14,963 research outputs found
Fluctuations of fitness distributions and the rate of Muller's ratchet
The accumulation of deleterious mutations is driven by rare fluctuations
which lead to the loss of all mutation free individuals, a process known as
Muller's ratchet. Even though Muller's ratchet is a paradigmatic process in
population genetics, a quantitative understanding of its rate is still lacking.
The difficulty lies in the nontrivial nature of fluctuations in the fitness
distribution which control the rate of extinction of the fittest genotype. We
address this problem using the simple but classic model of mutation selection
balance with deleterious mutations all having the same effect on fitness. We
show analytically how fluctuations among the fittest individuals propagate to
individuals of lower fitness and have a dramatically amplified effects on the
bulk of the population at a later time. If a reduction in the size of the
fittest class reduces the mean fitness only after a delay, selection opposing
this reduction is also delayed. This delayed restoring force speeds up Muller's
ratchet. We show how the delayed response can be accounted for using a path
integral formulation of the stochastic dynamics and provide an expression for
the rate of the ratchet that is accurate across a broad range of parameters.Comment: Genetics 201
The Self-Organization of Interaction Networks for Nature-Inspired Optimization
Over the last decade, significant progress has been made in understanding
complex biological systems, however there have been few attempts at
incorporating this knowledge into nature inspired optimization algorithms. In
this paper, we present a first attempt at incorporating some of the basic
structural properties of complex biological systems which are believed to be
necessary preconditions for system qualities such as robustness. In particular,
we focus on two important conditions missing in Evolutionary Algorithm
populations; a self-organized definition of locality and interaction epistasis.
We demonstrate that these two features, when combined, provide algorithm
behaviors not observed in the canonical Evolutionary Algorithm or in
Evolutionary Algorithms with structured populations such as the Cellular
Genetic Algorithm. The most noticeable change in algorithm behavior is an
unprecedented capacity for sustainable coexistence of genetically distinct
individuals within a single population. This capacity for sustained genetic
diversity is not imposed on the population but instead emerges as a natural
consequence of the dynamics of the system
The Self-Organization of Interaction Networks for Nature-Inspired Optimization
Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system
Evolution of new regulatory functions on biophysically realistic fitness landscapes
Regulatory networks consist of interacting molecules with a high degree of
mutual chemical specificity. How can these molecules evolve when their function
depends on maintenance of interactions with cognate partners and simultaneous
avoidance of deleterious "crosstalk" with non-cognate molecules? Although
physical models of molecular interactions provide a framework in which
co-evolution of network components can be analyzed, most theoretical studies
have focused on the evolution of individual alleles, neglecting the network. In
contrast, we study the elementary step in the evolution of gene regulatory
networks: duplication of a transcription factor followed by selection for TFs
to specialize their inputs as well as the regulation of their downstream genes.
We show how to coarse grain the complete, biophysically realistic
genotype-phenotype map for this process into macroscopic functional outcomes
and quantify the probability of attaining each. We determine which evolutionary
and biophysical parameters bias evolutionary trajectories towards fast
emergence of new functions and show that this can be greatly facilitated by the
availability of "promiscuity-promoting" mutations that affect TF specificity
Learning behavior in abstract memory schemes for dynamic optimization problems
This is the post-print version of this article. The official article can be accessed from the link below - Copyright @ 2009 Springer VerlagIntegrating memory into evolutionary algorithms is one major approach to enhance their performance in dynamic environments. An abstract memory scheme has been recently developed for evolutionary algorithms in dynamic environments, where the abstraction of good solutions is stored in the memory instead of good solutions themselves to improve future problem solving. This paper further investigates this abstract memory with a focus on understanding the relationship between learning and memory, which is an important but poorly studied issue for evolutionary algorithms in dynamic environments. The experimental study shows that the abstract memory scheme enables learning processes and hence efficiently improves the performance of evolutionary algorithms in dynamic environments.The work by S. Yang was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1
Predictability of evolutionary trajectories in fitness landscapes
Experimental studies on enzyme evolution show that only a small fraction of
all possible mutation trajectories are accessible to evolution. However, these
experiments deal with individual enzymes and explore a tiny part of the fitness
landscape. We report an exhaustive analysis of fitness landscapes constructed
with an off-lattice model of protein folding where fitness is equated with
robustness to misfolding. This model mimics the essential features of the
interactions between amino acids, is consistent with the key paradigms of
protein folding and reproduces the universal distribution of evolutionary rates
among orthologous proteins. We introduce mean path divergence as a quantitative
measure of the degree to which the starting and ending points determine the
path of evolution in fitness landscapes. Global measures of landscape roughness
are good predictors of path divergence in all studied landscapes: the mean path
divergence is greater in smooth landscapes than in rough ones. The
model-derived and experimental landscapes are significantly smoother than
random landscapes and resemble additive landscapes perturbed with moderate
amounts of noise; thus, these landscapes are substantially robust to mutation.
The model landscapes show a deficit of suboptimal peaks even compared with
noisy additive landscapes with similar overall roughness. We suggest that
smoothness and the substantial deficit of peaks in the fitness landscapes of
protein evolution are fundamental consequences of the physics of protein
folding.Comment: 14 pages, 7 figure
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