Literature DB >> 16469852

Proceedings of the SMBE Tri-National Young Investigators' Workshop 2005. Mutation rate and the cost of complexity.

Ralph Haygood1.   

Abstract

Two recent theoretical studies of adaptation suggest that more complex organisms tend to adapt more slowly. Specifically, in Fisher's "geometric" model of a finite population where multiple traits are under optimizing selection, the average progress ensuing from a single mutation decreases as the number of traits increases--the "cost of complexity." Here, I draw on molecular and histological data to assess the extent to which on a large phylogenetic scale, this predicted decrease in the rate of adaptation per mutation is mitigated by an increase in the number of mutations per generation as complexity increases. As an index of complexity for multicellular organisms, I use the number of visibly distinct types of cell in the body. Mutation rate is the product of mutational target size and population mutation rate per unit target. Despite much scatter, genome size appears to be positively correlated with complexity (as indexed by cell-type number), which along with other considerations suggests that mutational target size tends to increase with complexity. In contrast, effective population mutation rate per unit target appears to be negatively correlated with complexity. The net result is that mutation rate probably does tend to increase with complexity, although probably not fast enough to eliminate the cost of complexity.

Mesh:

Year:  2006        PMID: 16469852     DOI: 10.1093/molbev/msj104

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  12 in total

1.  Genomic patterns of pleiotropy and the evolution of complexity.

Authors:  Zhi Wang; Ben-Yang Liao; Jianzhi Zhang
Journal:  Proc Natl Acad Sci U S A       Date:  2010-09-27       Impact factor: 11.205

2.  The frailty of adaptive hypotheses for the origins of organismal complexity.

Authors:  Michael Lynch
Journal:  Proc Natl Acad Sci U S A       Date:  2007-05-09       Impact factor: 11.205

3.  A meta-analysis of the genomic and transcriptomic composition of complex life.

Authors:  Ganqiang Liu; John S Mattick; Ryan J Taft
Journal:  Cell Cycle       Date:  2013-06-06       Impact factor: 4.534

4.  The evolution of key cell cycle proteins correlates with an increase in the complexity of eukaryotic organisms.

Authors:  I I Turnaev; K V Gunbin; N A Kolchanov
Journal:  Dokl Biochem Biophys       Date:  2009 May-Jun       Impact factor: 0.788

5.  Pleiotropic mutations are subject to strong stabilizing selection.

Authors:  Katrina McGuigan; Julie M Collet; Scott L Allen; Stephen F Chenoweth; Mark W Blows
Journal:  Genetics       Date:  2014-05-02       Impact factor: 4.562

Review 6.  Genomic mutation rates: what high-throughput methods can tell us.

Authors:  Koodali T Nishant; Nadia D Singh; Eric Alani
Journal:  Bioessays       Date:  2009-09       Impact factor: 4.345

7.  Strong functional patterns in the evolution of eukaryotic genomes revealed by the reconstruction of ancestral protein domain repertoires.

Authors:  Christian M Zmasek; Adam Godzik
Journal:  Genome Biol       Date:  2011-01-17       Impact factor: 13.583

8.  The relationship between proteome size, structural disorder and organism complexity.

Authors:  Eva Schad; Peter Tompa; Hedi Hegyi
Journal:  Genome Biol       Date:  2011-12-19       Impact factor: 13.583

9.  Correcting for differential transcript coverage reveals a strong relationship between alternative splicing and organism complexity.

Authors:  Lu Chen; Stephen J Bush; Jaime M Tovar-Corona; Atahualpa Castillo-Morales; Araxi O Urrutia
Journal:  Mol Biol Evol       Date:  2014-03-27       Impact factor: 16.240

10.  The rate and effects of spontaneous mutation on fitness traits in the social amoeba, Dictyostelium discoideum.

Authors:  David W Hall; Sara Fox; Jennie J Kuzdzal-Fick; Joan E Strassmann; David C Queller
Journal:  G3 (Bethesda)       Date:  2013-07-08       Impact factor: 3.154

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