File Entry: Identifying Gene Knockout Strategies Using a Hybrid of Bees Algorithm and Flux Balance Analysis For in silico Optimization of Microbial Strains

Created: 2012-05-11 02:15:08      Last updated: 2012-05-11 02:15:09
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Version created on: 2012-05-11 02:15:08

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 Genome-scale metabolic networks reconstructions from different

organisms have become popular in recent years. Genetic engineering is proven to
be able to obtain the desirable phenotypes. Optimization algorithms are
implemented in previous works to identify the effects of gene knockout on the
results. However, the previous works face the problem of falling into local
minima. Thus, a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA)
is proposed in this paper to solve the local minima problem and to predict optimal
sets of gene deletion for maximizing the growth rate of certain metabolite. This
paper involves two case studies that consider the production of succinate and
lactate as targets, by using E.coli as model organism. The results from this
experiment are the list of knockout genes and the growth rate after the deletion.
BAFBA shows better results compared to the other methods. The identified list

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