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Jiménez del Val I, Kyriakopoulos S, Albrecht S, Stockmann H, Rudd PM, Polizzi KM, Kontoravdi C. CHOmpact: A reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability. Biotechnol Bioeng 2023; 120:2479-2493. [PMID: 37272445 PMCID: PMC10952303 DOI: 10.1002/bit.28459] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 06/06/2023]
Abstract
Metabolic modeling has emerged as a key tool for the characterization of biopharmaceutical cell culture processes. Metabolic models have also been instrumental in identifying genetic engineering targets and developing feeding strategies that optimize the growth and productivity of Chinese hamster ovary (CHO) cells. Despite their success, metabolic models of CHO cells still present considerable challenges. Genome-scale metabolic models (GeMs) of CHO cells are very large (>6000 reactions) and are difficult to constrain to yield physiologically consistent flux distributions. The large scale of GeMs also makes the interpretation of their outputs difficult. To address these challenges, we have developed CHOmpact, a reduced metabolic network that encompasses 101 metabolites linked through 144 reactions. Our compact reaction network allows us to deploy robust, nonlinear optimization and ensure that the computed flux distributions are physiologically consistent. Furthermore, our CHOmpact model delivers enhanced interpretability of simulation results and has allowed us to identify the mechanisms governing shifts in the anaplerotic consumption of asparagine and glutamate as well as an important mechanism of ammonia detoxification within mitochondria. CHOmpact, thus, addresses key challenges of large-scale metabolic models and will serve as a platform to develop dynamic metabolic models for the control and optimization of biopharmaceutical cell culture processes.
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Affiliation(s)
| | - Sarantos Kyriakopoulos
- Manufacturing Science and TechnologyBioMarin PharmaceuticalCorkIrelandIreland
- Present address:
Drug Product DevelopmentJanssen PharmaceuticalsSchaffhausenSwitzerland
| | - Simone Albrecht
- GlycoScience GroupNational Institute for Bioprocessing Research and TrainingDublinIreland
| | - Henning Stockmann
- GlycoScience GroupNational Institute for Bioprocessing Research and TrainingDublinIreland
| | - Pauline M. Rudd
- GlycoScience GroupNational Institute for Bioprocessing Research and TrainingDublinIreland
- Present address:
Bioprocessing Technology InstituteAgency for Science, Technology and Research (A*STAR)SingaporeSingapore
| | - Karen M. Polizzi
- Department of Chemical EngineeringImperial College LondonLondonUK
| | - Cleo Kontoravdi
- Department of Chemical EngineeringImperial College LondonLondonUK
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2
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Copeland CA, Olenchock BA, Ziehr D, McGarrity S, Leahy K, Young JD, Loscalzo J, Oldham WM. MYC overrides HIF-1α to regulate proliferating primary cell metabolism in hypoxia. eLife 2023; 12:e82597. [PMID: 37428010 DOI: 10.7554/elife.82597] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 06/27/2023] [Indexed: 07/11/2023] Open
Abstract
Hypoxia requires metabolic adaptations to sustain energetically demanding cellular activities. While the metabolic consequences of hypoxia have been studied extensively in cancer cell models, comparatively little is known about how primary cell metabolism responds to hypoxia. Thus, we developed metabolic flux models for human lung fibroblast and pulmonary artery smooth muscle cells proliferating in hypoxia. Unexpectedly, we found that hypoxia decreased glycolysis despite activation of hypoxia-inducible factor 1α (HIF-1α) and increased glycolytic enzyme expression. While HIF-1α activation in normoxia by prolyl hydroxylase (PHD) inhibition did increase glycolysis, hypoxia blocked this effect. Multi-omic profiling revealed distinct molecular responses to hypoxia and PHD inhibition, and suggested a critical role for MYC in modulating HIF-1α responses to hypoxia. Consistent with this hypothesis, MYC knockdown in hypoxia increased glycolysis and MYC over-expression in normoxia decreased glycolysis stimulated by PHD inhibition. These data suggest that MYC signaling in hypoxia uncouples an increase in HIF-dependent glycolytic gene transcription from glycolytic flux.
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Affiliation(s)
- Courtney A Copeland
- Department of Medicine, Brigham and Women's Hospital, Boston, United States
- Department of Medicine, Harvard Medical School, Boston, United States
| | - Benjamin A Olenchock
- Department of Medicine, Brigham and Women's Hospital, Boston, United States
- Department of Medicine, Harvard Medical School, Boston, United States
| | - David Ziehr
- Department of Medicine, Brigham and Women's Hospital, Boston, United States
- Department of Medicine, Harvard Medical School, Boston, United States
- Department of Medicine, Massachusetts General Hospital, Boston, United States
| | - Sarah McGarrity
- Department of Medicine, Brigham and Women's Hospital, Boston, United States
- Department of Medicine, Harvard Medical School, Boston, United States
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Kevin Leahy
- Department of Medicine, Brigham and Women's Hospital, Boston, United States
- Department of Medicine, Harvard Medical School, Boston, United States
| | - Jamey D Young
- Departments of Chemical & Biomolecular Engineering and Molecular Physiology & Biophysics, Vanderbilt University, Nashville, United States
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Boston, United States
- Department of Medicine, Harvard Medical School, Boston, United States
| | - William M Oldham
- Department of Medicine, Brigham and Women's Hospital, Boston, United States
- Department of Medicine, Harvard Medical School, Boston, United States
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3
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Yasemi M, Jolicoeur M. A genome-scale dynamic constraint-based modelling (gDCBM) framework predicts growth dynamics, medium composition and intracellular flux distributions in CHO clonal variations. Metab Eng 2023; 78:209-222. [PMID: 37348809 DOI: 10.1016/j.ymben.2023.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/16/2022] [Accepted: 06/09/2023] [Indexed: 06/24/2023]
Abstract
Optimizing mammalian cell growth and bioproduction is a tedious task. However, due to the inherent complexity of eukaryotic cells, heuristic experimental approaches such as, metabolic engineering and bioprocess design, are frequently integrated with mathematical models of cell culture to improve biological process efficiency and find paths for improvement. Constraint-based metabolic models have evolved over the last two decades to be used for dynamic modelling in addition to providing a linear description of steady-state metabolic systems. Formulation and implementation of the underlying optimization problems require special attention to the model's performance and feasibility, lack of defects in the definition of system components, and consideration of optimal alternate solutions, in addition to processing power limitations. Here, the time-resolved dynamics of a genome-scale metabolic network of Chinese hamster ovary (CHO) cell metabolism are shown using a genome-scale dynamic constraint-based modelling framework (gDCBM). The metabolic network was adapted from a reference model of CHO genome-scale metabolic model (GSMM), iCHO_DG44_v1, and dynamic restrictions were imposed to its exchange fluxes based on experimental results. We used this framework for predicting physiological changes in CHO clonal variants. Because of the methodical creation of the components for the flux balance analysis optimization problem and the integration of a switch time, this model can generate sequential predictions of intracellular fluxes during growth and non-growth phases (per hour of culture time) and transparently reveal the shortcomings in such practice. As a result of the differences exploited by various clones, we can understand the relevance of changes in intracellular flux distribution and exometabolomics. The integration of various omics data into the given gDCBM framework, as well as the reductionist analysis of the model, can further help bioprocess optimization.
