title: Mathematical modeling for functional divergence after gene duplication.
abstract: In this paper, I present a statistical framework for modeling the functional divergence after gene duplication. A rate-component model to describe the rate covariation among homologous genes of a gene family is implemented when a phylogenetic tree is known. The Markov chain model is rigorous but may require a huge amount of computational time when the number of sequences is large. On the other hand, the Poisson-based model is mathematically analytical so that computation is very fast even for a large dataset. Moreover, under the posterior framework, we have developed a site-specific profile for predicting important amino acid residues responsible for these functional differences between member genes of a gene family. Our study may have great potential for functional genomics because it is cost-effective, and these predictions can be further tested by biological experimentation.
authors.affiliation: Department of Zoology/Genetics, Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. xgu@iastate.edu
authors.forename: X
authors.initials: X
authors.lastname: Gu
mesh_terms: D005075:Biological Evolution; D020440:Gene Duplication; D016013:Likelihood Functions; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D016012:Poisson Distribution