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Gene knockout identification using an extension of Bees Hill Flux Balance Analysis.

Choon YW, Mohamad MS, Deris S, Chong CK, Omatu S, Corchado JM - Biomed Res Int (2015)

Bottom Line: However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging.This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically.Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes.

View Article: PubMed Central - PubMed

Affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.

ABSTRACT
Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes.

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Related in: MedlinePlus

BAFBA flowchart. Note. Red-dotted box is Flux Balance Analysis which is hybridized into standard BA as an objective function in order to predict the effect of gene knockout.
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fig1: BAFBA flowchart. Note. Red-dotted box is Flux Balance Analysis which is hybridized into standard BA as an objective function in order to predict the effect of gene knockout.

Mentions: Figure 1 shows the flow of the BAFBA. The BAFBA is initialised by mimicking a population of bees. In identifying gene knockout, a bee is represented by a binary variable to indicate the absence or the presence of genes in the reaction. In this study, the BAFBA is started with the bees being placed randomly in the search space. The fitness of the sites visited by the bees is evaluated using the FBA. Bees with the highest fitness would be denoted as “selected bees” and the sites they visited would be chosen for a neighbourhood search. A small amount of “selected bees” was expected to encourage local exploitation. After many tests, we found that an appropriate maximum “selected bees” was (1/4) ×  n. We chose and limited the amount of selected bees within the range [1, (1/4) ×  n] to prevent the selection of too many sites for a neighbourhood search. Each bee was required to go through this repetitive local search neighbourhood procedure until the best possible answer was obtained. Meanwhile, the remaining bees were assigned randomly to search for new potential solutions.


Gene knockout identification using an extension of Bees Hill Flux Balance Analysis.

Choon YW, Mohamad MS, Deris S, Chong CK, Omatu S, Corchado JM - Biomed Res Int (2015)

BAFBA flowchart. Note. Red-dotted box is Flux Balance Analysis which is hybridized into standard BA as an objective function in order to predict the effect of gene knockout.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4385639&req=5

fig1: BAFBA flowchart. Note. Red-dotted box is Flux Balance Analysis which is hybridized into standard BA as an objective function in order to predict the effect of gene knockout.
Mentions: Figure 1 shows the flow of the BAFBA. The BAFBA is initialised by mimicking a population of bees. In identifying gene knockout, a bee is represented by a binary variable to indicate the absence or the presence of genes in the reaction. In this study, the BAFBA is started with the bees being placed randomly in the search space. The fitness of the sites visited by the bees is evaluated using the FBA. Bees with the highest fitness would be denoted as “selected bees” and the sites they visited would be chosen for a neighbourhood search. A small amount of “selected bees” was expected to encourage local exploitation. After many tests, we found that an appropriate maximum “selected bees” was (1/4) ×  n. We chose and limited the amount of selected bees within the range [1, (1/4) ×  n] to prevent the selection of too many sites for a neighbourhood search. Each bee was required to go through this repetitive local search neighbourhood procedure until the best possible answer was obtained. Meanwhile, the remaining bees were assigned randomly to search for new potential solutions.

Bottom Line: However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging.This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically.Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes.

View Article: PubMed Central - PubMed

Affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.

ABSTRACT
Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes.

Show MeSH
Related in: MedlinePlus