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Affiliation(s)
- Mohammadreza Yasemi
- Research Laboratory in Applied Metabolic Engineering, Department of Chemical Engineering, Polytechnique Montréal, P.O. Box 6079, Centre-ville Station, Montréal, Québec, H3C 3A7, Canada.
| | - Mario Jolicoeur
- Research Laboratory in Applied Metabolic Engineering, Department of Chemical Engineering, Polytechnique Montréal, P.O. Box 6079, Centre-ville Station, Montréal, Québec, H3C 3A7, Canada.
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Eduardo HGR, Silvestre ABJ, Agustín BC, Inés GPE, Edgar SM. Calculation of All Possible Stoichiometric Coefficients and Theoretical Yields of Microbial Global Reactions. BIOTECHNOL BIOPROC E 2022. [DOI: 10.1007/s12257-022-0061-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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5
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Abstract
Although the culture of VERO cells in bioreactors is an important industrial bioprocess for the production of viruses and vaccines, surprisingly few reports on the analysis of the flux distribution in the cell metabolism have been published. In this study, an attempt is made to fill this gap by providing an analysis of relatively simple metabolic networks, which are constructed to describe the cell behavior in different culture conditions, e.g., the exponential growth phase (availability of glucose and glutamine), cell growth without glutamine, and cell growth without glucose and glutamine. The metabolic networks are kept as simple as possible in order to avoid underdeterminacy linked to the lack of extracellular measurements, and a unique flux distribution is computed in each case based on a mild assumption that the macromolecular composition of the cell is known. The result of this computation provides some insight into the metabolic changes triggered by the culture conditions, which could support the design of feedback control strategies in fed batch or perfusion bioreactors where the lactate concentration is measured online and regulated by controlling the delivery rates of glucose and, possibly, of some essential amino acids.
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Rish AJ, Drennen JK, Anderson CA. Metabolic trends of Chinese hamster ovary cells in biopharmaceutical production under batch and fed-batch conditions. Biotechnol Prog 2021; 38:e3220. [PMID: 34676699 DOI: 10.1002/btpr.3220] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/05/2021] [Accepted: 10/19/2021] [Indexed: 11/07/2022]
Abstract
Extensive knowledge of Chinese hamster ovary (CHO) cell metabolism is required to improve process productivity and culture performance in biopharmaceutical manufacturing. However, CHO cells show a dynamic metabolism during culturing in batch and fed-batch bioreactors. CHO cell metabolism is generally described as taking place in three stages: exponential growth phase, stationary phase, and death phase. This review aims to summarize the trends of central metabolism for CHO cells during each stage. Additional insights into how culture conditions are related to phase transitions and force metabolic rewiring are provided. Understanding of CHO cell metabolism lends itself to improving culture qualities by, for example, identifying sources of toxic byproducts and pathways for cellular engineering. In summary, this review describes the changes in CHO cell central metabolism over the course of the culture.
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Affiliation(s)
- Adam J Rish
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, Pennsylvania, USA
| | - James K Drennen
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, Pennsylvania, USA
- Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, Pennsylvania, USA
| | - Carl A Anderson
- Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, Pennsylvania, USA
- Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, Pennsylvania, USA
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7
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How to Tackle Underdeterminacy in Metabolic Flux Analysis? A Tutorial and Critical Review. Processes (Basel) 2021. [DOI: 10.3390/pr9091577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Metabolic flux analysis is often (not to say almost always) faced with system underdeterminacy. Indeed, the linear algebraic system formed by the steady-state mass balance equations around the intracellular metabolites and the equality constraints related to the measurements of extracellular fluxes do not define a unique solution for the distribution of intracellular fluxes, but instead a set of solutions belonging to a convex polytope. Various methods have been proposed to tackle this underdeterminacy, including flux pathway analysis, flux balance analysis, flux variability analysis and sampling. These approaches are reviewed in this article and a toy example supports the discussion with illustrative numerical results.
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Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches. Processes (Basel) 2021. [DOI: 10.3390/pr9020322] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.
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Martínez JA, Bulté DB, Contreras MA, Palomares LA, Ramírez OT. Dynamic Modeling of CHO Cell Metabolism Using the Hybrid Cybernetic Approach With a Novel Elementary Mode Analysis Strategy. Front Bioeng Biotechnol 2020; 8:279. [PMID: 32351947 PMCID: PMC7174696 DOI: 10.3389/fbioe.2020.00279] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 03/17/2020] [Indexed: 12/18/2022] Open
Abstract
Chinese hamster ovary (CHO) cell culture has a major importance on the production of biopharmaceuticals, including recombinant therapeutic proteins such as monoclonal antibodies (MAb). Mathematical modeling of biological systems can successfully assess metabolism complexity while providing logical and systematic methods for relevant genetic target and culture parameter identification toward cell growth and productivity improvements. Most modeling approaches on CHO cells have been performed under stationary constraints, and only a few dynamic models have been presented on simplified reaction sets, due to substantial overparameterization problems. The hybrid cybernetic modeling (HCM) approach has been recently used to describe the dynamic behavior by incorporating regulation between different metabolic states by elementary mode participation control, with sets of equations evaluated by objective functions. However, as metabolic networks evaluated are constructed toward a genomic scale, and cell compartmentalization is considered, identification of the active set becomes more difficult as EM number exponentially grows. Thus, the development of robust approaches for EM active set selection and analysis with smaller computational requirements is required to impulse the use of cybernetic modeling on larger up to genome-scale networks. In this report, a novel elementary mode selection strategy, based on a polar representation of the convex solution space is presented and coupled to a cybernetic approach to model the dynamic physiologic and metabolic behavior of CHO-S cell cultures. The proposed Polar Space Yield Analysis (PSYA) was compared to other reported elementary mode selection approaches derived from Common Metabolic Objective Analysis (CMOA) used in Flux Balance Analysis (FBA), Yield Space Analysis (YSA), and Lumped Yield Space Analysis (LYSA). For this purpose, exponential growth phase dynamic metabolic models were calculated using kinetic rate equations based on previously modeled growth parameters. Finally, complete culture dynamic metabolic flux models were constructed using the HCM approach with selected elementary mode sets. The yield space elementary mode- and the polar space elementary mode- hybrid cybernetic models presented the best fits and performances. Also, a flux reaction perturbation prediction approach based on the polar yield solution space resulted useful for metabolic network flux distribution capability analysis and identification of potential genetic modifications targets.
