TY - JOUR
T1 - A hybrid of Bees algorithm and regulatory on/off minimization for optimizing lactate and succinate production
AU - Yong, Mohd Izzat
AU - Mohamad, Mohd Saberi
AU - Choon, Yee Wen
AU - Chan, Weng Howe
AU - Adli, Hasyiya Karimah
AU - Syazwan Wsw, Khairul Nizar
AU - Yusoff, Nooraini
AU - Remli, Muhammad Akmal
N1 - Publisher Copyright:
© 2022 the author(s), published by De Gruyter, Berlin/Boston.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - Metabolic engineering has expanded in importance and employment in recent years and is now extensively applied particularly in the production of biomass from microbes. Metabolic network models have been employed extravagantly in computational processes developed to enhance metabolic production and suggest changes in organisms. The crucial issue has been the unrealistic flux distribution presented in prior work on rational modelling framework adopting Optknock and OptGene. In order to address the problem, a hybrid of Bees Algorithm and Regulatory On/Off Minimization (BAROOM) is used. By employing Escherichia coli as the model organism, the most excellent set of genes in E. coli that can be removed and advance the production of succinate can be decided. Evidences shows that BAROOM outperforms alternative strategies used to escalate in succinate production in model organisms like E. coli by selecting the best set of genes to be removed.
AB - Metabolic engineering has expanded in importance and employment in recent years and is now extensively applied particularly in the production of biomass from microbes. Metabolic network models have been employed extravagantly in computational processes developed to enhance metabolic production and suggest changes in organisms. The crucial issue has been the unrealistic flux distribution presented in prior work on rational modelling framework adopting Optknock and OptGene. In order to address the problem, a hybrid of Bees Algorithm and Regulatory On/Off Minimization (BAROOM) is used. By employing Escherichia coli as the model organism, the most excellent set of genes in E. coli that can be removed and advance the production of succinate can be decided. Evidences shows that BAROOM outperforms alternative strategies used to escalate in succinate production in model organisms like E. coli by selecting the best set of genes to be removed.
KW - artificial intelligence
KW - bioinformatics
KW - data science
KW - metabolic engineering
KW - modelling
KW - optimization
UR - http://www.scopus.com/inward/record.url?scp=85139379735&partnerID=8YFLogxK
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U2 - 10.1515/jib-2022-0003
DO - 10.1515/jib-2022-0003
M3 - Article
C2 - 35852123
AN - SCOPUS:85139379735
SN - 1613-4516
VL - 19
JO - Journal of integrative bioinformatics
JF - Journal of integrative bioinformatics
IS - 3
ER -