Comparison of Optimization-Modelling Methods for Metabolites Production in<i>Escherichia coli</i>

Lee, Mee K.; Mohamad, Mohd Saberi; Choon, Yee Wen; Daud, Kauthar Mohd; Nasarudin, Nurul Athirah; Ismail, Mohd Arfian; Ibrahim, Zuwairie; Napis, Suhaimi; Sinnott, Richard

doi:10.1515/jib-2019-0073

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…Experiments showed that ethanol production reached a maximum of 17.2270 mmolh −1 gDW −1 with three genes knocked out, while growth rate reached a maximum of 0.2338 h −1 with five genes knocked out. One year later, Lee et al compared the performance of PSOMOMA with CSMOMA and ABCMOMA for the succinate yield maximization method in E. coli [74]. The results of PSOMOMA were validated with the results of wet-lab experiments.…”

Section: Particle Swarm Optimization Algorithm (Pso)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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Section: Particle Swarm Optimization Algorithm (Pso)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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show abstract

“…Comprehensive carbon metabolism model (375 reactions) to analyze the metabolism and predict knockout strategies for maximum SA production with maintaining the cell growth 2018 [95] A. succinogenes Genome-scale metabolic model to evaluate the metabolic capability of the strain to produce SA under various conditions 2018 [30] Zymomonas mobilis Genome-scale metabolic model to characterize SA-producing capability and comparatively identify gene deletions for enhanced SA production 2018 [96] E. coli Optimization modeling to identify near-optimal knockout genes for the maximum production of SA 2020 [97] Aspergillus niger…”

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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“…Unlike data-driven modeling, optimization- and enumeration-based strategies can be used to investigate and characterize a biochemical network from first principles and at the near-steady state ( Segre et al, 2002 ; Shlomi et al, 2005 ; Wagner and Urbanczik, 2005 ; Urbanczik, 2007 ; Orth et al, 2010 ; Muller and Regensburger, 2016 ; Klamt et al, 2017 ; Klamt et al, 2018 ; Lee et al, 2020 ). Algorithms which assess the flux of a reactant (flux balance analysis, flux variability analysis, regulatory on–off minimization, and minimization of metabolic adjustment) will maximize or minimize the biomass of a metabolite of interest and can be used to investigate the effects of deletions and other perturbations on the flux of metabolites through a large network ( Segre et al, 2002 ; Shlomi et al, 2005 ; Orth et al, 2010 ; Klamt et al, 2018 ; Lee et al, 2020 ). The numerical enumeration of elementary flux modes and vectors, along with extreme pathway analysis, can be used to derive meaningful information about “metabolic” hubs and smaller subsets of cooperating reactions from biochemical networks ( Wagner and Urbanczik, 2005 ; Urbanczik, 2007 ; Muller and Regensburger, 2016 ; Klamt et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

ReDirection: an R-package to compute the probable dissociation constant for every reaction of a user-defined biochemical network

Kundu

2023

Front. Mol. Biosci.

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Biochemical networks integrate enzyme-mediated substrate conversions with non-enzymatic complex formation and disassembly to accomplish complex biochemical and physiological functions. The choice of parameters and constraints used in most of these studies is numerically motivated and network-specific. Although sound in theory, the outcomes that result depart significantly from the intracellular milieu and are less likely to retain relevance in a clinical setting. There is a need for a computational tool which is biochemically relevant, mathematically rigorous, and unbiased, and can ascribe functionality to and generate potentially testable hypotheses for a user-defined biochemical network. Here, we present “ReDirection,” an R-package which computes the probable dissociation constant for every reaction of a biochemical network directly from a null space-generated subspace of the stoichiometry number matrix of the modeled network. “ReDirection” delineates this subspace by excluding all trivial and redundant or duplicate occurrences of non-trivial vectors, combinatorially summing the vectors that remain and verifying that the upper or lower bounds of the sequence of terms formed by each row of this subspace belong to the open real-valued intervals −∞,−1 or 1,∞ or whether the number of terms that are differently signed are almost equal. “ReDirection” iterates these steps until these bounds are consistent and unambiguous for all reactions of the modeled biochemical network. Thereafter, “ReDirection” filters the terms from each row of this subspace, bins them to outcome-specific subsets, sums and maps this to an outcome-specific reaction vector, and computes the p1-norm, which is the probable dissociation constant for a reaction. “ReDirection” works on first principles, does not discriminate between enzymatic and non-enzymatic reactions, offers a biochemically relevant and mathematically rigorous environment to explore user-defined biochemical networks under baseline and perturbed conditions, and can be used to address empirically intractable biochemical problems. The utility and relevance of “ReDirection” are highlighted by numerical studies on stoichiometric number models of biochemical networks of galactose metabolism and heme and cholesterol biosynthesis. “ReDirection” is freely available and accessible from the comprehensive R archive network (CRAN) with the URL (https://cran.r-project.org/package=ReDirection).

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Comparison of Optimization-Modelling Methods for Metabolites Production inEscherichia coli

Cited by 6 publications

References 38 publications

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

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

ReDirection: an R-package to compute the probable dissociation constant for every reaction of a user-defined biochemical network

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