Natural computation meta-heuristics for the in silico optimization of microbial strains

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Search: onr:"swepub:oai:research.chalmers.se:d8f8418f-c702-49b2-9859-83513023c14a" > Natural computation...

1 of 1
Previous record
Next record
To hitlist

Natural computation meta-heuristics for the in silico optimization of microbial strains

Rocha, M. (author): Universidade do Minho,University of Minho

Maia, P. (author): Universidade do Minho,University of Minho

Mendes, R. (author): Universidade do Minho,University of Minho

Pinto, J. P. (author): Universidade do Minho,University of Minho

Ferreira, E. C. (author): Universidade do Minho,University of Minho

Nielsen, Jens B, 1962 (author): Chalmers tekniska högskola,Chalmers University of Technology

Patil, K. R. (author): Danmarks Tekniske Universitet,Technical University of Denmark

Rocha, I. (author): Universidade do Minho,University of Minho

show less...

(creator_code:org_t)

2008-11-27
2008
English.
In: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 9, s. 499-

Related links:: http://publications.... (primary) (free); show more...; https://bmcbioinform...; https://doi.org/10.1...; https://research.cha...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

Background: One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/ productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for in silico metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution. Results: This work reports on improved EAs, as well as novel Simulated Annealing (SA) algorithms to address the task of in silico metabolic engineering. Both approaches use a variable size set-based representation, thereby allowing the automatic finding of the best number of gene deletions necessary for achieving a given productivity goal. The work presents extensive computational experiments, involving four case studies that consider the production of succinic and lactic acid as the targets, by using S. cerevisiae and E. coli as model organisms. The proposed algorithms are able to reach optimal/ near-optimal solutions regarding the production of the desired compounds and presenting low variability among the several runs. Conclusion: The results show that the proposed SA and EA both perform well in the optimization task. A comparison between them is favourable to the SA in terms of consistency in obtaining optimal solutions and faster convergence. In both cases, the use of variable size representations allows the automati c discovery of the approximate number of gene deletions, without compromising the optimality of the solutions. © 2008 Rocha et al; licensee BioMed Central Ltd.

Subject headings

NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
NATURVETENSKAP -- Kemi (hsv//swe)
NATURAL SCIENCES -- Chemical Sciences (hsv//eng)

Publication and Content Type

art (subject category)
ref (subject category)

Find in a library

BMC Bioinformatics (Search for host publication in LIBRIS)

To the university's database

1 of 1
Previous record
Next record
To hitlist

Find more in SwePub

By the author/editor: Rocha, M.; Maia, P.; Mendes, R.; Pinto, J. P.; Ferreira, E. C.; Nielsen, Jens B, ...; show more...; Patil, K. R.; Rocha, I.; show less...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Biological Scien ...; and Bioinformatics a ...

NATURAL SCIENCES: NATURAL SCIENCES; and Chemical Science ...

Articles in the publication: BMC Bioinformati ...

By the university: Chalmers University of Technology

Search outside SwePub

Extend your search to:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se