scholarly journals A General Solution of the Wright–Fisher Model of Random Genetic Drift

2016 ◽  
Vol 27 (4) ◽  
pp. 467-492 ◽  
Author(s):  
Tat Dat Tran ◽  
Julian Hofrichter ◽  
Jürgen Jost
Keyword(s):  
General Solution ◽  
Genetic Drift ◽  
Random Genetic Drift ◽  
Fisher Model

CYTO-NUCLEAR EPISTASIS: TWO-LOCUS RANDOM GENETIC DRIFT IN HERMAPHRODITIC AND DIOECIOUS SPECIES

Evolution ◽  
2006 ◽  
Vol 60 (4) ◽  
pp. 643 ◽  
Author(s):  
Michael J. Wade ◽  
Charles J. Goodnight
Keyword(s):  
Genetic Drift ◽  
Random Genetic Drift ◽  
Dioecious Species

scholarly journals Influence of Random Genetic Drift on Human Immunodeficiency Virus Type 1envEvolution During Chronic Infection

Genetics ◽  
2004 ◽  
Vol 166 (3) ◽  
pp. 1155-1164 ◽  
Author(s):  
Daniel Shriner ◽  
Raj Shankarappa ◽  
Mark A. Jensen ◽  
David C. Nickle ◽  
John E. Mittler ◽  
...  
Keyword(s):  
Human Immunodeficiency Virus ◽  
Human Immunodeficiency Virus Type ◽  
Virus Type ◽  
Genetic Drift ◽  
Chronic Infection ◽  
Random Genetic Drift ◽  
Immunodeficiency Virus

scholarly journals Evolution of gene regulatory networks by means of selection and random genetic drift

2018 ◽  
Author(s):  
Antonios Kioukis ◽  
Pavlos Pavlidis
Keyword(s):  
Natural Selection ◽  
Positive Selection ◽  
Genetic Drift ◽  
Regulatory Networks ◽  
Fitness Landscape ◽  
Random Genetic Drift ◽  
Maturation Period ◽  
Evolutionary Forces ◽  
Gene Regulatory

The evolution of a population by means of genetic drift and natural selection operating on a gene regulatory network (GRN) of an individual has not been scrutinized in depth. Thus, the relative importance of various evolutionary forces and processes on shaping genetic variability in GRNs is understudied. Furthermore, it is not known if existing tools that identify recent and strong positive selection from genomic sequences, in simple models of evolution, can detect recent positive selection when it operates on GRNs. Here, we propose a simulation framework, called EvoNET, that simulates forward-in-time the evolution of GRNs in a population. Since the population size is finite, random genetic drift is explicitly applied. The fitness of a mutation is not constant, but we evaluate the fitness of each individual by measuring its genetic distance from an optimal genotype. Mutations and recombination may take place from generation to generation, modifying the genotypic composition of the population. Each individual goes through a maturation period, where its GRN reaches equilibrium. At the next step, individuals compete to produce the next generation. As time progresses, the beneficial genotypes push the population higher in the fitness landscape. We examine properties of the GRN evolution such as robustness against the deleterious effect of mutations and the role of genetic drift. We confirm classical results from Andreas Wagner’s work that GRNs show robustness against mutations and we provide new results regarding the interplay between random genetic drift and natural selection.

scholarly journals Numerical complete solution for random genetic drift by energetic variational approach

2019 ◽  
Vol 53 (2) ◽  
pp. 615-634 ◽  
Author(s):  
Chenghua Duan ◽  
Chun Liu ◽  
Cheng Wang ◽  
Xingye Yue
Keyword(s):  
Genetic Drift ◽  
Variational Approach ◽  
Numerical Solutions ◽  
Complete Solution ◽  
Random Genetic Drift ◽  
Least Action Principle ◽  
Dirac Delta ◽  
Splitting Technique ◽  
Least Action ◽  
Convection Dominated

In this paper, we focus on numerical solutions for random genetic drift problem, which is governed by a degenerated convection-dominated parabolic equation. Due to the fixation phenomenon of genes, Dirac delta singularities will develop at boundary points as time evolves. Based on an energetic variational approach (EnVarA), a balance between the maximal dissipation principle (MDP) and least action principle (LAP), we obtain the trajectory equation. In turn, a numerical scheme is proposed using a convex splitting technique, with the unique solvability (on a convex set) and the energy decay property (in time) justified at a theoretical level. Numerical examples are presented for cases of pure drift and drift with semi-selection. The remarkable advantage of this method is its ability to catch the Dirac delta singularity close to machine precision over any equidistant grid.

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