Constructing and Analyzing the Fitness Landscape of an Experimental Evolutionary Process

ChemBioChem ◽  
2008 ◽  
Vol 9 (14) ◽  
pp. 2260-2267 ◽  
Author(s):  
Manfred T. Reetz ◽  
Joaquin Sanchis
Keyword(s):  
Fitness Landscape ◽  
Evolutionary Process

scholarly journals Adaptive fitness landscape for replicator systems: to maximize or not to maximize

2018 ◽  
Vol 13 (3) ◽  
pp. 25 ◽  
Author(s):  
Alexander S. Bratus ◽  
Yuri S. Semenov ◽  
Artem S. Novozhilov
Keyword(s):  
Natural Selection ◽  
Fitness Landscape ◽  
Evolutionary Process ◽  
Hill Climbing ◽  
Adaptive Landscape ◽  
Basic Model ◽  
Mean Fitness ◽  
The Mean ◽  
The Hill ◽  
Fisher’S Fundamental Theorem

Sewall Wright’s adaptive landscape metaphor penetrates a significant part of evolutionary thinking. Supplemented with Fisher’s fundamental theorem of natural selection and Kimura’s maximum principle, it provides a unifying and intuitive representation of the evolutionary process under the influence of natural selection as the hill climbing on the surface of mean population fitness. On the other hand, it is also well known that for many more or less realistic mathematical models this picture is a severe misrepresentation of what actually occurs. Therefore, we are faced with two questions. First, it is important to identify the cases in which adaptive landscape metaphor actually holds exactly in the models, that is, to identify the conditions under which system’s dynamics coincides with the process of searching for a (local) fitness maximum. Second, even if the mean fitness is not maximized in the process of evolution, it is still important to understand the structure of the mean fitness manifold and see the implications of this structure on the system’s dynamics. Using as a basic model the classical replicator equation, in this note we attempt to answer these two questions and illustrate our results with simple well studied systems.

Multidimensional epistasis and fitness landscapes in enzyme evolution

2012 ◽  
Vol 445 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Wei Zhang ◽  
Daniel F. A. R. Dourado ◽  
Pedro Alexandrino Fernandes ◽  
Maria João Ramos ◽  
Bengt Mannervik
Keyword(s):  
Glutathione Transferase ◽  
Fitness Landscape ◽  
Evolutionary Process ◽  
Salient Feature ◽  
Selective Advantage ◽  
Enzyme Evolution ◽  
Conventional Analysis ◽  
Evolutionary Response ◽  
Single Protein ◽  
Evolutionary Trajectories

The conventional analysis of enzyme evolution is to regard one single salient feature as a measure of fitness, expressed in a milieu exposing the possible selective advantage at a given time and location. Given that a single protein may serve more than one function, fitness should be assessed in several dimensions. In the present study we have explored individual mutational steps leading to a triple-point-mutated human GST (glutathione transferase) A2-2 displaying enhanced activity with azathioprine. A total of eight alternative substrates were used to monitor the diverse evolutionary trajectories. The epistatic effects of the mutations on catalytic activity were variable in sign and magnitude and depended on the substrate used, showing that epistasis is a multidimensional quality. Evidently, the multidimensional fitness landscape can lead to alternative trajectories resulting in enzymes optimized for features other than the selectable markers relevant at the origin of the evolutionary process. In this manner the evolutionary response is robust and can adapt to changing environmental conditions.

scholarly journals On the feasibility of saltational evolution

2018 ◽  
Author(s):  
Mikhail I. Katsnelson ◽  
Yuri I. Wolf ◽  
Eugene V. Koonin
Keyword(s):  
Evolutionary Biology ◽  
Fitness Landscape ◽  
Evolutionary Process ◽  
Analytic Solutions ◽  
Population Bottlenecks ◽  
Effective Population ◽  
Evolutionary Transitions ◽  
Evolutionary Changes ◽  
Saltational Evolution ◽  
As Stress

