Epistasis Blog

From the Computational Genetics Laboratory at the University of Pennsylvania (www.epistasis.org)

Wednesday, November 01, 2006

Computational Evolution

Evolutionary computing is a computational intelligence methodology that is inspired by how nature solves problems through evolution by natural selection. We have employed a variety of evolutionary computing methods such as genetic programming in our recent work for modeling epistasis (see last post on SyMod) and for detecting epistasis on a genome-wide scale with MDR (see previous posts in May and June).

A new paper by Banzhaf et al. in Nature Reviews Genetics proposes a new field called computational evolution that builds on past successes with artificial evolution.

Banzhaf W, Beslon G, Christensen S, Foster JA, Kepes F, Lefort V, Miller JF, Radman M, Ramsden JJ. Guidelines: From artificial evolution to computational evolution: a research agenda. Nat Rev Genet. 2006 Sep;7(9):729-35. [PubMed]

Computational scientists have developed algorithms inspired by natural evolution for at least 50 years. These algorithms solve optimization and design problems by building solutions that are 'more fit' relative to desired properties. However, the basic assumptions of this approach are outdated. We propose a research programme to develop a new field: computational evolution. This approach will produce algorithms that are based on current understanding of molecular and evolutionary biology and could solve previously unimaginable or intractable computational and biological problems.

For background reading on evolutionary computing see:

Foster JA. Evolutionary computation. Nat Rev Genet. 2001 Jun;2(6):428-36. [PubMed]

Evolution does not require DNA, or even living organisms. In computer science, the field known as 'evolutionary computation' uses evolution as an algorithmic tool, implementing random variation, reproduction and selection by altering and moving data within a computer. This harnesses the power of evolution as an alternative to the more traditional ways to design software or hardware. Research into evolutionary computation should be of interest to geneticists, as evolved programs often reveal properties - such as robustness and non-expressed DNA - that are analogous to many biological phenomena.

If you are interested in using these methods for genetic analysis you might consider attending the Genetic and Evolutionary Computing Conference (GECCO-2007) next year in London and the European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO) next year in Valencia, Spain. . See my posts below for information about submitting a paper to either conference.


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