scholarly journals Simulating Evolution in Asexual Populations with Epistasis

2021 ◽  
pp. 121-154
Author(s):  
Ramon Diaz-Uriarte
Keyword(s):  
Fitness Landscapes ◽  
Epistatic Interactions ◽  
Asexual Populations

AbstractI show how to use OncoSimulR, software for forward-time genetic simulations, to simulate evolution of asexual populations in the presence of epistatic interactions. This chapter emphasizes the specification of fitness and epistasis, both directly (i.e., specifying the effects of individual mutations and their epistatic interactions) and indirectly (using models for random fitness landscapes).

scholarly journals Effects of intra-gene fitness interactions on the benefit of sexual recombination

2006 ◽  
Vol 34 (4) ◽  
pp. 560-561 ◽  
Author(s):  
R.A. Watson ◽  
D.M. Weinreich ◽  
J. Wakeley
Keyword(s):  
Nucleotide Sequence ◽  
Point Mutation ◽  
Sequence Space ◽  
Fitness Effect ◽  
Epistatic Interactions ◽  
Genetic Sequence ◽  
High Fitness ◽  
Sexual Recombination ◽  
Evolutionary Trajectories ◽  
Asexual Populations

Whereas spontaneous point mutation operates on nucleotides individually, sexual recombination manipulates the set of nucleotides within an allele as an essentially particulate unit. In principle, these two different scales of variation enable selection to follow fitness gradients in two different spaces: in nucleotide sequence space and allele sequence space respectively. Epistasis for fitness at these two scales, between nucleotides and between genes, may be qualitatively different and may significantly influence the advantage of mutation-based and recombination-based evolutionary trajectories respectively. We examine scenarios where the genetic sequence within a gene strongly influences the fitness effect of a mutation in that gene, whereas epistatic interactions between sites in different genes are weak or absent. We find that, in cases where beneficial alleles of a gene differ from one another at several nucleotide sites, sexual populations can exhibit enormous benefit compared with asexual populations: not only discovering fit genotypes faster than asexual populations, but also discovering high-fitness genotypes that are effectively not evolvable in asexual populations.

scholarly journals The distribution of epistasis on simple fitness landscapes

2018 ◽  
Author(s):  
Christelle Fraïsse ◽  
John J. Welch
Keyword(s):  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Null Model ◽  
Large Data ◽  
Fitness Landscapes ◽  
Mutational Bias ◽  
Data Set ◽  
Epistatic Interactions ◽  
Standing Variation ◽  
Mean And Variance

AbstractFitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information about the overall distribution is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effects sizes, mutational bias, and maladaptation of the wild-type. We illustrate our approach by reanalysing a large data set of mutant effects in a yeast snoRNA. Though characterized by some strong epistatic interactions, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have little effect on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape, and the distribution of mutations, and so it is expected to vary in consistent ways between new mutations, standing variation, and fixed mutations.

scholarly journals Impact of epistasis and pleiotropy on evolutionary adaptation

2011 ◽  
Vol 279 (1727) ◽  
pp. 247-256 ◽  
Author(s):  
Bjørn Østman ◽  
Arend Hintze ◽  
Christoph Adami
Keyword(s):  
Adaptive Evolution ◽  
Quantitative Measure ◽  
Fitness Landscapes ◽  
Mutation Rates ◽  
Evolutionary Adaptation ◽  
Epistatic Interactions ◽  
Adaptive Process ◽  
The Impact ◽  
Increasing Function ◽  
Intermediate Amount

Evolutionary adaptation is often likened to climbing a hill or peak. While this process is simple for fitness landscapes where mutations are independent, the interaction between mutations (epistasis) as well as mutations at loci that affect more than one trait (pleiotropy) are crucial in complex and realistic fitness landscapes. We investigate the impact of epistasis and pleiotropy on adaptive evolution by studying the evolution of a population of asexual haploid organisms (haplotypes) in a model of N interacting loci, where each locus interacts with K other loci. We use a quantitative measure of the magnitude of epistatic interactions between substitutions, and find that it is an increasing function of K . When haplotypes adapt at high mutation rates, more epistatic pairs of substitutions are observed on the line of descent than expected. The highest fitness is attained in landscapes with an intermediate amount of ruggedness that balance the higher fitness potential of interacting genes with their concomitant decreased evolvability. Our findings imply that the synergism between loci that interact epistatically is crucial for evolving genetic modules with high fitness, while too much ruggedness stalls the adaptive process.

scholarly journals The influence of higher-order epistasis on biological fitness landscape topography

2017 ◽  
Author(s):  
Daniel M. Weinreich ◽  
Yinghong Lan ◽  
Jacob Jaffe ◽  
Robert B. Heckendorn
Keyword(s):  
Natural Selection ◽  
Fitness Landscape ◽  
Higher Order ◽  
Fitness Landscapes ◽  
Epistatic Interactions ◽  
Epistatic Effects

AbstractThe effect of a mutation on the organism often depends on what other mutations are already present in its genome. Geneticists refer to such mutational interactions as epistasis. Pairwise epistatic effects have been recognized for over a century, and their evolutionary implications have received theoretical attention for nearly as long. However, pairwise epistatic interactions themselves can vary with genomic background. This is called higher-order epistasis, and its consequences for evolution are much less well understood. Here, we assess the influence that higher-order epistasis has on the topography of 16 published, biological fitness landscapes. We find that on average, their effects on fitness landscape declines with order, and suggest that notable exceptions to this trend may deserve experimental scrutiny. We explore whether natural selection may have contributed to this finding, and conclude by highlight opportunities for further work dissecting the influence that epistasis of all orders has on the efficiency of natural selection.

