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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

Comparison between different methods for production of lactic acid by E. coli. Note. BPCY is in gram (gram-glucose·hour)−1.
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fig7: Comparison between different methods for production of lactic acid by E. coli. Note. BPCY is in gram (gram-glucose·hour)−1.

Mentions: Table 4 shows the results of the extension of BHFBA and previous works. The extension of BHFBA resulted in a better growth rate and BPCY than the previous works, which are 0.62501 and 5.2241, respectively. Figure 7 shows the comparison among the methods of producing lactic acid in E. coli. The extension of BHFBA shows a drastic difference in the value of BPCY and a small improvement in the growth rate. The deletion of fructose bisphosphatase and phosphoglycerate kinase decreased the efficiency of gluconeogenesis, which resulted in an increased concentration of phosphoenolpyruvate. Phosphoenolpyruvate was then converted into pyruvate and then lactic acid. Knocking out acetaldehyde dehydrogenase, which catalyses the conversion of acetaldehyde into acetic acid, eliminated the competing product, acetic acid. In consequence, the yield of lactic acid is improved.


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)

Comparison between different methods for production of lactic acid by E. coli. Note. BPCY is in gram (gram-glucose·hour)−1.
© Copyright Policy
Related In: Results  -  Collection

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

fig7: Comparison between different methods for production of lactic acid by E. coli. Note. BPCY is in gram (gram-glucose·hour)−1.
Mentions: Table 4 shows the results of the extension of BHFBA and previous works. The extension of BHFBA resulted in a better growth rate and BPCY than the previous works, which are 0.62501 and 5.2241, respectively. Figure 7 shows the comparison among the methods of producing lactic acid in E. coli. The extension of BHFBA shows a drastic difference in the value of BPCY and a small improvement in the growth rate. The deletion of fructose bisphosphatase and phosphoglycerate kinase decreased the efficiency of gluconeogenesis, which resulted in an increased concentration of phosphoenolpyruvate. Phosphoenolpyruvate was then converted into pyruvate and then lactic acid. Knocking out acetaldehyde dehydrogenase, which catalyses the conversion of acetaldehyde into acetic acid, eliminated the competing product, acetic acid. In consequence, the yield of lactic acid is improved.

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