
accessible path to the global maximum, is dramatically lowered by the modular
structure.Comment: 26 pages, 12 figures; final version with some typos correcte
Biological evolution through mutation, selection, and drift: An introductory review
Motivated by present activities in (statistical) physics directed towards
biological evolution, we review the interplay of three evolutionary forces:
mutation, selection, and genetic drift. The review addresses itself to
physicists and intends to bridge the gap between the biological and the
physical literature. We first clarify the terminology and recapitulate the
basic models of population genetics, which describe the evolution of the
composition of a population under the joint action of the various evolutionary
forces. Building on these foundations, we specify the ingredients explicitly,
namely, the various mutation models and fitness landscapes. We then review
recent developments concerning models of mutational degradation. These predict
upper limits for the mutation rate above which mutation can no longer be
controlled by selection, the most important phenomena being error thresholds,
Muller's ratchet, and mutational meltdowns. Error thresholds are deterministic
phenomena, whereas Muller's ratchet requires the stochastic component brought
about by finite population size. Mutational meltdowns additionally rely on an
explicit model of population dynamics, and describe the extinction of
populations. Special emphasis is put on the mutual relationship between these
phenomena. Finally, a few connections with the process of molecular evolution
are established.Comment: 62 pages, 6 figures, many reference
Adaptation in tunably rugged fitness landscapes: The Rough Mount Fuji Model
Much of the current theory of adaptation is based on Gillespie's mutational
landscape model (MLM), which assumes that the fitness values of genotypes
linked by single mutational steps are independent random variables. On the
other hand, a growing body of empirical evidence shows that real fitness
landscapes, while possessing a considerable amount of ruggedness, are smoother
than predicted by the MLM. In the present article we propose and analyse a
simple fitness landscape model with tunable ruggedness based on the Rough Mount
Fuji (RMF) model originally introduced by Aita et al. [Biopolymers 54:64-79
(2000)] in the context of protein evolution. We provide a comprehensive
collection of results pertaining to the topographical structure of RMF
landscapes, including explicit formulae for the expected number of local
fitness maxima, the location of the global peak, and the fitness correlation
function. The statistics of single and multiple adaptive steps on the RMF
landscape are explored mainly through simulations, and the results are compared
to the known behavior in the MLM model. Finally, we show that the RMF model can
explain the large number of second-step mutations observed on a highly-fit
first step backgound in a recent evolution experiment with a microvirid
bacteriophage [Miller et al., Genetics 187:185-202 (2011)].Comment: 43 pages, 12 figures; revised version with new results on the number
of fitness maxim
Universality classes of interaction structures for NK fitness landscapes
Kauffman's NK-model is a paradigmatic example of a class of stochastic models
of genotypic fitness landscapes that aim to capture generic features of
epistatic interactions in multilocus systems. Genotypes are represented as
sequences of binary loci. The fitness assigned to a genotype is a sum of
contributions, each of which is a random function defined on a subset of loci. These subsets or neighborhoods determine the genetic interactions of
the model. Whereas earlier work on the NK model suggested that most of its
properties are robust with regard to the choice of neighborhoods, recent work
has revealed an important and sometimes counter-intuitive influence of the
interaction structure on the properties of NK fitness landscapes. Here we
review these developments and present new results concerning the number of
local fitness maxima and the statistics of selectively accessible (that is,
fitness-monotonic) mutational pathways. In particular, we develop a unified
framework for computing the exponential growth rate of the expected number of
local fitness maxima as a function of , and identify two different
universality classes of interaction structures that display different
asymptotics of this quantity for large . Moreover, we show that the
probability that the fitness landscape can be traversed along an accessible
path decreases exponentially in for a large class of interaction structures
that we characterize as locally bounded. Finally, we discuss the impact of the
NK interaction structures on the dynamics of evolution using adaptive walk
models.Comment: 61 pages, 9 figure
Greedy adaptive walks on a correlated fitness landscape
We study adaptation of a haploid asexual population on a fitness landscape
defined over binary genotype sequences of length . We consider greedy
adaptive walks in which the population moves to the fittest among all single
mutant neighbors of the current genotype until a local fitness maximum is
reached. The landscape is of the rough mount Fuji type, which means that the
fitness value assigned to a sequence is the sum of a random and a deterministic
component. The random components are independent and identically distributed
random variables, and the deterministic component varies linearly with the
distance to a reference sequence. The deterministic fitness gradient is a
parameter that interpolates between the limits of an uncorrelated random
landscape () and an effectively additive landscape ().
When the random fitness component is chosen from the Gumbel distribution,
explicit expressions for the distribution of the number of steps taken by the
greedy walk are obtained, and it is shown that the walk length varies
non-monotonically with the strength of the fitness gradient when the starting
point is sufficiently close to the reference sequence. Asymptotic results for
general distributions of the random fitness component are obtained using
extreme value theory, and it is found that the walk length attains a
non-trivial limit for , different from its values for and
, if is scaled with in an appropriate combination.Comment: minor change
Quantitative analyses of empirical fitness landscapes
The concept of a fitness landscape is a powerful metaphor that offers insight
into various aspects of evolutionary processes and guidance for the study of
evolution. Until recently, empirical evidence on the ruggedness of these
landscapes was lacking, but since it became feasible to construct all possible
genotypes containing combinations of a limited set of mutations, the number of
studies has grown to a point where a classification of landscapes becomes
possible. The aim of this review is to identify measures of epistasis that
allow a meaningful comparison of fitness landscapes and then apply them to the
empirical landscapes to discern factors that affect ruggedness. The various
measures of epistasis that have been proposed in the literature appear to be
equivalent. Our comparison shows that the ruggedness of the empirical landscape
is affected by whether the included mutations are beneficial or deleterious and
by whether intra- or intergenic epistasis is involved. Finally, the empirical
landscapes are compared to landscapes generated with the Rough Mt.\ Fuji model.
Despite the simplicity of this model, it captures the features of the
experimental landscapes remarkably well.Comment: 24 pages, 5 figures; to appear in Journal of Statistical Mechanics:
Theory and Experimen
- …