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Affiliation(s)
- Juan A. Martínez
- Departamento de Medicina Molecular y Bioprocesos, Instituto de Biotecnología, Universidad Nacional de México, Cuernavaca, Mexico
| | | | | | | | - Octavio T. Ramírez
- Departamento de Medicina Molecular y Bioprocesos, Instituto de Biotecnología, Universidad Nacional de México, Cuernavaca, Mexico
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10
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Graham RJ, Ketcham S, Mohammad A, Bandaranayake BMB, Cao T, Ghosh B, Weaver J, Yoon S, Faustino PJ, Ashraf M, Cruz CN, Madhavarao CN. Zinc supplementation improves the harvest purity of β-glucuronidase from CHO cell culture by suppressing apoptosis. Appl Microbiol Biotechnol 2019; 104:1097-1108. [DOI: 10.1007/s00253-019-10296-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 11/21/2019] [Accepted: 12/03/2019] [Indexed: 11/30/2022]
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11
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Abbate T, Fernandes de Sousa S, Dewasme L, Bastin G, Vande Wouwer A. Inference of dynamic macroscopic models of cell metabolism based on elementary flux modes analysis. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.107325] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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12
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Dynamic metabolic network modeling of mammalian Chinese hamster ovary (CHO) cell cultures with continuous phase kinetics transitions. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2018.11.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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13
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Hagrot E, Oddsdóttir HÆ, Mäkinen M, Forsgren A, Chotteau V. Novel column generation-based optimization approach for poly-pathway kinetic model applied to CHO cell culture. Metab Eng Commun 2018; 8:e00083. [PMID: 30809468 PMCID: PMC6376161 DOI: 10.1016/j.mec.2018.e00083] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 10/30/2018] [Accepted: 12/08/2018] [Indexed: 11/26/2022] Open
Abstract
Mathematical modelling can provide precious tools for bioprocess simulation, prediction, control and optimization of mammalian cell-based cultures. In this paper we present a novel method to generate kinetic models of such cultures, rendering complex metabolic networks in a poly-pathway kinetic model. The model is based on subsets of elementary flux modes (EFMs) to generate macro-reactions. Thanks to our column generation-based optimization algorithm, the experimental data are used to identify the EFMs, which are relevant to the data. Here the systematic enumeration of all the EFMs is eliminated and a network including a large number of reactions can be considered. In particular, the poly-pathway model can simulate multiple metabolic behaviors in response to changes in the culture conditions. We apply the method to a network of 126 metabolic reactions describing cultures of antibody-producing Chinese hamster ovary cells, and generate a poly-pathway model that simulates multiple experimental conditions obtained in response to variations in amino acid availability. A good fit between simulated and experimental data is obtained, rendering the variations in the growth, product, and metabolite uptake/secretion rates. The intracellular reaction fluxes simulated by the model are explored, linking variations in metabolic behavior to adaptations of the intracellular metabolism. Novel method to model multiple states by a poly-pathway kinetic model. EFMs relevant to data identified by column generation (CG)-based optimization. CG optimization enables use of networks much larger than systematic enumeration. A kinetic model simulates changes in metabolic rates linked to available amino acids. The flux distribution of each metabolic state is visualized in the original network.
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Affiliation(s)
- Erika Hagrot
- Cell Technology Group, Department of Industrial Biotechnology/Bioprocess Design, School of Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, Sweden
| | - Hildur Æsa Oddsdóttir
- Department of Mathematics, Division of Optimization and Systems Theory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Meeri Mäkinen
- Cell Technology Group, Department of Industrial Biotechnology/Bioprocess Design, School of Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, Sweden
| | - Anders Forsgren
- Department of Mathematics, Division of Optimization and Systems Theory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Véronique Chotteau
- Cell Technology Group, Department of Industrial Biotechnology/Bioprocess Design, School of Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, Sweden.,WCPR, Wallenberg Centre for Protein Research, Sweden
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14
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Theoretical model and characteristics of mitochondrial thermogenesis. BIOPHYSICS REPORTS 2018; 4:63-67. [PMID: 29756006 PMCID: PMC5937889 DOI: 10.1007/s41048-018-0054-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Accepted: 03/22/2018] [Indexed: 11/08/2022] Open
Abstract
Based on the first law of thermodynamics and the thermal diffusion equation, the deduced theoretical model of mitochondrial thermogenesis satisfies the Laplace equation and is a special case of the thermal diffusion equation. The model settles the long-standing question of the ability to increase cellular temperature by endogenous thermogenesis and explains the thermogenic characteristics of brown adipocytes. The model and calculations also suggest that the number of free available protons is the major limiting factor for endogenous thermogenesis and its speed.
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15
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Voit EO. The best models of metabolism. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2017; 9:10.1002/wsbm.1391. [PMID: 28544810 PMCID: PMC5643013 DOI: 10.1002/wsbm.1391] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 03/31/2017] [Accepted: 04/01/2017] [Indexed: 12/25/2022]
Abstract
Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Eberhard O Voit
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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16
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Poly-pathway model, a novel approach to simulate multiple metabolic states by reaction network-based model – Application to amino acid depletion in CHO cell culture. J Biotechnol 2017; 259:235-247. [DOI: 10.1016/j.jbiotec.2017.05.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Revised: 05/30/2017] [Accepted: 05/31/2017] [Indexed: 01/10/2023]
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17
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Pan X, Streefland M, Dalm C, Wijffels RH, Martens DE. Selection of chemically defined media for CHO cell fed-batch culture processes. Cytotechnology 2017; 69:39-56. [PMID: 27900626 PMCID: PMC5264622 DOI: 10.1007/s10616-016-0036-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 10/26/2016] [Indexed: 01/17/2023] Open
Abstract
Two CHO cell clones derived from the same parental CHOBC® cell line and producing the same monoclonal antibody (BC-G, a low producing clone; BC-P, a high producing clone) were tested in four basal media in all possible combinations with three feeds (=12 conditions) in fed-batch cultures. Higher amino acid feeding did not always lead to higher mAb production. The two clones showed differences in cell physiology, metabolism and optimal medium-feed combinations. During the phase transitions of all cultures, cell metabolism showed a shift represented by lower specific consumption and production rates, except for the specific glucose consumption rate in cultures fed by Actifeed A/B. The BC-P clone fed by Actifeed A/B showed a threefold cell volume increase and an increase of the specific consumption rate of glucose in the stationary phase. Since feeding was based on glucose this resulted in accumulation of amino acids for this feed, while this did not occur for the poorer feed (EFA/B). The same feed also led to an increase of cell size for the BC-G clone, but to a lesser extent.
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Affiliation(s)
- Xiao Pan
- Bioprocess Engineering, Wageningen University, PO Box 16, 6700 AA, Wageningen, The Netherlands.
| | - Mathieu Streefland
- Bioprocess Engineering, Wageningen University, PO Box 16, 6700 AA, Wageningen, The Netherlands
| | - Ciska Dalm
- Synthon Biopharmaceuticals BV, Upstream Process Development, PO Box 7071, 6503 GN, Nijmegen, The Netherlands
| | - René H Wijffels
- Bioprocess Engineering, Wageningen University, PO Box 16, 6700 AA, Wageningen, The Netherlands
- Faculty of Biosciences and Aquaculture, Nord University, 8049, Bodø, Norway
| | - Dirk E Martens
- Bioprocess Engineering, Wageningen University, PO Box 16, 6700 AA, Wageningen, The Netherlands
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18
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Galleguillos SN, Ruckerbauer D, Gerstl MP, Borth N, Hanscho M, Zanghellini J. What can mathematical modelling say about CHO metabolism and protein glycosylation? Comput Struct Biotechnol J 2017; 15:212-221. [PMID: 28228925 PMCID: PMC5310201 DOI: 10.1016/j.csbj.2017.01.005] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Revised: 01/09/2017] [Accepted: 01/12/2017] [Indexed: 11/15/2022] Open
Abstract
Chinese hamster ovary cells have been in the spotlight for process optimization in recent years, due to being the major, long established cell factory for the production of recombinant proteins. A deep, quantitative understanding of CHO metabolism and mechanisms involved in protein glycosylation has proven to be attainable through the development of high throughput technologies. Here we review the most notable accomplishments in the field of modelling CHO metabolism and protein glycosylation.
