000131225 001__ 131225
000131225 005__ 20240206154529.0
000131225 0247_ $$2doi$$a10.1016/j.biosystems.2015.01.007
000131225 0248_ $$2sideral$$a89514
000131225 037__ $$aART-2015-89514
000131225 041__ $$aeng
000131225 100__ $$aAlcalde Cuesta, F.
000131225 245__ $$aFast and asymptotic computation of the fixation probability for Moran processes on graphs
000131225 260__ $$c2015
000131225 5060_ $$aAccess copy available to the general public$$fUnrestricted
000131225 5203_ $$aEvolutionary dynamics has been classically studied for homogeneous populations, but now there is a growing interest in the non-homogeneous case. One of the most important models has been proposed in Lieberman et al. (2005), adapting to a weighted directed graph the process described in Moran (1958). The Markov chain associated with the graph can be modified by erasing all non-trivial loops in its state space, obtaining the so-called Embedded Markov chain (EMC). The fixation probability remains unchanged, but the expected time to absorption (fixation or extinction) is reduced. In this paper, we shall use this idea to compute asymptotically the average fixation probability for complete bipartite graphs Kn,m. To this end, we firstly review some recent results on evolutionary dynamics on graphs trying to clarify some points. We also revisit the ‘Star Theorem’ proved in Lieberman et al. (2005) for the star graphs K1,m. Theoretically, EMC techniques allow fast computation of the fixation probability, but in practice this is not always true. Thus, in the last part of the paper, we compare this algorithm with the standard Monte Carlo method for some kind of complex networks.
000131225 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E15$$9info:eu-repo/grantAgreement/ES/MICINN/MTM2010-15471
000131225 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000131225 590__ $$a1.495$$b2015
000131225 591__ $$aBIOLOGY$$b40 / 85 = 0.471$$c2015$$dQ2$$eT2
000131225 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b28 / 56 = 0.5$$c2015$$dQ2$$eT2
000131225 592__ $$a0.602$$b2015
000131225 593__ $$aApplied Mathematics$$c2015$$dQ2
000131225 593__ $$aBiochemistry, Genetics and Molecular Biology (miscellaneous)$$c2015$$dQ2
000131225 593__ $$aMedicine (miscellaneous)$$c2015$$dQ2
000131225 593__ $$aModeling and Simulation$$c2015$$dQ2
000131225 593__ $$aStatistics and Probability$$c2015$$dQ3
000131225 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000131225 700__ $$aGonzález Sequeiros, P.
000131225 700__ $$0(orcid)0000-0002-1184-5901$$aLozano Rojo, Á.
000131225 773__ $$g129 (2015), 25-35$$pBiosystems$$tBioSystems$$x0303-2647
000131225 8564_ $$s1796081$$uhttps://zaguan.unizar.es/record/131225/files/texto_completo.pdf$$yPostprint
000131225 8564_ $$s1507832$$uhttps://zaguan.unizar.es/record/131225/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000131225 909CO $$ooai:zaguan.unizar.es:131225$$particulos$$pdriver
000131225 951__ $$a2024-02-06-14:51:42
000131225 980__ $$aARTICLE