Linked Life Data

19073195

Source:http://linkedlifedata.com/resource/pubmed/id/19073195

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2009-2-13
pubmed:abstractText
We model the process of directed evolution (DE) in silico using genetic algorithms. Making use of the NK fitness landscape model, we analyse the effects of mutation rate, crossover and selection pressure on the performance of DE. A range of values of K, the epistatic interaction of the landscape, are considered, and high- and low-throughput modes of evolution are compared. Our findings suggest that for runs of or around ten generations' duration-as is typical in DE-there is little difference between the way in which DE needs to be configured in the high- and low-throughput regimes, nor across different degrees of landscape epistasis. In all cases, a high selection pressure (but not an extreme one) combined with a moderately high mutation rate works best, while crossover provides some benefit but only on the less rugged landscapes. These genetic algorithms were also compared with a "model-based approach" from the literature, which uses sequential fixing of the problem parameters based on fitting a linear model. Overall, we find that purely evolutionary techniques fare better than do model-based approaches across all but the smoothest landscapes.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1095-8541
pubmed:author
pubmed:issnType
Electronic
pubmed:day
7
pubmed:volume
257
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
131-41
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
In silico modelling of directed evolution: Implications for experimental design and stepwise evolution.
pubmed:affiliation
Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, M1 7ND, UK. david.wedge@manchester.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't