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Affiliation(s)
- Sarah N Galleguillos
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - David Ruckerbauer
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Matthias P Gerstl
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Nicole Borth
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Michael Hanscho
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Jürgen Zanghellini
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
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19
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Pan X, Streefland M, Dalm C, Wijffels RH, Martens DE. Selection of chemically defined media for CHO cell fed-batch culture processes. Cytotechnology 2016. [PMID: 27900626 DOI: 10.1007/s10616‐016‐0036‐5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Two CHO cell clones derived from the same parental CHOBC® cell line and producing the same monoclonal antibody (BC-G, a low producing clone; BC-P, a high producing clone) were tested in four basal media in all possible combinations with three feeds (=12 conditions) in fed-batch cultures. Higher amino acid feeding did not always lead to higher mAb production. The two clones showed differences in cell physiology, metabolism and optimal medium-feed combinations. During the phase transitions of all cultures, cell metabolism showed a shift represented by lower specific consumption and production rates, except for the specific glucose consumption rate in cultures fed by Actifeed A/B. The BC-P clone fed by Actifeed A/B showed a threefold cell volume increase and an increase of the specific consumption rate of glucose in the stationary phase. Since feeding was based on glucose this resulted in accumulation of amino acids for this feed, while this did not occur for the poorer feed (EFA/B). The same feed also led to an increase of cell size for the BC-G clone, but to a lesser extent.
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Affiliation(s)
- Xiao Pan
- Bioprocess Engineering, Wageningen University, PO Box 16, 6700 AA, Wageningen, The Netherlands.
| | - Mathieu Streefland
- Bioprocess Engineering, Wageningen University, PO Box 16, 6700 AA, Wageningen, The Netherlands
| | - Ciska Dalm
- Synthon Biopharmaceuticals BV, Upstream Process Development, PO Box 7071, 6503 GN, Nijmegen, The Netherlands
| | - René H Wijffels
- Bioprocess Engineering, Wageningen University, PO Box 16, 6700 AA, Wageningen, The Netherlands.,Faculty of Biosciences and Aquaculture, Nord University, 8049, Bodø, Norway
| | - Dirk E Martens
- Bioprocess Engineering, Wageningen University, PO Box 16, 6700 AA, Wageningen, The Netherlands
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Affiliation(s)
- Jennifer Pfizenmaier
- University of Stuttgart; Institute of Biochemical Engineering; Allmandring 31 70569 Stuttgart Germany
| | - Ralf Takors
- University of Stuttgart; Institute of Biochemical Engineering; Allmandring 31 70569 Stuttgart Germany
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21
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Hefzi H, Ang KS, Hanscho M, Bordbar A, Ruckerbauer D, Lakshmanan M, Orellana CA, Baycin-Hizal D, Huang Y, Ley D, Martinez VS, Kyriakopoulos S, Jiménez NE, Zielinski DC, Quek LE, Wulff T, Arnsdorf J, Li S, Lee JS, Paglia G, Loira N, Spahn PN, Pedersen LE, Gutierrez JM, King ZA, Lund AM, Nagarajan H, Thomas A, Abdel-Haleem AM, Zanghellini J, Kildegaard HF, Voldborg BG, Gerdtzen ZP, Betenbaugh MJ, Palsson BO, Andersen MR, Nielsen LK, Borth N, Lee DY, Lewis NE. A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism. Cell Syst 2016; 3:434-443.e8. [PMID: 27883890 PMCID: PMC5132346 DOI: 10.1016/j.cels.2016.10.020] [Citation(s) in RCA: 152] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Revised: 06/16/2016] [Accepted: 10/21/2016] [Indexed: 12/22/2022]
Abstract
Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.
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Affiliation(s)
- Hooman Hefzi
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kok Siong Ang
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore; Bioprocessing Technology Institute, Agency for Science, Technology and Research (A(∗)STAR), 20 Biopolis Way, 06-01, Centros, Singapore 138668, Singapore
| | - Michael Hanscho
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, 1190 Vienna, Austria; Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria
| | - Aarash Bordbar
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - David Ruckerbauer
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, 1190 Vienna, Austria; Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A(∗)STAR), 20 Biopolis Way, 06-01, Centros, Singapore 138668, Singapore
| | - Camila A Orellana
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Building 75), Brisbane, QLD 4072, Australia
| | - Deniz Baycin-Hizal
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Yingxiang Huang
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla 92093, CA, USA
| | - Daniel Ley
- Department of Systems Biology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Veronica S Martinez
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Building 75), Brisbane, QLD 4072, Australia
| | - Sarantos Kyriakopoulos
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A(∗)STAR), 20 Biopolis Way, 06-01, Centros, Singapore 138668, Singapore
| | - Natalia E Jiménez
- Centre for Biotechnology and Bioengineering, Department of Chemical Engineering and Biotechnology, University of Chile, Santiago 8370456, Chile; MATHomics, Center for Mathematical Modeling; Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago 8370456, Chile
| | - Daniel C Zielinski
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Lake-Ee Quek
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Building 75), Brisbane, QLD 4072, Australia
| | - Tune Wulff
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Johnny Arnsdorf
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Shangzhong Li
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jae Seong Lee
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Giuseppe Paglia
- Center for Systems Biology, University of Iceland, 101 Reykjavik, Iceland
| | - Nicolas Loira
- Centre for Biotechnology and Bioengineering, Department of Chemical Engineering and Biotechnology, University of Chile, Santiago 8370456, Chile; MATHomics, Center for Mathematical Modeling; Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago 8370456, Chile
| | - Philipp N Spahn
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Lasse E Pedersen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Jahir M Gutierrez
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zachary A King
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anne Mathilde Lund
- Department of Systems Biology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Harish Nagarajan
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla 92093, CA, USA
| | - Alex Thomas
- Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla 92093, CA, USA
| | - Alyaa M Abdel-Haleem
- Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Computational Bioscience Research Centre, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | - Juergen Zanghellini
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, 1190 Vienna, Austria; Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria
| | - Helene F Kildegaard
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Bjørn G Voldborg
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Ziomara P Gerdtzen
- Centre for Biotechnology and Bioengineering, Department of Chemical Engineering and Biotechnology, University of Chile, Santiago 8370456, Chile; MATHomics, Center for Mathematical Modeling; Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago 8370456, Chile
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Mikael R Andersen
- Department of Systems Biology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Lars K Nielsen
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Building 75), Brisbane, QLD 4072, Australia
| | - Nicole Borth
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, 1190 Vienna, Austria; Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria.
| | - Dong-Yup Lee
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore; Bioprocessing Technology Institute, Agency for Science, Technology and Research (A(∗)STAR), 20 Biopolis Way, 06-01, Centros, Singapore 138668, Singapore.
| | - Nathan E Lewis
- Novo Nordisk Foundation Center for Biosustainability at the School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
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22
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Richelle A, Gziri KM, Bogaerts P. A methodology for building a macroscopic FBA-based dynamical simulator of cell cultures through flux variability analysis. Biochem Eng J 2016. [DOI: 10.1016/j.bej.2016.06.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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23
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Morales Y, Bosque G, Vehí J, Picó J, Llaneras F. PFA toolbox: a MATLAB tool for Metabolic Flux Analysis. BMC SYSTEMS BIOLOGY 2016; 10:46. [PMID: 27401090 PMCID: PMC4940746 DOI: 10.1186/s12918-016-0284-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2015] [Accepted: 06/01/2016] [Indexed: 11/29/2022]
Abstract
BACKGROUND Metabolic Flux Analysis (MFA) is a methodology that has been successfully applied to estimate metabolic fluxes in living cells. However, traditional frameworks based on this approach have some limitations, particularly when measurements are scarce and imprecise. This is very common in industrial environments. The PFA Toolbox can be used to face those scenarios. RESULTS Here we present the PFA (Possibilistic Flux Analysis) Toolbox for MATLAB, which simplifies the use of Interval and Possibilistic Metabolic Flux Analysis. The main features of the PFA Toolbox are the following: (a) It provides reliable MFA estimations in scenarios where only a few fluxes can be measured or those available are imprecise. (b) It provides tools to easily plot the results as interval estimates or flux distributions. (c) It is composed of simple functions that MATLAB users can apply in flexible ways. (d) It includes a Graphical User Interface (GUI), which provides a visual representation of the measurements and their uncertainty. (e) It can use stoichiometric models in COBRA format. In addition, the PFA Toolbox includes a User's Guide with a thorough description of its functions and several examples. CONCLUSIONS The PFA Toolbox for MATLAB is a freely available Toolbox that is able to perform Interval and Possibilistic MFA estimations.