SodaPop: a forward simulation suite for the evolutionary dynamics of asexual populations on protein fitness landscapes

2019 ◽  
Vol 35 (20) ◽  
pp. 4053-4062 ◽  
Author(s):  
Louis Gauthier ◽  
Rémicia Di Franco ◽  
Adrian W R Serohijos
Keyword(s):  
Population Dynamics ◽  
Population Size ◽  
Protein Evolution ◽  
Evolutionary Dynamics ◽  
Fitness Landscapes ◽  
Supplementary Information ◽  
Biological Organization ◽  
Physical Force ◽  
Protein Biochemistry ◽  
Asexual Populations

Abstract Motivation Protein evolution is determined by forces at multiple levels of biological organization. Random mutations have an immediate effect on the biophysical properties, structure and function of proteins. These same mutations also affect the fitness of the organism. However, the evolutionary fate of mutations, whether they succeed to fixation or are purged, also depends on population size and dynamics. There is an emerging interest, both theoretically and experimentally, to integrate these two factors in protein evolution. Although there are several tools available for simulating protein evolution, most of them focus on either the biophysical or the population-level determinants, but not both. Hence, there is a need for a publicly available computational tool to explore both the effects of protein biophysics and population dynamics on protein evolution. Results To address this need, we developed SodaPop, a computational suite to simulate protein evolution in the context of the population dynamics of asexual populations. SodaPop accepts as input several fitness landscapes based on protein biochemistry or other user-defined fitness functions. The user can also provide as input experimental fitness landscapes derived from deep mutational scanning approaches or theoretical landscapes derived from physical force field estimates. Here, we demonstrate the broad utility of SodaPop with different applications describing the interplay of selection for protein properties and population dynamics. SodaPop is designed such that population geneticists can explore the influence of protein biochemistry on patterns of genetic variation, and that biochemists and biophysicists can explore the role of population size and demography on protein evolution. Availability and implementation Source code and binaries are freely available at https://github.com/louisgt/SodaPop under the GNU GPLv3 license. The software is implemented in C++ and supported on Linux, Mac OS/X and Windows. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Analysis of epistatic interactions and fitness landscapes using a new geometric approach

2007 ◽  
Vol 7 (1) ◽  
pp. 60 ◽  
Author(s):  
Niko Beerenwinkel ◽  
Lior Pachter ◽  
Bernd Sturmfels ◽  
Santiago F Elena ◽  
Richard E Lenski
Keyword(s):  
Geometric Approach ◽  
Fitness Landscapes ◽  
Epistatic Interactions

scholarly journals Measuring epistasis in fitness landscapes: the correlation of fitness effects of mutations

2016 ◽  
Author(s):  
Luca Ferretti ◽  
Benjamin Schmiegelt ◽  
Daniel Weinreich ◽  
Atsushi Yamauchi ◽  
Yutaka Kobayashi ◽  
...  
Keyword(s):  
Missing Data ◽  
Fitness Landscapes ◽  
High Dimensional ◽  
Epistatic Interactions ◽  
Walsh Spectrum ◽  
Fitness Effects ◽  
Landscape Models ◽  
Natural Measure

Genotypic fitness landscapes are constructed by assessing the fitness of all possible combinations of a given number of mutations. In the last years, several experimental fitness landscapes have been completely resolved. As fitness landscapes are high-dimensional, simple measures of their structure are used as statistics in empirical applications. Epistasis is one of the most relevant features of fitness landscapes. Here we propose a new natural measure of the amount of epistasis based on the correlation of fitness effects of mutations. This measure has a natural interpretation, captures well the interaction between mutations and can be obtained analytically for most landscape models. We discuss how this measure is related to previous measures of epistasis (number of peaks, roughness/slope, fraction of sign epistasis, Fourier-Walsh spectrum) and how it can be easily extended to landscapes with missing data or with fitness ranks only. Furthermore, the dependence of the correlation of fitness effects on mutational distance contains interesting information about the patterns of epistasis. This dependence can be used to uncover the amount and nature of epistatic interactions in a landscape or to discriminate between different landscape models.

scholarly journals Heterogeneity of the GFP fitness landscape and data-driven protein design

2021 ◽  
Author(s):  
Louisa Gonzalez Somermeyer ◽  
Aubin Fleiss ◽  
Alexander S Mishin ◽  
Nina G Bozhanova ◽  
Anna A. Igolkina ◽  
...  
Keyword(s):  
Protein Design ◽  
Fitness Landscape ◽  
Landscape Heterogeneity ◽  
Fluorescent Proteins ◽  
Evolutionary Relationship ◽  
Sequence Divergence ◽  
Fitness Landscapes ◽  
Data Driven ◽  
Practical Application ◽  
Epistatic Interactions

Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Counterintuitively, mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design – instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.

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