Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.
Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.
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DOI：
10.1371/journal.pone.0102744
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Corchado JM
中科院分区：
文献类型：
--
作者：
Choon YW;Mohamad MS;Deris S;Illias RM;Chong CK;Chai LE;Omatu S;Corchado JM
Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.
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影响因子：
3
作者：
Patil, KR;Rocha, I;Förster, J;Nielsen, J
通讯作者：
Nielsen, J
DOI：
10.1038/nrmicro1949
发表时间：
2009-02
期刊：
Nature reviews. Microbiology
影响因子：
--
作者：
通讯作者：
--
影响因子：
3.7
作者：
Soons ZI;Ferreira EC;Patil KR;Rocha I
通讯作者：
Rocha I
影响因子：
6.4
作者：
Pagliardini J;Hubmann G;Alfenore S;Nevoigt E;Bideaux C;Guillouet SE
通讯作者：
Guillouet SE
影响因子：
3
作者：
Rocha, Miguel;Maia, Paulo;Mendes, Rui;Pinto, Jose P.;Ferreira, Eugenio C.;Nielsen, Jens;Patil, Kiran Raosaheb;Rocha, Isabel
通讯作者：
Rocha, Isabel