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Affiliation(s)
- Yeimy Morales
- MICElab, IIIA, Universitat de Girona, Campus Montilivi, P4, Girona, 17071, Spain.
| | - Gabriel Bosque
- Institut Universitari d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camino de Vera s/n, Edificio 5C, 46022, Valencia, Spain
| | - Josep Vehí
- MICElab, IIIA, Universitat de Girona, Campus Montilivi, P4, Girona, 17071, Spain
| | - Jesús Picó
- Institut Universitari d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camino de Vera s/n, Edificio 5C, 46022, Valencia, Spain
| | - Francisco Llaneras
- MICElab, IIIA, Universitat de Girona, Campus Montilivi, P4, Girona, 17071, Spain
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24
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Hagrot E, Oddsdóttir HÆ, Hosta JG, Jacobsen EW, Chotteau V. RETRACTED: Poly-pathway model, a novel approach to simulate multiple metabolic states by reaction network-based model – Application to amino acid depletion in CHO cell culture. J Biotechnol 2016; 228:37-49. [DOI: 10.1016/j.jbiotec.2016.03.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Revised: 03/03/2016] [Accepted: 03/09/2016] [Indexed: 12/20/2022]
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25
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Fernandes de Sousa S, Bastin G, Jolicoeur M, Vande Wouwer A. Dynamic metabolic flux analysis using a convex analysis approach: Application to hybridoma cell cultures in perfusion. Biotechnol Bioeng 2015; 113:1102-12. [DOI: 10.1002/bit.25879] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Revised: 10/30/2015] [Accepted: 11/01/2015] [Indexed: 12/22/2022]
Affiliation(s)
| | - Georges Bastin
- Department of Mathematical Engineering; ICTEAM; Catholic University of Louvain; Louvain-La-Neuve Belgium
| | - Mario Jolicoeur
- Department of Chemical Engineering; Laboratory in Applied Metabolic Engineering; Polytechnic University of Montreal; Montréal Canada
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26
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Sá JV, Duarte TM, Carrondo MJT, Alves PM, Teixeira AP. Metabolic Flux Analysis: A Powerful Tool in Animal Cell Culture. CELL ENGINEERING 2015. [DOI: 10.1007/978-3-319-10320-4_16] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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Vercammen D, Logist F, Impe JV. Dynamic estimation of specific fluxes in metabolic networks using non-linear dynamic optimization. BMC SYSTEMS BIOLOGY 2014; 8:132. [PMID: 25466625 PMCID: PMC4280005 DOI: 10.1186/s12918-014-0132-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Accepted: 08/13/2014] [Indexed: 11/13/2022]
Abstract
BACKGROUND Metabolic network models describing the biochemical reaction network and material fluxes inside microorganisms open interesting routes for the model-based optimization of bioprocesses. Dynamic metabolic flux analysis (dMFA) has lately been studied as an extension of regular metabolic flux analysis (MFA), rendering a dynamic view of the fluxes, also in non-stationary conditions. Recent dMFA implementations suffer from some drawbacks, though. More specifically, the fluxes are not estimated as specific fluxes, which are more biologically relevant. Also, the flux profiles are not smooth, and additional constraints like, e.g., irreversibility constraints on the fluxes, cannot be taken into account. Finally, in all previous methods, a basis for the null space of the stoichiometric matrix, i.e., which set of free fluxes is used, needs to be chosen. This choice is not trivial, and has a large influence on the resulting estimates. RESULTS In this work, a new methodology based on a B-spline parameterization of the fluxes is presented. Because of the high degree of non-linearity due to this parameterization, an incremental knot insertion strategy has been devised, resulting in a sequence of non-linear dynamic optimization problems. These are solved using state-of-the-art dynamic optimization methods and tools, i.e., orthogonal collocation, an interior-point optimizer and automatic differentiation. Also, a procedure to choose an optimal basis for the null space of the stoichiometric matrix is described, discarding the need to make a choice beforehand. The proposed methodology is validated on two simulated case studies: (i) a small-scale network with 7 fluxes, to illustrate the operation of the algorithm, and (ii) a medium-scale network with 68 fluxes, to show the algorithm's capabilities for a realistic network. The results show an accurate correspondence to the reference fluxes used to simulate the measurements, both in a theoretically ideal setting with no experimental noise, and in a realistic noise setting. CONCLUSIONS Because, apart from a metabolic reaction network and the measurements, no extra input needs to be given, the resulting algorithm is a systematic, integrated and accurate methodology for dynamic metabolic flux analysis that can be run online in real-time if necessary.
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Affiliation(s)
- Dominique Vercammen
- KU Leuven, BioTeC - Chemical and Biochemical Process Technology and Control & OPTEC - Center of Excellence: Optimization in Engineering, Department of Chemical Engineering, Willem de Croylaan 46/2423, Leuven, 3001, Belgium.
| | - Filip Logist
- KU Leuven, BioTeC - Chemical and Biochemical Process Technology and Control & OPTEC - Center of Excellence: Optimization in Engineering, Department of Chemical Engineering, Willem de Croylaan 46/2423, Leuven, 3001, Belgium.
| | - Jan Van Impe
- KU Leuven, BioTeC - Chemical and Biochemical Process Technology and Control & OPTEC - Center of Excellence: Optimization in Engineering, Department of Chemical Engineering, Willem de Croylaan 46/2423, Leuven, 3001, Belgium.
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Oddsdóttir HÆ, Hagrot E, Chotteau V, Forsgren A. On dynamically generating relevant elementary flux modes in a metabolic network using optimization. J Math Biol 2014; 71:903-20. [PMID: 25323319 DOI: 10.1007/s00285-014-0844-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 10/06/2014] [Indexed: 11/24/2022]
Abstract
Elementary flux modes (EFMs) are pathways through a metabolic reaction network that connect external substrates to products. Using EFMs, a metabolic network can be transformed into its macroscopic counterpart, in which the internal metabolites have been eliminated and only external metabolites remain. In EFMs-based metabolic flux analysis (MFA) experimentally determined external fluxes are used to estimate the flux of each EFM. It is in general prohibitive to enumerate all EFMs for complex networks, since the number of EFMs increases rapidly with network complexity. In this work we present an optimization-based method that dynamically generates a subset of EFMs and solves the EFMs-based MFA problem simultaneously. The obtained subset contains EFMs that contribute to the optimal solution of the EFMs-based MFA problem. The usefulness of our method was examined in a case-study using data from a Chinese hamster ovary cell culture and two networks of varied complexity. It was demonstrated that the EFMs-based MFA problem could be solved at a low computational cost, even for the more complex network. Additionally, only a fraction of the total number of EFMs was needed to compute the optimal solution.
