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dc.contributor.author
Anisimova, Maria
dc.date.accessioned
2017-10-27T08:15:49Z
dc.date.available
2017-06-11T17:24:56Z
dc.date.available
2017-10-27T08:15:49Z
dc.date.issued
2015-05-01
dc.identifier.issn
1471-2148
dc.identifier.other
10.1186/s12862-015-0352-y
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/100864
dc.identifier.doi
10.3929/ethz-b-000100864
dc.description.abstract
Background Today computational molecular evolution is a vibrant research field that benefits from the availability of large and complex new generation sequencing data – ranging from full genomes and proteomes to microbiomes, metabolomes and epigenomes. The grounds for this progress were established long before the discovery of the DNA structure. Specifically, Darwin’s theory of evolution by means of natural selection not only remains relevant today, but also provides a solid basis for computational research with a variety of applications. But a long-term progress in biology was ensured by the mathematical sciences, as exemplified by Sir R. Fisher in early 20th century. Now this is true more than ever: The data size and its complexity require biologists to work in close collaboration with experts in computational sciences, modeling and statistics. Results Natural selection drives function conservation and adaptation to emerging pathogens or new environments; selection plays key role in immune and resistance systems. Here I focus on computational methods for evaluating selection in molecular sequences, and argue that they have a high potential for applications. Pharma and biotech industries can successfully use this potential, and should take the initiative to enhance their research and development with state of the art bioinformatics approaches. Conclusions This review provides a quick guide to the current computational approaches that apply the evolutionary principles of natural selection to real life problems – from drug target validation, vaccine design and protein engineering to applications in agriculture, ecology and conservation.
en_US
dc.format
application/pdf
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Adaptation
en_US
dc.subject
Applied bioinformatics
en_US
dc.subject
Conservation
en_US
dc.subject
Drug target
en_US
dc.subject
Immune response
en_US
dc.subject
Modeling
en_US
dc.subject
Molecular evolution
en_US
dc.subject
Resistance
en_US
dc.subject
Selection
en_US
dc.title
Darwin and Fisher meet at biotech: on the potential of computational molecular evolution in industry
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2015-05-01
ethz.journal.title
BMC Evolutionary Biology
ethz.journal.volume
15
en_US
ethz.journal.abbreviated
BMC evol. biol.
ethz.pages.start
76
en_US
ethz.size
9 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.code.ddc
DDC - DDC::5 - Science::570 - Life sciences
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.scopus
ethz.identifier.nebis
004100194
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-11T17:25:13Z
ethz.source
ECIT
ethz.identifier.importid
imp5936532c3eb3414483
ethz.ecitpid
pub:158373
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-10-27T08:15:51Z
ethz.rosetta.lastUpdated
2020-02-15T08:28:16Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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