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Mathematical modeling for functional divergence after gene duplication.

X Gu
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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.

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Biological EvolutionGene DuplicationLikelihood FunctionsMarkov ChainsModels, GeneticModels, StatisticalModels, TheoreticalPoisson DistributionJournal ArticleResearch Support, U.S. Gov't, P.H.S.

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X Gu. 2001. Mathematical modeling for functional divergence after gene duplication.. https://doi.org/10.1089/10665270152530827

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