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Affiliation(s)
- Hildur Æsa Oddsdóttir
- Department of Mathematics, Optimization and Systems Theory, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden,
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DRUM: a new framework for metabolic modeling under non-balanced growth. Application to the carbon metabolism of unicellular microalgae. PLoS One 2014; 9:e104499. [PMID: 25105494 PMCID: PMC4126706 DOI: 10.1371/journal.pone.0104499] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2014] [Accepted: 07/12/2014] [Indexed: 11/23/2022] Open
Abstract
Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.
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Lohr V, Hädicke O, Genzel Y, Jordan I, Büntemeyer H, Klamt S, Reichl U. The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis. BMC Biotechnol 2014; 14:72. [PMID: 25077436 PMCID: PMC4124504 DOI: 10.1186/1472-6750-14-72] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Accepted: 07/16/2014] [Indexed: 01/10/2023] Open
Abstract
Background In human vaccine manufacturing some pathogens such as Modified Vaccinia Virus Ankara, measles, mumps virus as well as influenza viruses are still produced on primary material derived from embryonated chicken eggs. Processes depending on primary cell culture, however, are difficult to adapt to modern vaccine production. Therefore, we derived previously a continuous suspension cell line, AGE1.CR.pIX, from muscovy duck and established chemically-defined media for virus propagation. Results To better understand vaccine production processes, we developed a stoichiometric model of the central metabolism of AGE1.CR.pIX cells and applied flux variability and metabolic flux analysis. Results were compared to literature dealing with mammalian and insect cell culture metabolism focusing on the question whether cultured avian cells differ in metabolism. Qualitatively, the observed flux distribution of this avian cell line was similar to distributions found for mammalian cell lines (e.g. CHO, MDCK cells). In particular, glucose was catabolized inefficiently and glycolysis and TCA cycle seem to be only weakly connected. Conclusions A distinguishing feature of the avian cell line is that glutaminolysis plays only a minor role in energy generation and production of precursors, resulting in low extracellular ammonia concentrations. This metabolic flux study is the first for a continuous avian cell line. It provides a basis for further metabolic analyses to exploit the biotechnological potential of avian and vertebrate cell lines and to develop specific optimized cell culture processes, e.g. vaccine production processes.
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Affiliation(s)
- Verena Lohr
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr, 1, 39106 Magdeburg, Germany.
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31
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Ghorbaniaghdam A, Henry O, Jolicoeur M. An in-silico study of the regulation of CHO cells glycolysis. J Theor Biol 2014; 357:112-22. [PMID: 24801859 DOI: 10.1016/j.jtbi.2014.04.035] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2013] [Revised: 04/15/2014] [Accepted: 04/24/2014] [Indexed: 10/25/2022]
Abstract
In this work, a kinetic-metabolic model previously developed for CHO cells is used to study glycolysis regulation. The model is assessed for its biological relevance by analyzing its ability to simulate metabolic events induced following a hypoxic perturbation. Feedback and feedforward regulatory mechanisms known to occur to either inhibit or activate fluxes of glycolysis, are implemented in various combined scenarios and their effects on the metabolic response were analyzed. This study aims at characterizing the role of intermediates of glycolysis and of the cell energetic state, described as the AMP-to-ATP ratio, as inhibitors and activators of glycolysis pathway. In addition to the glycolysis pathway, we here describe the transient metabolic response of pathways that are connected to glycolysis, such as the pentose phosphate pathway, TCA cycle, cell bioenergetics system, glutamine and amino acids metabolisms. Taken individually, each regulatory mechanism leads to an oscillatory behavior in response to a hypoxic perturbation, while their combination clearly damps oscillations. However, only the addition of the cell energetic state to the regulatory mechanisms results in a non-oscillating response leading to metabolic flux rate rearrangement corresponding to the anaerobic metabolism expected to prevail under hypoxic conditions. We thus demonstrate in this work, from model simulations, that the robustness of a cell energetic metabolism can be described from a combination of feedback and feedforward inhibition and activation regulatory mechanisms of glycolysis fluxes, involving intermediates of glycolysis and the cell energetic state itself.
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Affiliation(s)
- Atefeh Ghorbaniaghdam
- Canada Research Chair in Applied Metabolic Engineering, Canada; Department of Chemical Engineering, École Polytechnique de Montréal, P.O. box 6079, Centre-ville Station, Montréal, Québec H3C 3A7, Canada
| | - Olivier Henry
- Department of Chemical Engineering, École Polytechnique de Montréal, P.O. box 6079, Centre-ville Station, Montréal, Québec H3C 3A7, Canada
| | - Mario Jolicoeur
- Canada Research Chair in Applied Metabolic Engineering, Canada; Department of Chemical Engineering, École Polytechnique de Montréal, P.O. box 6079, Centre-ville Station, Montréal, Québec H3C 3A7, Canada.
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32
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Nicolae A, Wahrheit J, Bahnemann J, Zeng AP, Heinzle E. Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation. BMC SYSTEMS BIOLOGY 2014; 8:50. [PMID: 24773761 PMCID: PMC4022241 DOI: 10.1186/1752-0509-8-50] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2013] [Accepted: 04/07/2014] [Indexed: 12/03/2022]
Abstract
BACKGROUND Mapping the intracellular fluxes for established mammalian cell lines becomes increasingly important for scientific and economic reasons. However, this is being hampered by the high complexity of metabolic networks, particularly concerning compartmentation. RESULTS Intracellular fluxes of the CHO-K1 cell line central carbon metabolism were successfully determined for a complex network using non-stationary 13C metabolic flux analysis. Mass isotopomers of extracellular metabolites were determined using [U-13C6] glucose as labeled substrate. Metabolic compartmentation and extracellular transport reversibility proved essential to successfully reproduce the dynamics of the labeling patterns. Alanine and pyruvate reversibility changed dynamically even if their net production fluxes remained constant. Cataplerotic fluxes of cytosolic phosphoenolpyruvate carboxykinase and mitochondrial malic enzyme and pyruvate carboxylase were successfully determined. Glycolytic pyruvate channeling to lactate was modeled by including a separate pyruvate pool. In the exponential growth phase, alanine, glycine and glutamate were excreted, and glutamine, aspartate, asparagine and serine were taken up; however, all these amino acids except asparagine were exchanged reversibly with the media. High fluxes were determined in the pentose phosphate pathway and the TCA cycle. The latter was fueled mainly by glucose but also by amino acid catabolism. CONCLUSIONS The CHO-K1 central metabolism in controlled batch culture proves to be robust. It has the main purpose to ensure fast growth on a mixture of substrates and also to mitigate oxidative stress. It achieves this by using compartmentation to control NADPH and NADH availability and by simultaneous synthesis and catabolism of amino acids.
