A hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to optimize metabolites production in genome-scale models

Arif, Muhammad; Mohamad, Mohd Saberi; Latif, Muhammad Shafie Abd; Deris, Safaai; Remli, Muhammad Akmal; Daud, Kauthar Mohd; Ibrahim, Zuwairie; Omatu, Sigeru; Corchado, Juan M.

doi:10.1016/j.compbiomed.2018.09.015

Cited by 10 publications

(6 citation statements)

References 38 publications

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“…The experimental result obtained from the hybrid of PSOMOMA algorithm is compared with the previous algorithms in enhancing the succinic production and the growth rate of E. coli. This section compares the growth rate and succinic production of E. coli from PSOMOMA with the previous results obtained for CSMOMA, ABCMOMA and also results from the wet laboratory [7], [11]. Table 3 below shows the result obtained for PSOMOMA, CSMOMA and ABCMOMA.…”

Section: Resultsmentioning

confidence: 79%

“…However, there is a possibility that the steady-state obtained by ROOM is hardly being found by the organism. In this research, MOMA is chosen as modeling algorithm considering that FBA assumes the mutant organism is having the same optimal metabolic state as a wild-type organism [7]. Furthermore, MOMA is more suitable to predict the suboptimal flux distribution in mutant organisms.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Optimization-Modelling Methods for Metabolites Production inEscherichia coli

Lee

Mohamad

Choon

et al. 2020

Journal of Integrative Bioinformatics

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The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite’s production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.

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Section: Resultsmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Comparison of Optimization-Modelling Methods for Metabolites Production inEscherichia coli

Lee

Mohamad

Choon

et al. 2020

Journal of Integrative Bioinformatics

Self Cite

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“…Yield (BPCY) as the objective function [31]. The algorithm enhanced the BPCY of succinate and lactate in the metabolic model of E. coli iJR904 to 1.37 and 2.9442, respectively, and the BPCY of succinate in the metabolic model of S.cerevisiae iAZ900 was increased to 0.07231.…”

Section: Minimization Of Metabolic Adjustment (Moma)mentioning

confidence: 99%

Advances and applications of machine learning and intelligent optimization algorithms in genome-scale metabolic network models

Bai¹,

You²,

Zhang

et al. 2022

Syst Microbiol and Biomanuf

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Due to the increasing demand for microbially manufactured products in various industries, it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired products. Recently, with the gradual cross-fertilization between computer science and bioinformatics fields, machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models (GSMMs) based on constrained optimization methods, and many high-quality related works have been published. Therefore, this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering, with special emphasis on GSMMs. Specifically, the development history of GSMMs is first reviewed. Then, the analysis methods of GSMMs based on constraint optimization are presented. Next, this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic models. In addition, the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.

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“…Dynamic flux balance analysis to estimate the maximum theoretical productivity of a batch culture system 2017 [90] E. coli Metabolism-downstream coupled model for metabolic engineering of the strain to produce SA using glycerol substate 2018 [91] E. coli and Z. mobilis Hybrid of differential search algorithm and flux balance analysis to identify knockout relations for enhanced SA production 2018 [92] E. coli Genome-scale metabolic model to predict gene deletion for enhanced SA production using glycerol substrate 2018 [93] E. coli and S. cerevisiae Hybrid of optimization algorithm and genome-scale metabolic models to predict the near-optimal set of gene deletions for overproduction of SA 2018 [94] A. succinogenes…”

Section: E Coli and A Succinogenesmentioning

confidence: 99%

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

et al. 2021

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Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.

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A hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to optimize metabolites production in genome-scale models

Cited by 10 publications

References 38 publications

Comparison of Optimization-Modelling Methods for Metabolites Production inEscherichia coli

Comparison of Optimization-Modelling Methods for Metabolites Production inEscherichia coli

Advances and applications of machine learning and intelligent optimization algorithms in genome-scale metabolic network models

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

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