CamOptimus

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorCankorur-Cetinkaya, Ayca
dc.contributor.authorDias, Joao M. L.
dc.contributor.authorKludas, Jana
dc.contributor.authorSlater, Nigel K. H.
dc.contributor.authorRousu, Juho
dc.contributor.authorOliver, Stephen G.
dc.contributor.authorDikicioglu, Duygu
dc.contributor.departmentUniversity of Cambridge
dc.contributor.departmentWellcome Trust Sanger Institute
dc.contributor.departmentDepartment of Computer Science
dc.date.accessioned2017-08-03T12:10:53Z
dc.date.available2017-08-03T12:10:53Z
dc.date.issued2017
dc.description.abstractMultiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple‐to‐use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).en
dc.description.versionPeer revieweden
dc.format.extent11
dc.format.extent829-839
dc.format.mimetypeapplication/pdf
dc.identifier.citationCankorur-Cetinkaya , A , Dias , J M L , Kludas , J , Slater , N K H , Rousu , J , Oliver , S G & Dikicioglu , D 2017 , ' CamOptimus : a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology ' , MICROBIOLOGY , vol. 163 , no. 6 , pp. 829-839 . https://doi.org/10.1099/mic.0.000477en
dc.identifier.doi10.1099/mic.0.000477
dc.identifier.issn1350-0872
dc.identifier.issn1465-2080
dc.identifier.otherPURE UUID: da69d854-1ea4-4f8a-a19b-cbd8123d1c6e
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/da69d854-1ea4-4f8a-a19b-cbd8123d1c6e
dc.identifier.otherPURE LINK: http://mic.microbiologyresearch.org/content/journal/micro/10.1099/mic.0.000477.v1
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/14290117/829_micro000477.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/27429
dc.identifier.urnURN:NBN:fi:aalto-201708036397
dc.language.isoenen
dc.relation.ispartofseriesMICROBIOLOGYen
dc.relation.ispartofseriesVolume 163, issue 6en
dc.rightsopenAccessen
dc.subject.keywordEvolutionary algorithms
dc.subject.keywordExperimental design tool
dc.subject.keywordGenetic algorithm
dc.subject.keywordPichia pastoris
dc.subject.keywordRecombinant protein production
dc.subject.keywordSymbolic regression
dc.titleCamOptimusen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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