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Affiliation(s)
- Averina Nicolae
- Universität des Saarlandes Technische Biochemie, Campus A 1.5, Saarbrücken D-66123, Germany
| | - Judith Wahrheit
- Universität des Saarlandes Technische Biochemie, Campus A 1.5, Saarbrücken D-66123, Germany
| | - Janina Bahnemann
- Institute of Bioprocess and Biosystems Engineering, Technische Universität Hamburg-Harburg, Denickestr. 15, Hamburg D - 21073, Germany
| | - An-Ping Zeng
- Institute of Bioprocess and Biosystems Engineering, Technische Universität Hamburg-Harburg, Denickestr. 15, Hamburg D - 21073, Germany
| | - Elmar Heinzle
- Universität des Saarlandes Technische Biochemie, Campus A 1.5, Saarbrücken D-66123, Germany
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Nicolae A, Wahrheit J, Bahnemann J, Zeng AP, Heinzle E. Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation. BMC SYSTEMS BIOLOGY 2014. [PMID: 24773761 DOI: 10.1186/1752‐0509‐8‐50] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Mapping the intracellular fluxes for established mammalian cell lines becomes increasingly important for scientific and economic reasons. However, this is being hampered by the high complexity of metabolic networks, particularly concerning compartmentation. RESULTS Intracellular fluxes of the CHO-K1 cell line central carbon metabolism were successfully determined for a complex network using non-stationary 13C metabolic flux analysis. Mass isotopomers of extracellular metabolites were determined using [U-13C6] glucose as labeled substrate. Metabolic compartmentation and extracellular transport reversibility proved essential to successfully reproduce the dynamics of the labeling patterns. Alanine and pyruvate reversibility changed dynamically even if their net production fluxes remained constant. Cataplerotic fluxes of cytosolic phosphoenolpyruvate carboxykinase and mitochondrial malic enzyme and pyruvate carboxylase were successfully determined. Glycolytic pyruvate channeling to lactate was modeled by including a separate pyruvate pool. In the exponential growth phase, alanine, glycine and glutamate were excreted, and glutamine, aspartate, asparagine and serine were taken up; however, all these amino acids except asparagine were exchanged reversibly with the media. High fluxes were determined in the pentose phosphate pathway and the TCA cycle. The latter was fueled mainly by glucose but also by amino acid catabolism. CONCLUSIONS The CHO-K1 central metabolism in controlled batch culture proves to be robust. It has the main purpose to ensure fast growth on a mixture of substrates and also to mitigate oxidative stress. It achieves this by using compartmentation to control NADPH and NADH availability and by simultaneous synthesis and catabolism of amino acids.
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Affiliation(s)
| | | | | | | | - Elmar Heinzle
- Universität des Saarlandes Technische Biochemie, Campus A 1,5, Saarbrücken D-66123, Germany.
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Wahrheit J, Nicolae A, Heinzle E. Dynamics of growth and metabolism controlled by glutamine availability in Chinese hamster ovary cells. Appl Microbiol Biotechnol 2013; 98:1771-83. [PMID: 24362913 DOI: 10.1007/s00253-013-5452-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Revised: 11/25/2013] [Accepted: 12/02/2013] [Indexed: 11/27/2022]
Abstract
The physiology of animal cells is characterized by constantly changing environmental conditions and adapting cellular responses. Applied dynamic metabolic flux analysis captures metabolic dynamics and can be applied to industrially relevant cultivation conditions. We investigated the impact of glutamine availability or limitation on the physiology of CHO K1 cells in eight different batch and fed-batch cultivations. Varying glutamine availability resulted in global metabolic changes. We observed dose-dependent effects of glutamine in batch cultivation. Identifying metabolic links from the glutamine metabolism to specific metabolic pathways, we show that glutamine feeding results in its coupling to tricarboxylic acid cycle fluxes and in its decoupling from metabolic waste production. We provide a mechanistic explanation of the cellular responses upon mild or severe glutamine limitation and ammonia stress. The growth rate of CHO K1 decreased with increasing ammonia levels in the supernatant. On the other hand, growth, especially culture longevity, was stimulated at mild glutamine-limiting conditions. Flux rearrangements in the pyruvate and amino acid metabolism compensate glutamine limitation by consumption of alternative carbon sources and facilitating glutamine synthesis and mitigate ammonia stress as result of glutamine abundance.
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Affiliation(s)
- Judith Wahrheit
- Biochemical Engineering Institute, Saarland University, Campus A1.5, 66123, Saarbrücken, Germany
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Kildegaard HF, Baycin-Hizal D, Lewis NE, Betenbaugh MJ. The emerging CHO systems biology era: harnessing the ‘omics revolution for biotechnology. Curr Opin Biotechnol 2013; 24:1102-7. [DOI: 10.1016/j.copbio.2013.02.007] [Citation(s) in RCA: 139] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2012] [Revised: 01/17/2013] [Accepted: 02/09/2013] [Indexed: 11/29/2022]
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Gupta AJ, Gruppen H, Maes D, Boots JW, Wierenga PA. Factors causing compositional changes in soy protein hydrolysates and effects on cell culture functionality. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2013; 61:10613-10625. [PMID: 24117369 DOI: 10.1021/jf403051z] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Soy protein hydrolysates significantly enhance cell growth and recombinant protein production in cell cultures. The extent of this enhancement in cell growth and IgG production is known to vary from batch to batch. This can be due to differences in the abundance of different classes of compounds (e.g., peptide content), the quality of these compounds (e.g., glycated peptides), or the presence of specific compounds (e.g., furosine). These quantitative and qualitative differences between batches of hydrolysates result from variation in the seed composition and seed/meal processing. Although a considerable amount of literature is available that describes these factors, this knowledge has not been combined in an overview yet. The aim of this review is to identify the most dominant factors that affect hydrolysate composition and functionality. Although there is a limited influence of variation in the seed composition, the overview shows that the qualitative changes in hydrolysate composition result in the formation of minor compounds (e.g., Maillard reaction products). In pure systems, these compounds have a profound effect on the cell culture functionality. This suggests that the presence of these compounds in soy protein hydrolysates may affect hydrolysate functionality as well. This influence on the functionality can be of direct or indirect nature. For instance, some minor compounds (e.g., Maillard reaction products) are cytotoxic, whereas other compounds (e.g., phytates) suppress protein hydrolysis during hydrolysate production, resulting in altered peptide composition, and, thus, affect the functionality.
