Mutation-Rate Threshold under Changing Environments with Sharp-Peak Fitness Function

2008 ◽  
Vol 53 (4) ◽  
pp. 2192-2197 ◽  
Author(s):  
Mark Ancliff ◽  
Jeong-Man Park
Keyword(s):  
Mutation Rate ◽  
Fitness Function ◽  
Sharp Peak ◽  
Changing Environments ◽  
Rate Threshold

scholarly journals Mutation rate variability as a driving force in adaptive evolution

2018 ◽  
Author(s):  
Dalit Engelhardt ◽  
Eugene I. Shakhnovich
Keyword(s):  
Mutation Rate ◽  
Bacterial Population ◽  
Molecular Level ◽  
Fitness Function ◽  
System Level ◽  
Resistance Evolution ◽  
High Fitness ◽  
Evolutionary Advantage ◽  
Stress Result ◽  
Very High

Mutation rate is a key determinant of the pace as well as outcome of evolution, and variability in this rate has been shown in different scenarios to play a key role in evolutionary adaptation and resistance evolution under stress. Here we investigate the dynamics of resistance fixation in a bacterial population with variable mutation rates and show that evolutionary outcomes are most sensitive to mutation rate variations when the population is subject to environmental and demographic conditions that suppress the evolutionary advantage of high-fitness subpopulations. By directly mapping a molecular-level biophysical fitness function to the system-level dynamics of the population we show that both low and very high, but not intermediate, levels of stress result in a disproportionate effect of hypermutation on resistance fixation and that traditional definitions of the selection coefficient are insufficient to account for this effect. We demonstrate how this behavior is directly tied to the extent of genetic hitchhiking in the system, the propagation of high-mutation rate cells through association with high-fitness mutations. Our results indicate a substantial role for mutation rate flexibility in the evolution of antibiotic resistance under conditions that present a weak advantage over wildtype to resistant cells.

scholarly journals Species Formation in Simple Ecosystems

1998 ◽  
Vol 09 (04) ◽  
pp. 555-571 ◽  
Author(s):  
F. Bagnoli ◽  
M. Bezzi
Keyword(s):  
Cellular Automaton ◽  
Long Range ◽  
Mutation Rate ◽  
Total Population ◽  
Fitness Function ◽  
Analytical Approximation ◽  
The Other ◽  
Collective Behaviors ◽  
Species Formation ◽  
Mutational Meltdown

In this paper we consider a microscopic model of a simple ecosystem. The basic ingredients of this model are individuals, and both the phenotypic and genotypic levels are taken in account. The model is based on a long range cellular automaton (CA); introducing simple interactions between the individuals, we get some of the complex collective behaviors observed in a real ecosystem. Since our fitness function is smooth, the model does not exhibit the error threshold transition; on the other hand the size of total population is not kept constant, and the mutational meltdown transition is present. We study the effects of competition between genetically similar individuals and how it can lead to species formation. This speciation transition does not depend on the mutation rate. We present also an analytical approximation of the model.

scholarly journals Using computation to enhance diagnosis and therapy: a novel mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Novel Mutation ◽  
Fitness Function ◽  
Genetic Disorders ◽  
Optimal Solution ◽  
Global Optimum ◽  
Adaptive Mutation ◽  
Local Optima

The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals When sinks become sources: adaptive colonization in asexuals

2018 ◽  
Author(s):  
Florian Lavigne ◽  
Guillaume Martin ◽  
Yoann Anciaux ◽  
Julien Papaϯx ◽  
Lionel Roques
Keyword(s):  
Mutation Rate ◽  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Management Strategies ◽  
Geometrical Model ◽  
Immigration Rate ◽  
Lethal Mutagenesis ◽  
Wide Range ◽  
Asexual Populations ◽  
Rate Threshold

