Kinetic modeling of cell metabolism for microbial production

Costa, Rafael S.; Hartmann, András; Vinga, Susana

doi:10.1016/j.jbiotec.2015.12.023

Cited by 45 publications

(21 citation statements)

References 184 publications

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“…This is the first time this method is applied to enhance performance of a metabolic pathway kinetic model. In other studies found in the literature, the unknown kinetic constants are parameterized or the kinetic equations can be approximated 36 , 45 – 47 . The present approach has some major advantages as it needs less parameters than the white-box approach, and it uses simplified kinetic equations that are biochemically plausible.…”

Section: Discussionmentioning

confidence: 99%

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Lo-Thong

Charton

Cadet³

et al. 2020

Sci Rep

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Materials and methods Second part of glycolysis experimental data. Experimental data of PGAM, ENO and PPDK activities and pathway flux (J obs) are obtained from plots of a previous study 8. The free online software WebPlotDigitizer (Version 4.1, https ://autom eris.io/WebPl otDig itize r/) is used to extract data from plots. These data are available in Tables S1 and S2. Artificial neural networks (ANNs). ANNs functioning mimics that of biological neurons, the networks consist of many layers allowing input reception and processing and output delivery. This technique can be used for solving classification or regression problems 18. To build the second part of glycolysis in ANNs, different types of software are employed: RStudio (Version 1.1.456), an open-source integrated development environment for R 19 and two packages: NeuralNet (Version 1.44.2) and Nnet (Version 7.3-12) 20,21. Complex pathway SImulator (COPASI) metabolic networks. A first metabolic network of the studied pathway was kindly provided by the authors of a previous study 8. This model is developed on GEPASI 22 , an old software for metabolic pathway modeling, replaced by COPASI since 2002. The second part of the glycolysis is also modeled by using the open source software called COPASI (Version 4.24) 23. This software is used for metabolic network design, analysis and optimization. The resulting metabolic networks are based on the use of enzyme properties (kinetic parameters and mechanism-based rate equations). Ethics approval and consent to participate. Not applicable. Methodology Black-white-and grey-box approach procedure. To conduct the present study, a specific methodol

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Section: Discussionmentioning

confidence: 99%

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Lo-Thong

Charton

Cadet³

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Another shortage is the exclusion of metabolite concentrations, enzyme levels, and metabolic regulation. Thus, those models cannot predict the complex dynamic cellular responses to environmental and genetic perturbations (Costa, Hartmann, & Vinga, ). Mechanistic‐based kinetic models, alternatively, incorporate enzyme kinetics and metabolic regulation, and enable to quantify optimal enzyme levels and metabolite concentrations required to enhance the productive capabilities.…”

Section: Discussionmentioning

confidence: 99%

Quantitative intracellular flux modeling and applications in biotherapeutic development and production using CHO cell cultures

Huang

Lee

Yoon

2017

Biotech & Bioengineering

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Chinese hamster ovary (CHO) cells have been widely used for producing many recombinant therapeutic proteins. Constraint-based modeling, such as flux balance analysis (FBA) and metabolic flux analysis (MFA), has been developing rapidly for the quantification of intracellular metabolic flux distribution at a systematic level. Such methods would produce detailed maps of flows through metabolic networks, which contribute significantly to better understanding of metabolism in cells. Although these approaches have been extensively established in microbial systems, their application to mammalian cells is sparse. This review brings together the recent development of constraint-based models and their applications in CHO cells. The further development of constraint-based modeling approaches driven by multi-omics datasets is discussed, and a framework of potential modeling application in cell culture engineering is proposed. Improved cell culture system understanding will enable robust developments in cell line and bioprocess engineering thus accelerating consistent process quality control in biopharmaceutical manufacturing.

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“…In brief, constraint-based modelling incorporates information about reaction stoichiometry and thermodynamics [1], while kinetic modelling requires adding kinetic parameters and regulatory mechanisms in addition to what is required in constraint-based modelling [2][3][4]. As a consequence, kinetic modelling precisely describes the dynamic behavior of a system.…”

Section: Metabolic Modelling Approachesmentioning

confidence: 99%

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

et al. 2020

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Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

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Kinetic modeling of cell metabolism for microbial production

Cited by 45 publications

References 184 publications

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Quantitative intracellular flux modeling and applications in biotherapeutic development and production using CHO cell cultures

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

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