Spectral analysis of transient amplifiers for death-birth updating constructed from regular graphs.

Hendrik Richter
Author Information
  1. Hendrik Richter: HTWK Leipzig University of Applied Sciences, Leipzig, Germany. hendrik.richter@htwk-leipzig.de. ORCID

Abstract

A central question of evolutionary dynamics on graphs is whether or not a mutation introduced in a population of residents survives and eventually even spreads to the whole population, or becomes extinct. The outcome naturally depends on the fitness of the mutant and the rules by which mutants and residents may propagate on the network, but arguably the most determining factor is the network structure. Some structured networks are transient amplifiers. They increase for a certain fitness range the fixation probability of beneficial mutations as compared to a well-mixed population. We study a perturbation method for identifying transient amplifiers for death-birth updating. The method involves calculating the coalescence times of random walks on graphs and finding the vertex with the largest remeeting time. If the graph is perturbed by removing an edge from this vertex, there is a certain likelihood that the resulting perturbed graph is a transient amplifier. We test all pairwise nonisomorphic regular graphs up to a certain order and thus cover the whole structural range expressible by these graphs. For cubic and quartic regular graphs we find a sufficiently large number of transient amplifiers. For these networks we carry out a spectral analysis and show that the graphs from which transient amplifiers can be constructed share certain structural properties. Identifying spectral and structural properties may promote finding and designing such networks.

Keywords

References

  1. PLoS Comput Biol. 2015 Nov 06;11(11):e1004437 [PMID: 26544962]
  2. J Theor Biol. 2008 Feb 21;250(4):634-41 [PMID: 18068731]
  3. Proc Natl Acad Sci U S A. 2003 Dec 9;100(25):14966-9 [PMID: 14657359]
  4. Nature. 2017 Apr 13;544(7649):227-230 [PMID: 28355181]
  5. Phys Rev E. 2017 Jul;96(1-1):012313 [PMID: 29347209]
  6. J Theor Biol. 2007 Jun 21;246(4):681-94 [PMID: 17350049]
  7. PLoS Comput Biol. 2020 Jan 17;16(1):e1007494 [PMID: 31951609]
  8. J Math Biol. 2014 Jan;68(1-2):109-43 [PMID: 23179131]
  9. Sci Rep. 2019 May 6;9(1):6946 [PMID: 31061385]
  10. J Theor Biol. 2018 Aug 14;451:10-18 [PMID: 29727631]
  11. Nature. 2004 Apr 8;428(6983):643-6 [PMID: 15074318]
  12. PLoS Comput Biol. 2019 Sep 16;15(9):e1007212 [PMID: 31525178]
  13. R Soc Open Sci. 2015 Apr 29;2(4):140465 [PMID: 26064637]
  14. Biosystems. 2012 Mar;107(3):186-96 [PMID: 22133717]
  15. Theory Biosci. 2007 Aug;126(1):15-21 [PMID: 18087753]
  16. PLoS Comput Biol. 2020 Jul 6;16(7):e1008010 [PMID: 32628660]
  17. Biosystems. 2017 Mar - Apr;153-154:26-44 [PMID: 28238940]
  18. Nature. 2005 Jan 20;433(7023):312-6 [PMID: 15662424]
  19. PLoS One. 2018 Nov 26;13(11):e0200670 [PMID: 30475815]
  20. Biosystems. 2016 Dec;150:87-91 [PMID: 27555086]
  21. Commun Biol. 2018 Jun 14;1:71 [PMID: 30271952]
  22. Nat Commun. 2019 Nov 8;10(1):5107 [PMID: 31704922]
  23. Commun Biol. 2019 Apr 23;2:137 [PMID: 31044162]
  24. Evol Appl. 2016 Mar 08;9(4):565-82 [PMID: 27099622]
  25. Biol Direct. 2016 Aug 23;11:41 [PMID: 27549612]
  26. Proc Biol Sci. 2000 Nov 7;267(1458):2177-82 [PMID: 11413630]
  27. Evol Appl. 2017 Apr 14;10(6):590-602 [PMID: 28616066]
  28. Elife. 2017 Dec 21;6: [PMID: 29266000]
  29. Proc Biol Sci. 2001 Apr 7;268(1468):761-9 [PMID: 11321066]
  30. Syst Biol. 2016 May;65(3):495-507 [PMID: 26658901]
  31. J R Soc Interface. 2019 Mar 29;16(152):20180677 [PMID: 30862280]
  32. J Theor Biol. 2003 Aug 21;223(4):433-50 [PMID: 12875822]
  33. J Theor Biol. 2015 Oct 7;382:44-56 [PMID: 26122591]
  34. Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Feb;85(2 Pt 2):026101 [PMID: 22463273]
  35. PLoS Comput Biol. 2020 Jan 17;16(1):e1007529 [PMID: 31951612]
  36. Bull Math Biol. 2006 Oct;68(7):1573-99 [PMID: 16832734]
  37. Science. 2013 Nov 22;342(6161):995-8 [PMID: 24264992]
  38. Biosystems. 2019 Jun;180:88-100 [PMID: 30914346]
  39. Science. 2002 Oct 25;298(5594):824-7 [PMID: 12399590]
  40. Sci Rep. 2017 Dec;7(1):82 [PMID: 28250441]
  41. Commun Biol. 2019 Apr 23;2:138 [PMID: 31044163]
  42. PLoS One. 2020 Feb 12;15(2):e0228728 [PMID: 32050004]
  43. Phys Rev E Stat Nonlin Soft Matter Phys. 2001 Aug;64(2 Pt 2):026704 [PMID: 11497741]
  44. Theory Biosci. 2019 Nov;138(2):261-275 [PMID: 30900107]

MeSH Term

Biological Evolution
Mutation
Probability

Word Cloud

Created with Highcharts 10.0.0graphstransientamplifierscertainpopulationnetworksregularstructuralanalysisdynamicsresidentswholefitnessmaynetworkrangemethoddeath-birthupdatingfindingvertexgraphperturbedspectralconstructedpropertiesSpectralcentralquestionevolutionarywhethermutationintroducedsurviveseventuallyevenspreadsbecomesextinctoutcomenaturallydependsmutantrulesmutantspropagatearguablydeterminingfactorstructurestructuredincreasefixationprobabilitybeneficialmutationscomparedwell-mixedstudyperturbationidentifyinginvolvescalculatingcoalescencetimesrandomwalkslargestremeetingtimeremovingedgelikelihoodresultingamplifiertestpairwisenonisomorphicorderthuscoverexpressiblecubicquarticfindsufficientlylargenumbercarryshowcanshareIdentifyingpromotedesigningEvolutionaryRegularTransientAmplifiers

Similar Articles

Cited By