Paper Info
Title
Simple stochastic birth andz death models of genome evolution: was there enough time for us to evolve?
Abstract
Motivation: The distributions of many genome-associated quantities, including the membership of paralogous gene families can be approximated with power laws. We are interested in developing mathematical models of genome evolution that adequately account for the shape of these distributions and describe the evolutionary dynamics of their formation. Results: We show that simple stochastic models of genome evolution lead to power-law asymptotics of protein domain family size distribution. These models, called Birth, Death and Innovation Models (BDIM), represent a special class of balanced birth-and-death processes, in which domain duplication and deletion rates are asymptotically equal up to the second order. The simplest, linear BDIM shows an excellent fit to the observed distributions of domain family size in diverse prokaryotic and eukaryotic genomes. However, the stochastic version of the linear BDIM explored here predicts that the actual size of large paralogous families is reached on an unrealistically long timescale. We show that introduction of non-linearity, which might be interpreted as interaction of a particular order between individual family members, allows the model to achieve genome evolution rates that are much better compatible with the current estimates of the rates of individual duplication/loss events.
Year
DOI
Venue
2003
10.1093/bioinformatics/btg351
BIOINFORMATICS
Keywords
Field
DocType
power law,stochastic model,genome evolution,protein domains,evolutionary dynamics,mathematical model,second order
Genome,Computer science,Birth–death process,Genome evolution,Stochastic modelling,Bioinformatics,Evolutionary dynamics,Mathematical model,Paralogous Gene,Power law
Journal
Volume
Issue
ISSN
19
15.0
1367-4803
Citations 
PageRank 
References 
9
1.79
3
Authors
3
Name
Order
Citations
PageRank
Georgy P. Karev1244.08
Yuri I. Wolf254076.15
Eugene V. Koonin3986239.69