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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: Optimisation algorithms have been developed to identify the effects of gene knockout.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.

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

The flow for calculating fitness function.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig4: The flow for calculating fitness function.

Mentions: The flow of calculating the fitness function is shown in Figure 4.


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)

The flow for calculating fitness function.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: The flow for calculating fitness function.
Mentions: The flow of calculating the fitness function is shown in Figure 4.

Bottom Line: Optimisation algorithms have been developed to identify the effects of gene knockout.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.

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