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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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Comparison between different methods for growth rate and BPCY of succinic acid by E. coli. Note. BPCY is in gram (gram-glucose·hour)−1.
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fig6: Comparison between different methods for growth rate and BPCY of succinic acid by E. coli. Note. BPCY is in gram (gram-glucose·hour)−1.

Mentions: Table 3 shows that the extension of BHFBA performs better than those proposed in previous studies with a growth rate of 0.7988 and BPCY of 0.93656. In addition, Figure 6 shows that the extension of BHFBA obtained the highest value for both growth rate and BPCY among the other methods tested. Knocking out succinate dehydrogenase (SUCD1i) interrupted the conversion of succinic acid to fumarate. By eliminating the conversion of succinic acid to fumarate, the production yield of succinic acid is improved. Next, phosphotransacetylase (PTAr) is removed. According to Burgard et al. [5], these mutants can grow anaerobically on glucose by producing lactate. In the next step, ribulose-5-phosphate-3-epimerase (RPE) is suggested to be knocked out. This knockout involves the inflow reaction of ammonium. As stated in Bohl et al., the utilisation of nitrate as the electron acceptor and ammonium source under anaerobic conditions can improve succinate production [21].


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 growth rate and BPCY of succinic 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

fig6: Comparison between different methods for growth rate and BPCY of succinic acid by E. coli. Note. BPCY is in gram (gram-glucose·hour)−1.
Mentions: Table 3 shows that the extension of BHFBA performs better than those proposed in previous studies with a growth rate of 0.7988 and BPCY of 0.93656. In addition, Figure 6 shows that the extension of BHFBA obtained the highest value for both growth rate and BPCY among the other methods tested. Knocking out succinate dehydrogenase (SUCD1i) interrupted the conversion of succinic acid to fumarate. By eliminating the conversion of succinic acid to fumarate, the production yield of succinic acid is improved. Next, phosphotransacetylase (PTAr) is removed. According to Burgard et al. [5], these mutants can grow anaerobically on glucose by producing lactate. In the next step, ribulose-5-phosphate-3-epimerase (RPE) is suggested to be knocked out. This knockout involves the inflow reaction of ammonium. As stated in Bohl et al., the utilisation of nitrate as the electron acceptor and ammonium source under anaerobic conditions can improve succinate production [21].

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