7,004 research outputs found
What does it take to evolve behaviorally complex organisms?
What genotypic features explain the evolvability of organisms that have to accomplish many different tasks? The genotype of behaviorally complex organisms may be more likely to encode modular neural architectures because neural modules dedicated to distinct tasks avoid neural interference, i.e., the arrival of conflicting messages for changing the value of connection weights during learning. However, if the connection weights for the various modules are genetically inherited, this raises the problem of genetic linkage: favorable mutations may fall on one portion of the genotype encoding one neural module and unfavorable mutations on another portion encoding another module. We show that this can prevent the genotype from reaching an adaptive optimum. This effect is different from other linkage effects described in the literature and we argue that it represents a new class of genetic constraints. Using simulations we show that sexual reproduction can alleviate the problem of genetic linkage by recombining separate modules all of which incorporate either favorable or unfavorable mutations. We speculate that this effect may contribute to the taxonomic prevalence of sexual reproduction among higher organisms. In addition to sexual recombination, the problem of genetic linkage for behaviorally complex organisms may be mitigated by entrusting evolution with the task of finding appropriate modular architectures and learning with the task of finding the appropriate connection weights for these architectures
Nonlinear deterministic equations in biological evolution
We review models of biological evolution in which the population frequency
changes deterministically with time. If the population is self-replicating,
although the equations for simple prototypes can be linearised, nonlinear
equations arise in many complex situations. For sexual populations, even in the
simplest setting, the equations are necessarily nonlinear due to the mixing of
the parental genetic material. The solutions of such nonlinear equations
display interesting features such as multiple equilibria and phase transitions.
We mainly discuss those models for which an analytical understanding of such
nonlinear equations is available.Comment: Invited review for J. Nonlin. Math. Phy
Finding a Mate With No Social Skills
Sexual reproductive behavior has a necessary social coordination component as
willing and capable partners must both be in the right place at the right time.
While there are many known social behavioral adaptations to support solutions
to this problem, we explore the possibility and likelihood of solutions that
rely only on non-social mechanisms. We find three kinds of social organization
that help solve this social coordination problem (herding, assortative mating,
and natal philopatry) emerge in populations of simulated agents with no social
mechanisms available to support these organizations. We conclude that the
non-social origins of these social organizations around sexual reproduction may
provide the environment for the development of social solutions to the same and
different problems.Comment: 8 pages, 5 figures, GECCO'1
The contribution of statistical physics to evolutionary biology
Evolutionary biology shares many concepts with statistical physics: both deal
with populations, whether of molecules or organisms, and both seek to simplify
evolution in very many dimensions. Often, methodologies have undergone parallel
and independent development, as with stochastic methods in population genetics.
We discuss aspects of population genetics that have embraced methods from
physics: amongst others, non-equilibrium statistical mechanics, travelling
waves, and Monte-Carlo methods have been used to study polygenic evolution,
rates of adaptation, and range expansions. These applications indicate that
evolutionary biology can further benefit from interactions with other areas of
statistical physics, for example, by following the distribution of paths taken
by a population through time.Comment: 18 pages, 3 figures, glossary. Accepted in Trend in Ecology and
Evolution (to appear in print in August 2011
Complexity of evolutionary equilibria in static fitness landscapes
A fitness landscape is a genetic space -- with two genotypes adjacent if they
differ in a single locus -- and a fitness function. Evolutionary dynamics
produce a flow on this landscape from lower fitness to higher; reaching
equilibrium only if a local fitness peak is found. I use computational
complexity to question the common assumption that evolution on static fitness
landscapes can quickly reach a local fitness peak. I do this by showing that
the popular NK model of rugged fitness landscapes is PLS-complete for K >= 2;
the reduction from Weighted 2SAT is a bijection on adaptive walks, so there are
NK fitness landscapes where every adaptive path from some vertices is of
exponential length. Alternatively -- under the standard complexity theoretic
assumption that there are problems in PLS not solvable in polynomial time --
this means that there are no evolutionary dynamics (known, or to be discovered,
and not necessarily following adaptive paths) that can converge to a local
fitness peak on all NK landscapes with K = 2. Applying results from the
analysis of simplex algorithms, I show that there exist single-peaked
landscapes with no reciprocal sign epistasis where the expected length of an
adaptive path following strong selection weak mutation dynamics is
even though an adaptive path to the optimum of length less
than n is available from every vertex. The technical results are written to be
accessible to mathematical biologists without a computer science background,
and the biological literature is summarized for the convenience of
non-biologists with the aim to open a constructive dialogue between the two
disciplines.Comment: 14 pages, 3 figure
Exploiting the adaptation dynamics to predict the distribution of beneficial fitness effects
Adaptation of asexual populations is driven by beneficial mutations and
therefore the dynamics of this process, besides other factors, depend on the
distribution of beneficial fitness effects. It is known that on uncorrelated
fitness landscapes, this distribution can only be of three types: truncated,
exponential and power law. We performed extensive stochastic simulations to
study the adaptation dynamics on rugged fitness landscapes, and identified two
quantities that can be used to distinguish the underlying distribution of
beneficial fitness effects. The first quantity studied here is the fitness
difference between successive mutations that spread in the population, which is
found to decrease in the case of truncated distributions, remain nearly a
constant for exponentially decaying distributions and increase when the fitness
distribution decays as a power law. The second quantity of interest, namely,
the rate of change of fitness with time also shows quantitatively different
behaviour for different beneficial fitness distributions. The patterns
displayed by the two aforementioned quantities are found to hold for both low
and high mutation rates. We discuss how these patterns can be exploited to
determine the distribution of beneficial fitness effects in microbial
experiments.Comment: Communicated to PLOS ON
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