Using Multiple Objective Functions in the Dynamic Model of Metabolic Networks of Escherichia coli

Simulating dynamic behaviors of metabolic networks of a living cell plays an important role in system biology. As a part of dynamic model, the choice of objective function has a large influence on simulation accuracy for these behaviors. In this work, in order to precisely describe the biomass yield and substrate utilization in glycolytic metabolism of Escherichia coli, we try to modify the objective function of the existing dynamic model by using maximization of glucose utilization to replace the traditional objective one. After that, the dynamic model with the new objective is converted to a standard optimal control problem. And then we compute such model through the use of the penalty function methods. The results illustrate that the simulation curves perfectly agree with experiment data, especially with biomass concentration. Thereby, we conclude that completely utilizing substrate glucose is feasible to describe and improve the simulation accuracy on concentrations of some important metabolites in Escherichia coli. The completeness of investigating such models will be helpful and instructive for the application of bioengineering.

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Solving the Dynamic Model of Metabolic Network of Escherichia coli by Adams Methods

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.424-425.900 ◽

2012 ◽

Vol 424-425 ◽

pp. 900-903 ◽

Cited By ~ 1

Author(s):

Qing Hua Zhou ◽

Jing Cui ◽

Juan Xie

Keyword(s):

Escherichia Coli ◽

Metabolic Network ◽

Dynamic Model ◽

Metabolic Networks ◽

Flux Distribution ◽

Penalty Function Methods ◽

E Coli ◽

Adams Methods ◽

Metabolite Concentrations ◽

Simulation Results

We previously analyzed the dynamic flux distribution and predicted glucose, biomass concentrations of metabolic networks ofE. coliby using the penalty function methods. But the consequences were not as well as we expected. In order to improve the predicated accuracy of the metabolite concentrations, instead of using Runge-Kutta algorithm, we apply Adams methods which belong to the multi-step ones in the process that solves the dynamic model of metabolic network ofE. coliand obtain better simulation results on the metabolic concentrations.

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Genome-scale target identification in Escherichia coli for high-titer production of free fatty acids

Nature Communications ◽

10.1038/s41467-021-25243-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Lixia Fang ◽

Jie Fan ◽

Shulei Luo ◽

Yaru Chen ◽

Congya Wang ◽

...

Keyword(s):

Escherichia Coli ◽

Fatty Acids ◽

Free Fatty Acids ◽

Stress Responses ◽

Metabolic Networks ◽

Target Identification ◽

High Titer ◽

Cell Factory ◽

Microbial Cell Factory ◽

Ffa Metabolism

AbstractTo construct a superior microbial cell factory for chemical synthesis, a major challenge is to fully exploit cellular potential by identifying and engineering beneficial gene targets in sophisticated metabolic networks. Here, we take advantage of CRISPR interference (CRISPRi) and omics analyses to systematically identify beneficial genes that can be engineered to promote free fatty acids (FFAs) production in Escherichia coli. CRISPRi-mediated genetic perturbation enables the identification of 30 beneficial genes from 108 targets related to FFA metabolism. Then, omics analyses of the FFAs-overproducing strains and a control strain enable the identification of another 26 beneficial genes that are seemingly irrelevant to FFA metabolism. Combinatorial perturbation of four beneficial genes involving cellular stress responses results in a recombinant strain ihfAL−-aidB+-ryfAM−-gadAH−, producing 30.0 g L−1 FFAs in fed-batch fermentation, the maximum titer in E. coli reported to date. Our findings are of help in rewiring cellular metabolism and interwoven intracellular processes to facilitate high-titer production of biochemicals.

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A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

Mathematical and Computational Applications ◽

10.3390/mca26020027 ◽

2021 ◽

Vol 26 (2) ◽

pp. 27

Author(s):

Alejandro Castellanos-Alvarez ◽

Laura Cruz-Reyes ◽

Eduardo Fernandez ◽

Nelson Rangel-Valdez ◽

Claudia Gómez-Santillán ◽

...

Keyword(s):

Genetic Algorithm ◽

Selective Pressure ◽

Optimization Problems ◽

Region Of Interest ◽

Decision Makers ◽

Multiple Objective ◽

Objective Functions ◽

Multi Objective ◽

Imperfect Knowledge ◽

Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

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Simultaneous Parameters Identifiability and Estimation of anE. coliMetabolic Network Model

BioMed Research International ◽

10.1155/2015/454765 ◽

2015 ◽

Vol 2015 ◽

pp. 1-21 ◽

Cited By ~ 5

Author(s):

Kese Pontes Freitas Alberton ◽

André Luís Alberton ◽

Jimena Andrea Di Maggio ◽

Vanina Gisela Estrada ◽

María Soledad Díaz ◽

...

Keyword(s):

Experimental Data ◽

Escherichia Coli ◽

Metabolic Networks ◽

Initial Conditions ◽

Model Fit ◽

Real Problem ◽

Kinetic Rates ◽

K 12 ◽

Procedure Model

This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of theEscherichia coliK-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

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Model-guided identification of novel gene amplification targets for improving succinate production in Escherichia coli NZN111

Integrative Biology ◽

10.1039/c7ib00077d ◽

2017 ◽

Vol 9 (10) ◽

pp. 830-835 ◽

Cited By ~ 2

Author(s):

Xingxing Jian ◽

Ningchuan Li ◽

Qian Chen ◽

Qiang Hua

Keyword(s):

Escherichia Coli ◽

Metabolic Engineering ◽

Gene Amplification ◽

Metabolic Networks ◽

Computational Analysis ◽

Succinate Production ◽

Novel Gene ◽

Metabolic Models ◽

Genome Scale

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed.

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Artificial Biosynthetic Pathway for an Unnatural Terpenoid with an Iridiumcontaining P450

10.26434/chemrxiv.11955174 ◽

2020 ◽

Author(s):

Jing Huang ◽

Zhennan Liu ◽

brandon bloomer ◽

Douglas Clark ◽

Aindrila Mukhopadhyay ◽

...

Keyword(s):

Escherichia Coli ◽

Synthetic Biology ◽

Chemical Reactions ◽

Biosynthetic Pathway ◽

Metabolic Networks ◽

Complex Molecules ◽

Whole Cells ◽

Porphyrin Complex ◽

Heme Transport ◽

Artificial Metalloenzymes

<div>Synthetic biology enables microbial hosts to produce complex molecules that are</div><div>otherwise produced by organisms that are rare or difficult to cultivate, but the structures of these</div><div>molecules are limited to chemical reactions catalyzed by natural enzymes. The integration of</div><div>artificial metalloenzymes (ArMs) that catalyze abiotic reactions into metabolic networks could</div><div>broaden the cache of molecules produced biosynthetically by microorgansms. We report the</div><div>assembly of an ArM containing an iridium-porphyrin complex in the cytoplasm of a terpene</div><div>producing Escherichia coli by a heterologous heme transport machinery, and insertion of this ArM</div><div>into a natural biosynthetic pathway to produce an unnatural terpenoid. This work shows that</div><div>synthetic biology and synthetic chemistry, incorporated together in whole cells, can produce</div><div>molecules previously inaccessible to nature.</div>

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