AbstractThe successful establishment of a population into a new empty habitat outside of its initial niche is a phenomenon akin to evolutionary rescue in the presence of immigration. It underlies a wide range of processes, such as biological invasions by alien organisms, host shifts in pathogens or the emergence of resistance to pesticides or antibiotics from untreated areas.In this study, we derive an analytically tractable framework to describe the coupled evolutionary and demographic dynamics of asexual populations in a source-sink system. In particular, we analyze the influence of several factors — immigration rate, mutational parameters, and harshness of the stress induced by the change of environment — on the establishment success in the sink (i.e. the formation of a self-sufficient population in the sink), and on the time until establishment. To this aim, we use a classic phenotype-fitness landscape (Fisher’s geometrical model in n dimensions) where source and sink habitats determine distinct phenotypic optima. The harshness of stress, in the sink, is determined by the distance between the fitness optimum in the sink and that of the source. The dynamics of the full distribution of fitness and of population size in the sink are analytically predicted under a strong mutation strong immigration limit where the population is always polymorphic.The resulting eco-evolutionary dynamics depend on mutation and immigration rates in a non straightforward way. Below some mutation rate threshold, establishment always occurs in the sink, following a typical four-phases trajectory of the mean fitness. The waiting time to this establishment is independent of the immigration rate and decreases with the mutation rate. Beyond the mutation rate threshold, lethal mutagenesis impedes establishment and the sink population remains so, albeit with an equilibrium state that depends on the details of the fitness landscape. We use these results to get some insight into possible effects of several management strategies.

scholarly journals Fitness Function Distributions over Generalized Search Neighborhoods in the q-ary Hypercube

2013 ◽  
Vol 21 (4) ◽  
pp. 561-590 ◽  
Author(s):  
Andrew M. Sutton ◽  
Francisco Chicano ◽  
L. Darrell Whitley
Keyword(s):  
Frequency Distribution ◽  
Mutation Rate ◽  
Graph Coloring ◽  
Fitness Function ◽  
Telecommunications Industry ◽  
Finite Alphabet ◽  
Complex Frequency ◽  
Coloring Problem ◽  
Graph Coloring Problem ◽  
Expected Fitness

The frequency distribution of a fitness function over regions of its domain is an important quantity for understanding the behavior of algorithms that employ randomized sampling to search the function. In general, exactly characterizing this distribution is at least as hard as the search problem, since the solutions typically live in the tails of the distribution. However, in some cases it is possible to efficiently retrieve a collection of quantities (called moments) that describe the distribution. In this paper, we consider functions of bounded epistasis that are defined over length-n strings from a finite alphabet of cardinality q. Many problems in combinatorial optimization can be specified as search problems over functions of this type. Employing Fourier analysis of functions over finite groups, we derive an efficient method for computing the exact moments of the frequency distribution of fitness functions over Hamming regions of the q-ary hypercube. We then use this approach to derive equations that describe the expected fitness of the offspring of any point undergoing uniform mutation. The results we present provide insight into the statistical structure of the fitness function for a number of combinatorial problems. For the graph coloring problem, we apply our results to efficiently compute the average number of constraint violations that lie within a certain number of steps of any coloring. We derive an expression for the mutation rate that maximizes the expected fitness of an offspring at each fitness level. We also apply the results to the slightly more complex frequency assignment problem, a relevant application in the domain of the telecommunications industry. As with the graph coloring problem, we provide formulas for the average value of the fitness function in Hamming regions around a solution and the expectation-optimal mutation rate.

On effects of non-systematic reflections on weak-beam images of partial dislocations

1991 ◽  
Vol 49 ◽  
pp. 688-689
Author(s):  
K. Z. Botros ◽  
S. S. Sheinin
Keyword(s):  
High Precision ◽  
Stacking Fault ◽  
Important Application ◽  
Stacking Fault Energy ◽  
Sharp Peak ◽  
Weak Beam ◽  
Partial Dislocations ◽  
Deviation Parameter ◽  
Beam Diffraction

The main features of weak beam images of dislocations were first described by Cockayne et al. using calculations of intensity profiles based on the kinematical and two beam dynamical theories. The feature of weak beam images which is of particular interest in this investigation is that intensity profiles exhibit a sharp peak located at a position very close to the position of the dislocation in the crystal. This property of weak beam images of dislocations has an important application in the determination of stacking fault energy of crystals. This can easily be done since the separation of the partial dislocations bounding a stacking fault ribbon can be measured with high precision, assuming of course that the weak beam relationship between the positions of the image and the dislocation is valid. In order to carry out measurements such as these in practice the specimen must be tilted to "good" weak beam diffraction conditions, which implies utilizing high values of the deviation parameter Sg.

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