One of the key tenets of Darwin’s theory that was inherited by the Modern Synthesis of evolutionary biology is gradualism, that is, the notion that evolution proceeds gradually, via accumulation of “infinitesimally small” heritable changes 1,2. However, some of the most consequential evolutionary changes, such as, for example, the emergence of major taxa, seem to occur abruptly rather than gradually, as captured in the concepts of punctuated equilibrium 3,4 and evolutionary transitions 5,6. We examine a mathematical model of an evolutionary process on a rugged fitness landscape 7,8 and obtain analytic solutions for the probability of multi-mutational leaps, that is, several mutations occurring simultaneously, within a single generation in one genome, and being fixed all together in the evolving population. The results indicate that, for typical, empirically observed combinations of the parameters of the evolutionary process, namely, effective population size, mutation rate, and distribution of selection coefficients of mutations, the probability of a multi-mutational leap is low, and accordingly, their contribution to the evolutionary process is minor at best. However, such leaps could become an important factor of evolution in situations of population bottlenecks and elevated mutation rates, such as stress-induced mutagenesis in microbes or tumor progression, as well as major evolutionary transitions and evolution of primordial replicators.

scholarly journals The rugged adaptive landscape of an emerging plant RNA virus

2014 ◽  
Author(s):  
Jasna Lalic ◽  
Santiago F. Elena
Keyword(s):  
Fitness Landscape ◽  
Rna Virus ◽  
Large Population ◽  
Emerging Infectious Diseases ◽  
Evolutionary Process ◽  
Adaptive Landscape ◽  
Evolutionary Potential ◽  
Recombination Rates ◽  
New Host ◽  
Population Sizes

RNA viruses are the main source of emerging infectious diseases owed to the evolutionary potential bestowed by their fast replication, large population sizes and high mutation and recombination rates. However, an equally important parameter, which is usually neglected, is the topography of the fitness landscape, that is, how many fitness maxima exist and how well connected they are, which determines the number of accessible evolutionary pathways. To address this question, we have reconstructed the fitness landscape describing the adaptation of Tobacco etch potyvirus to its new host, Arabidopsis thaliana. Fitness was measured for most of the genotypes in the landscape, showing the existence of peaks and holes. We found prevailing epistatic effects between mutations, with cases of reciprocal sign epistasis being common at latter stages. Therefore, results suggest that the landscape was rugged and holey, with several local fitness peaks and a very limited number of potential neutral paths. The viral genotype fixed at the end of the evolutionary process was not on the global fitness optima but stuck into a suboptimal peak.

scholarly journals Inferring long-term effective population size with Mutation-Selection models

2021 ◽  
Author(s):  
T. Latrille ◽  
V. Lanore ◽  
N. Lartillot
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Fitness Landscape ◽  
Evolutionary Process ◽  
Simulated Data ◽  
Effective Population ◽  
Current Form ◽  
Codon Models ◽  
Extant Species

AbstractMutation-selection phylogenetic codon models are grounded on population genetics first principles and represent a principled approach for investigating the intricate interplay between mutation, selection and drift. In their current form, mutation-selection codon models are entirely characterized by the collection of site-specific amino-acid fitness profiles. However, thus far, they have relied on the assumption of a constant genetic drift, translating into a unique effective population size (Ne) across the phylogeny, clearly an unreasonable hypothesis. This assumption can be alleviated by introducing variation in Ne between lineages. In addition to Ne, the mutation rate (μ) is susceptible to vary between lineages, and both should co-vary with life-history traits (LHTs). This suggests that the model should more globally account for the joint evolutionary process followed by all of these lineage-specific variables (Ne, μ, and LHTs). In this direction, we introduce an extended mutation-selection model jointly reconstructing in a Bayesian Monte Carlo framework the fitness landscape across sites and long-term trends in Ne, μ and LHTs along the phylogeny, from an alignment of DNA coding sequences and a matrix of observed LHTs in extant species. The model was tested against simulated data and applied to empirical data in mammals, isopods and primates. The reconstructed history of Ne in these groups appears to correlate with LHTs or ecological variables in a way that suggests that the reconstruction is reasonable, at least in its global trends. On the other hand, the range of variation in Ne inferred across species is surprisingly narrow. This last point suggests that some of the assumptions of the model, in particular concerning the assumed absence of epistatic interactions between sites, are potentially problematic.

