TY - GEN
T1 - A Hybrid of Bat Algorithm and Minimization of Metabolic Adjustment for Succinate and Lactate Production
AU - Man, Mei Yen
AU - Mohamad, Mohd Saberi
AU - Choon, Yee Wen
AU - Ismail, Mohd Arfian
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - The elementary concept of metabolic engineering is to manipulate those potential genes to augment the yields of metabolite production by restructuring and deregulating the metabolic networks. However, the yields of biochemical products are far below their theoretical maximums, although many conventional methods that have been introduced. These conventional methods had encountered problems in getting stuck at a local minimum. This paper proposes a hybrid of the Bat Algorithm (BAT) and the Minimization of Metabolic Adjustment (MOMA) to predict an ideal set of solutions in order to improve the production of succinate and lactate. The dataset utilized in this paper was the Escherichia coli core model, which is the subset of the iAF1260 E. coli metabolic network. The experimental results include the production rate, growth rate, and a list of knockout genes. From the comparative analysis, BATMOMA has better performance in terms of production rate compared to previous works, proving that it has the potential for resolving genetic engineering problems.
AB - The elementary concept of metabolic engineering is to manipulate those potential genes to augment the yields of metabolite production by restructuring and deregulating the metabolic networks. However, the yields of biochemical products are far below their theoretical maximums, although many conventional methods that have been introduced. These conventional methods had encountered problems in getting stuck at a local minimum. This paper proposes a hybrid of the Bat Algorithm (BAT) and the Minimization of Metabolic Adjustment (MOMA) to predict an ideal set of solutions in order to improve the production of succinate and lactate. The dataset utilized in this paper was the Escherichia coli core model, which is the subset of the iAF1260 E. coli metabolic network. The experimental results include the production rate, growth rate, and a list of knockout genes. From the comparative analysis, BATMOMA has better performance in terms of production rate compared to previous works, proving that it has the potential for resolving genetic engineering problems.
KW - Artificial intelligence
KW - Bat Algorithm
KW - Bioinformatics
KW - Escherichia coli
KW - Gene knockout strategy
KW - Lactate
KW - Metabolic engineering
KW - Minimization of Metabolic Adjustment
KW - Succinate
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U2 - 10.1007/978-3-030-54568-0_17
DO - 10.1007/978-3-030-54568-0_17
M3 - Conference contribution
AN - SCOPUS:85089246986
SN - 9783030545673
T3 - Advances in Intelligent Systems and Computing
SP - 166
EP - 175
BT - Practical Applications of Computational Biology and Bioinformatics, 14th International Conference, PACBB 2020
A2 - Panuccio, Gabriella
A2 - Rocha, Miguel
A2 - Fdez-Riverola, Florentino
A2 - Mohamad, Mohd Saberi
A2 - Casado-Vara, Roberto
PB - Springer
T2 - 14th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2020
Y2 - 17 June 2020 through 19 June 2020
ER -