scholarly journals Construction of arbitrarily strong amplifiers of natural selection using evolutionary graph theory

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Andreas Pavlogiannis ◽  
Josef Tkadlec ◽  
Krishnendu Chatterjee ◽  
Martin A. Nowak
Author(s):  
T. Monk ◽  
P. Green ◽  
M. Paulin

Evolutionary graph theory is the study of birth–death processes that are constrained by population structure. A principal problem in evolutionary graph theory is to obtain the probability that some initial population of mutants will fixate on a graph, and to determine how that fixation probability depends on the structure of that graph. A fluctuating mutant population on a graph can be considered as a random walk. Martingales exploit symmetry in the steps of a random walk to yield exact analytical expressions for fixation probabilities. They do not require simplifying assumptions such as large population sizes or weak selection. In this paper, we show how martingales can be used to obtain fixation probabilities for symmetric evolutionary graphs. We obtain simpler expressions for the fixation probabilities of star graphs and complete bipartite graphs than have been previously reported and show that these graphs do not amplify selection for advantageous mutations under all conditions.


Biosystems ◽  
2012 ◽  
Vol 107 (2) ◽  
pp. 66-80 ◽  
Author(s):  
Paulo Shakarian ◽  
Patrick Roos ◽  
Anthony Johnson

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Pei-ai Zhang

Evolutionary graph theory is a nice measure to implement evolutionary dynamics on spatial structures of populations. To calculate the fixation probability is usually regarded as a Markov chain process, which is affected by the number of the individuals, the fitness of the mutant, the game strategy, and the structure of the population. However the position of the new mutant is important to its fixation probability. Here the position of the new mutant is laid emphasis on. The method is put forward to calculate the fixation probability of an evolutionary graph (EG) of single level. Then for a class of bilevel EGs, their fixation probabilities are calculated and some propositions are discussed. The conclusion is obtained showing that the bilevel EG is more stable than the corresponding one-rooted EG.


Author(s):  
Paulo Shakarian ◽  
Abhinav Bhatnagar ◽  
Ashkan Aleali ◽  
Elham Shaabani ◽  
Ruocheng Guo

2014 ◽  
Vol 360 ◽  
pp. 117-128 ◽  
Author(s):  
Wes Maciejewski ◽  
Gregory J. Puleo

2010 ◽  
Vol 3 (4) ◽  
pp. 189-194 ◽  
Author(s):  
Chris Paley ◽  
Sergei Taraskin ◽  
Stephen Elliott

2017 ◽  
Vol 14 (135) ◽  
pp. 20170509 ◽  
Author(s):  
Madison S. Krieger ◽  
Alex McAvoy ◽  
Martin A. Nowak

In evolutionary processes, population structure has a substantial effect on natural selection. Here, we analyse how motion of individuals affects constant selection in structured populations. Motion is relevant because it leads to changes in the distribution of types as mutations march towards fixation or extinction. We describe motion as the swapping of individuals on graphs, and more generally as the shuffling of individuals between reproductive updates. Beginning with a one-dimensional graph, the cycle, we prove that motion suppresses natural selection for death–birth (DB) updating or for any process that combines birth–death (BD) and DB updating. If the rule is purely BD updating, no change in fixation probability appears in the presence of motion. We further investigate how motion affects evolution on the square lattice and weighted graphs. In the case of weighted graphs, we find that motion can be either an amplifier or a suppressor of natural selection. In some cases, whether it is one or the other can be a function of the relative reproductive rate, indicating that motion is a subtle and complex attribute of evolving populations. As a first step towards understanding less restricted types of motion in evolutionary graph theory, we consider a similar rule on dynamic graphs induced by a spatial flow and find qualitatively similar results, indicating that continuous motion also suppresses natural selection.


2010 ◽  
Vol 24 (27) ◽  
pp. 5285-5293 ◽  
Author(s):  
PU-YAN NIE ◽  
PEI-AI ZHANG

Evolutionary graph theory (EGT) is recently proposed by Lieberman et al. in 2005. EGT is successful for explaining biological evolution and some social phenomena. It is extremely important to consider the time of fixation for EGT in many practical problems, including evolutionary theory and the evolution of cooperation. This study characterizes the time to asymptotically reach fixation.


2007 ◽  
Vol 246 (4) ◽  
pp. 681-694 ◽  
Author(s):  
Hisashi Ohtsuki ◽  
Jorge M. Pacheco ◽  
Martin A. Nowak

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