Evolutionary Optimization Using Graph Based Evolutionary Algorithms

2003 ◽  
Author(s):  
Steven M. Corns ◽  
Kenneth M. Bryden ◽  
Daniel A. Ashlock
Keyword(s):  
Evolutionary Algorithms ◽  
Population Size ◽  
Fitness Landscape ◽  
Optimization Technique ◽  
Evolutionary Process ◽  
Evolutionary Optimization ◽  
Genetic Characteristics ◽  
Rate Of Spread ◽  
Optimal Population ◽  
The Impact

Graph based evolutionary algorithms (GBEAs) are a novel evolutionary optimization technique that utilize population graphing to impose a topology or geography on the evolving solution set. In many cases in nature, the ability of a particular member of a population to mate and reproduce is limited. The factors creating these limits vary widely and include geographical distance, mating rituals, and others. The effect of these factors is to limit the mating pool, reducing the rate of spread of genetic characteristics, and increased diversity within the population. GBEAs mimic these factors resulting in increased diversity within the solution population. When properly tuned to the problem and the size of the population set, GBEAs can result in improved convergence times and a more diverse number of viable solutions. This paper examines the impact of the fitness landscape, population size, and choice of graph on the evolutionary process. In general, it was found that there was an optimal population size and graph combination for each problem. This optimal graph/population was problem dependent.

scholarly journals On the feasibility of saltational evolution

2019 ◽  
Vol 116 (42) ◽  
pp. 21068-21075 ◽  
Author(s):  
Mikhail I. Katsnelson ◽  
Yuri I. Wolf ◽  
Eugene V. Koonin
Keyword(s):  
Fitness Landscape ◽  
Evolutionary Process ◽  
Adaptive Strategy ◽  
Analytic Solutions ◽  
Mutation Rates ◽  
Effective Population ◽  
Single Generation ◽  
Saltational Evolution ◽  
Induced Mutagenesis ◽  
As Stress

Is evolution always gradual or can it make leaps? We examine a mathematical model of an evolutionary process on a fitness landscape and obtain analytic solutions for the probability of multimutation leaps, that is, several mutations occurring simultaneously, within a single generation in 1 genome, and being fixed all together in the evolving population. The results indicate that, for typical, empirically observed combinations of the parameters of the evolutionary process, namely, effective population size, mutation rate, and distribution of selection coefficients of mutations, the probability of a multimutation leap is low, and accordingly the contribution of such leaps is minor at best. However, we show that, taking sign epistasis into account, leaps could become an important factor of evolution in cases of substantially elevated mutation rates, such as stress-induced mutagenesis in microbes. We hypothesize that stress-induced mutagenesis is an evolvable adaptive strategy.

scholarly journals Genetic Algorithms as a method to study adaptive walks in biological landscapes

2020 ◽  
Author(s):  
Edith Invernizzi ◽  
Graeme D Ruxton
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Biology ◽  
Fitness Landscape ◽  
Evolutionary Process ◽  
Simulation Modelling ◽  
Fitness Landscapes ◽  
Mathematical Methods ◽  
Reproductive Process ◽  
Modelling Framework ◽  
Phenotypic Fitness

AbstractThe metaphor of fitness landscapes is common in evolutionary biology, as a way to visualise the change in allele or phenotypic frequencies of a population under selection. Understanding how different factors in the evolutionary process affect the trajectory of the population across the landscape is of interest to both theoretical and empirical evolutionary biologists. However, fitness landscape studies often have to rely heavily on mathematical methods that are not easy to access by biologically trained researchers. Here, we used a method borrowed from engineering - genetic algorithms - to simulate the evolutionary process and study how different components affect the path taken through a phenotypic fitness landscape. In a simple study, we compare five selection models that reflect different degrees of dependency of fitness on trait quality: this includes strengths of selection, trait-quality dependent reproductive hierarchy and the amount of stochasticity in the reproductive process. We include an analysis of other evolutionary variables such as population size and mutation rate. We analyse a game theory problem, as a test landscape, that lends itself to analysis through a deterministic mathematical simulation, which we use for comparison. Our results show that there are differences in the speed with which different models of selection lead to the fitness optimum.Author summaryEvolution and adaptation in biology occurs in fitness landscapes, multidimensional spaces representing all possible genotypic or phenotypic combinations, where population adapt by following the cline of the fitness dimension. The study of adaptation on complex fitness landscapes has so far been limited by the need for mathematically heavy methods. Here, we present a simulation modelling framework, genetic algorithms, that can be used for evolutionary simulations of a population on a fitness landscape of chosen features and with custom evolutionary parameters.

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