Literature DB >> 12414184

Genotype-phenotype mapping: genes as computer programs.

Douglas B Kell1.   

Abstract

The effects of genes on phenotype are mediated by processes that are typically unknown but whose determination is desirable. The conversion from gene to phenotype is not a simple function of individual genes, but involves the complex interactions of many genes; it is what is known as a nonlinear mapping problem. A computational method called genetic programming allows the representation of candidate nonlinear mappings in several possible trees. To find the best model, the trees are 'evolved' by processes akin to mutation and recombination, and the trees that more closely represent the actual data are preferentially selected. The result is an improved tree of rules that represent the nonlinear mapping directly. In this way, the encoding of cellular and higher-order activities by genes is seen as directly analogous to computer programs. This analogy is of utility in biological genetics and in problems of genotype-phenotype mapping.

Mesh:

Year:  2002        PMID: 12414184     DOI: 10.1016/s0168-9525(02)02765-8

Source DB:  PubMed          Journal:  Trends Genet        ISSN: 0168-9525            Impact factor:   11.639


  11 in total

Review 1.  Metabolic engineering in the -omics era: elucidating and modulating regulatory networks.

Authors:  Goutham N Vemuri; Aristos A Aristidou
Journal:  Microbiol Mol Biol Rev       Date:  2005-06       Impact factor: 11.056

2.  High-throughput metabolic fingerprinting of legume silage fermentations via Fourier transform infrared spectroscopy and chemometrics.

Authors:  Helen E Johnson; David Broadhurst; Douglas B Kell; Michael K Theodorou; Roger J Merry; Gareth W Griffith
Journal:  Appl Environ Microbiol       Date:  2004-03       Impact factor: 4.792

3.  Discrimination of modes of action of antifungal substances by use of metabolic footprinting.

Authors:  Jess Allen; Hazel M Davey; David Broadhurst; Jem J Rowland; Stephen G Oliver; Douglas B Kell
Journal:  Appl Environ Microbiol       Date:  2004-10       Impact factor: 4.792

Review 4.  Integrated network analysis and effective tools in plant systems biology.

Authors:  Atsushi Fukushima; Shigehiko Kanaya; Kozo Nishida
Journal:  Front Plant Sci       Date:  2014-11-04       Impact factor: 5.753

5.  Nonequilibrium population dynamics of phenotype conversion of cancer cells.

Authors:  Joseph Xu Zhou; Angela Oliveira Pisco; Hong Qian; Sui Huang
Journal:  PLoS One       Date:  2014-12-01       Impact factor: 3.240

6.  Evolutionary algorithms and synthetic biology for directed evolution: commentary on "on the mapping of genotype to phenotype in evolutionary algorithms" by Peter A. Whigham, Grant Dick, and James Maclaurin.

Authors:  Douglas B Kell
Journal:  Genet Program Evolvable Mach       Date:  2017-03-29       Impact factor: 1.714

7.  Mapping and Predicting Non-Linear Brassica rapa Growth Phenotypes Based on Bayesian and Frequentist Complex Trait Estimation.

Authors:  R L Baker; W F Leong; S Welch; C Weinig
Journal:  G3 (Bethesda)       Date:  2018-03-28       Impact factor: 3.154

8.  Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.

Authors:  Steve O'Hagan; Joshua Knowles; Douglas B Kell
Journal:  PLoS One       Date:  2012-11-21       Impact factor: 3.240

9.  A systematic strategy for large-scale analysis of genotype phenotype correlations: identification of candidate genes involved in African trypanosomiasis.

Authors:  Paul Fisher; Cornelia Hedeler; Katherine Wolstencroft; Helen Hulme; Harry Noyes; Stephen Kemp; Robert Stevens; Andrew Brass
Journal:  Nucleic Acids Res       Date:  2007-08-20       Impact factor: 16.971

Review 10.  Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently.

Authors:  Andrew Currin; Neil Swainston; Philip J Day; Douglas B Kell
Journal:  Chem Soc Rev       Date:  2015-03-07       Impact factor: 54.564

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