Modeling the microbial growth of two Escherichia coli strains in a multi-substrate environment

Poccia, María Eugenia; Beccaria, Alejandro José; Dondo, Rodolfo Gabriel

doi:10.1590/0104-6632.20140312s00002587

Cited by 14 publications

(6 citation statements)

References 15 publications

(23 reference statements)

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“…Similar results for the estimates of the yield of glucose per biomass (Y S/X ) are reported in [41,43]. According to [44,45], the values of the parameters µ max and k S are also within acceptable boundaries.…”

Section: Numerical Results and Discussionsupporting

confidence: 79%

Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm

2021

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In this paper, the artificial bee colony (ABC) algorithm is hybridized with the genetic algorithm (GA) for a model parameter identification problem. When dealing with real-world and large-scale problems, it becomes evident that concentrating on a sole metaheuristic algorithm is somewhat restrictive. A skilled combination between metaheuristics or other optimization techniques, a so-called hybrid metaheuristic, can provide more efficient behavior and greater flexibility. Hybrid metaheuristics combine the advantages of one algorithm with the strengths of another. ABC, based on the foraging behavior of honey bees, and GA, based on the mechanics of nature selection, are among the most efficient biologically inspired population-based algorithms. The performance of the proposed ABC-GA hybrid algorithm is examined, including classic benchmark test functions. To demonstrate the effectiveness of ABC-GA for a real-world problem, parameter identification of an Escherichia coli MC4110 fed-batch cultivation process model is considered. The computational results of the designed algorithm are compared to the results of different hybridized biologically inspired techniques (ant colony optimization (ACO) and firefly algorithm (FA))—hybrid algorithms as ACO-GA, GA-ACO and ACO-FA. The algorithms are applied to the same problems—a set of benchmark test functions and the real nonlinear optimization problem. Taking into account the overall searchability and computational efficiency, the results clearly show that the proposed ABC–GA algorithm outperforms the considered hybrid algorithms.

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Section: Numerical Results and Discussionsupporting

confidence: 79%

Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm

2021

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“…The nonlinear constraints were set to have DO, glycerol, and complex nitrogen-rich substrate greater than zero. The lower and upper bounds for model parameters were selected based on the literature [33] , [34] , [35] , [36] , [37] . Model fitting was achieved by minimizing the objective function using the Hybrid Scheme in GA available in MATLAB R2020b (The MathWorks, Natick, MA).…”

Section: Resultsmentioning

confidence: 99%

High cell density cultivation of E. coli in shake flasks for the production of recombinant proteins

Ganjave

Dodia

Sunder

et al. 2022

Biotechnology Reports

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Highlights True fed-batch strategy for high cell density cultivation of E. coli in shake flask. Cybernetic model-based optimization of the feeding recipe. Biomass of 19.9–21.5 g DCW/L, in agreement with the model prediction. Volumetric productivity for tested proteins increased 8–34-fold compared to batch. Scale up of fed-batch recipe to bioreactor resulted in further 2.8-fold increase.

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“…Logistic models have been preferred particularly when PHA production in microbial culture remains nongrowth-associated (Dhanasekar et al, 2001;Elsayed et al, 2018Elsayed et al, , 2019Mahmoudi et al, 2010). Cybernetic models are used to describe the allocation of resources in the cellular system such as biomass into products and enzymes under multi-substrate conditions (Poccia et al, 2014). For example, growth and PHA production from R. eutropha feeding on glucose, fructose, and propionic acid was modelled by considering the synthesis and decay of two specific enzymes (Piccoli et al, 2020).…”

Section: Acetate Ammoniamentioning

confidence: 99%

Structural variability, implementational irregularities in mathematical modelling of polyhydroxyalkanoates (PHAs) production—A state‐of‐the‐art review

Lhamo

Mahanty

2022

Biotech & Bioengineering

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The rate and extent of microbial polyhydroxyalkanoates (PHAs) production rely on the availability of substrates, growth of microbial biomass, and intracellular accumulation of polymer under nitrogen‐limited conditions. The dynamics of PHAs production captured through various structured or unstructured models can be extended to design an optimal feeding strategy for process intensification. Large variability in process assumptions, choices of kinetics, and model complexity is expected depending on substrate(s), microbial metabolism, and discretization of the process under consideration. This communication attempts to review the estimation of stoichiometric yield coefficients, metabolic modelling, and choices of unstructured kinetics in microbial PHA production. Implementational irregularities in parameter estimation and quality check in modelling exercises have also been reviewed. It is observed that the scope of the majority of the “modelling” studies is confined to the estimation of stoichiometric parameters with limited utility. In dynamic models, microbial growth is often described using either Monod or logistic variants, while PHAs production adopts a Luedeking–Piret expression with or without substrate inhibition. Though model selection, regression with experimental data, parameter estimation, and model validation are integral parts of the exercise, very few provide sufficient coverage on all those aspects. Application of the model to control or optimize the bioprocess has rarely been attempted.

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Modeling the microbial growth of two Escherichia coli strains in a multi-substrate environment

Cited by 14 publications

References 15 publications

Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm

Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm

High cell density cultivation of E. coli in shake flasks for the production of recombinant proteins

Structural variability, implementational irregularities in mathematical modelling of polyhydroxyalkanoates (PHAs) production—A state‐of‐the‐art review

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