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Affiliation(s)
- Abhishek J Gupta
- Laboratory of Food Chemistry, Wageningen University , P.O. Box 17, 6700 AA Wageningen, The Netherlands
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Carinhas N, Duarte TM, Barreiro LC, Carrondo MJT, Alves PM, Teixeira AP. Metabolic signatures of GS-CHO cell clones associated with butyrate treatment and culture phase transition. Biotechnol Bioeng 2013; 110:3244-57. [DOI: 10.1002/bit.24983] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2013] [Revised: 05/26/2013] [Accepted: 06/12/2013] [Indexed: 01/05/2023]
Affiliation(s)
- Nuno Carinhas
- iBET; Instituto de Biologia Experimental e Tecnológica; Apartado 12 2781-901 Oeiras Portugal
- Instituto de Tecnologia Química e Biológica; Universidade Nova de Lisboa; Av. da República 2780-157 Oeiras Portugal
| | - Tiago M. Duarte
- iBET; Instituto de Biologia Experimental e Tecnológica; Apartado 12 2781-901 Oeiras Portugal
- Instituto de Tecnologia Química e Biológica; Universidade Nova de Lisboa; Av. da República 2780-157 Oeiras Portugal
| | - Laura C. Barreiro
- iBET; Instituto de Biologia Experimental e Tecnológica; Apartado 12 2781-901 Oeiras Portugal
- Instituto de Tecnologia Química e Biológica; Universidade Nova de Lisboa; Av. da República 2780-157 Oeiras Portugal
| | - Manuel J. T. Carrondo
- iBET; Instituto de Biologia Experimental e Tecnológica; Apartado 12 2781-901 Oeiras Portugal
- Instituto de Tecnologia Química e Biológica; Universidade Nova de Lisboa; Av. da República 2780-157 Oeiras Portugal
- Departamento de Química, Faculdade de Ciências e Tecnologia; Universidade Nova de Lisboa; 2829-516 Caparica Portugal
| | - Paula M. Alves
- iBET; Instituto de Biologia Experimental e Tecnológica; Apartado 12 2781-901 Oeiras Portugal
- Instituto de Tecnologia Química e Biológica; Universidade Nova de Lisboa; Av. da República 2780-157 Oeiras Portugal
| | - Ana P. Teixeira
- iBET; Instituto de Biologia Experimental e Tecnológica; Apartado 12 2781-901 Oeiras Portugal
- Instituto de Tecnologia Química e Biológica; Universidade Nova de Lisboa; Av. da República 2780-157 Oeiras Portugal
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Geng J, Bi J, Zeng A, Yuan J. Application of hybrid cybernetic model in simulating myeloma cell culture co-consuming glucose and glutamine with mixed consumption patterns. Process Biochem 2013. [DOI: 10.1016/j.procbio.2013.03.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Dean J, Reddy P. Metabolic analysis of antibody producing CHO cells in fed-batch production. Biotechnol Bioeng 2013; 110:1735-47. [DOI: 10.1002/bit.24826] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Revised: 10/27/2012] [Accepted: 12/17/2012] [Indexed: 12/19/2022]
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40
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Isotopically nonstationary 13C flux analysis of Myc-induced metabolic reprogramming in B-cells. Metab Eng 2012; 15:206-17. [PMID: 22898717 DOI: 10.1016/j.ymben.2012.07.008] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2012] [Revised: 06/29/2012] [Accepted: 07/30/2012] [Indexed: 01/07/2023]
Abstract
We assessed several methods of (13)C metabolic flux analysis (MFA) and found that isotopically nonstationary MFA achieved maximum flux resolution in cultured P493-6 B-cells, which have been engineered to provide tunable expression of the Myc oncoprotein. Comparison of metabolic flux maps obtained under oncogenic (High) and endogenous (Low) Myc expression levels revealed network-wide reprogramming in response to ectopic Myc expression. High Myc cells relied more heavily on mitochondrial oxidative metabolism than Low Myc cells and globally upregulated their consumption of amino acids relative to glucose. TCA cycle and amphibolic mitochondrial pathways exhibited 2- to 4-fold flux increases in High Myc cells, in contrast to modest increases in glucose uptake and lactate excretion. Because our MFA approach relied exclusively upon isotopic measurements of protein-bound amino acids and RNA-bound ribose, it is readily applicable to more complex tumor models that are not amenable to direct extraction and isotopic analysis of free intracellular metabolites.
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Zamorano F, Vande Wouwer A, Jungers RM, Bastin G. Dynamic metabolic models of CHO cell cultures through minimal sets of elementary flux modes. J Biotechnol 2012; 164:409-22. [PMID: 22698821 DOI: 10.1016/j.jbiotec.2012.05.005] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2012] [Revised: 05/16/2012] [Accepted: 05/21/2012] [Indexed: 11/16/2022]
Abstract
The concept of Elementary Flux Modes (EFMs) has been of central importance in a number of studies involving the analysis of metabolism. In Provost and Bastin (2007) this concept is used to translate the metabolic networks of the different phases of CHO cell cultures into macroscopic bioreactions linking extracellular substrates to products. However, a critical issue concerns the calculation of these elementary flux vectors, as their number combinatorially increases with the size of the metabolic network. In this study, a detailed metabolic network of CHO cells is considered, where the above-mentioned combinatorial explosion makes the computation of the elementary flux modes impossible. To alleviate this problem, a methodology proposed in Jungers et al. (2011) is used to compute a decomposition of admissible flux vectors in a minimal number of elementary flux modes without explicitly enumerating all of them. As a result, a set of macroscopic bioreactions linking the extracellular measured species is obtained at a very low computational expense. The procedure is repeated for the several cell culture phases and a global model is built using a multi-model approach, which is able to successfully predict the evolution of experimental data.
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Affiliation(s)
- F Zamorano
- Department of Automatic Control, University of Mons, Boulevard Dolez 31, 7000 Mons, Belgium.
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Gerdtzen ZP. Modeling metabolic networks for mammalian cell systems: general considerations, modeling strategies, and available tools. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2012; 127:71-108. [PMID: 21984615 DOI: 10.1007/10_2011_120] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Over the past decades, the availability of large amounts of information regarding cellular processes and reaction rates, along with increasing knowledge about the complex mechanisms involved in these processes, has changed the way we approach the understanding of cellular processes. We can no longer rely only on our intuition for interpreting experimental data and evaluating new hypotheses, as the information to analyze is becoming increasingly complex. The paradigm for the analysis of cellular systems has shifted from a focus on individual processes to comprehensive global mathematical descriptions that consider the interactions of metabolic, genomic, and signaling networks. Analysis and simulations are used to test our knowledge by refuting or validating new hypotheses regarding a complex system, which can result in predictive capabilities that lead to better experimental design. Different types of models can be used for this purpose, depending on the type and amount of information available for the specific system. Stoichiometric models are based on the metabolic structure of the system and allow explorations of steady state distributions in the network. Detailed kinetic models provide a description of the dynamics of the system, they involve a large number of reactions with varied kinetic characteristics and require a large number of parameters. Models based on statistical information provide a description of the system without information regarding structure and interactions of the networks involved. The development of detailed models for mammalian cell metabolism has only recently started to grow more strongly, due to the intrinsic complexities of mammalian systems, and the limited availability of experimental information and adequate modeling tools. In this work we review the strategies, tools, current advances, and recent models of mammalian cells, focusing mainly on metabolism, but discussing the methodology applied to other types of networks as well.
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Affiliation(s)
- Ziomara P Gerdtzen
- Department of Chemical Engineering and Biotechnology, Millennium Institute for Cell Dynamics and Biotechnology: a Centre for Systems Biology, University of Chile, Beauchef 850, Santiago, Chile,
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Ahn WS, Antoniewicz MR. Towards dynamic metabolic flux analysis in CHO cell cultures. Biotechnol J 2011; 7:61-74. [DOI: 10.1002/biot.201100052] [Citation(s) in RCA: 96] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2011] [Revised: 10/11/2011] [Accepted: 10/26/2011] [Indexed: 12/23/2022]
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Optical sensors for monitoring dynamic changes of intracellular metabolite levels in mammalian cells. Nat Protoc 2011; 6:1818-33. [PMID: 22036884 DOI: 10.1038/nprot.2011.392] [Citation(s) in RCA: 88] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Knowledge of the in vivo levels, distribution and flux of ions and metabolites is crucial to our understanding of physiology in both healthy and diseased states. The quantitative analysis of the dynamics of ions and metabolites with subcellular resolution in vivo poses a major challenge for the analysis of metabolic processes. Genetically encoded Förster resonance energy transfer (FRET) sensors can be used for real-time in vivo detection of metabolites. FRET sensor proteins, for example, for glucose, can be targeted genetically to any cellular compartment, or even to subdomains (e.g., a membrane surface), by adding signal sequences or fusing the sensors to specific proteins. The sensors can be used for analyses in individual mammalian cells in culture, in tissue slices and in intact organisms. Applications include gene discovery, high-throughput drug screens or systematic analysis of regulatory networks affecting uptake, efflux and metabolism. Quantitative analyses obtained with the help of FRET sensors for glucose or other ions and metabolites provide valuable data for modeling of flux. Here we provide a detailed protocol for monitoring glucose levels in the cytosol of mammalian cell cultures through the use of FRET glucose sensors; moreover, the protocol can be used for other ions and metabolites and for analyses in other organisms, as has been successfully demonstrated in bacteria, yeast and even intact plants. The whole procedure typically takes ∼4 d including seeding and transfection of mammalian cells; the FRET-based analysis of transfected cells takes ∼5 h.
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