1
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Karimi Alavijeh M, Lee YY, Gras SL. A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments. Eng Life Sci 2024; 24:e2400023. [PMID: 38975020 PMCID: PMC11223373 DOI: 10.1002/elsc.202400023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 07/09/2024] Open
Abstract
Bioreactor scale-up and scale-down have always been a topical issue for the biopharmaceutical industry and despite considerable effort, the identification of a fail-safe strategy for bioprocess development across scales remains a challenge. With the ubiquitous growth of digital transformation technologies, new scaling methods based on computer models may enable more effective scaling. This study aimed to evaluate the potential application of machine learning (ML) algorithms for bioreactor scale-up, with a specific focus on the prediction of scaling parameters. Factors critical to the development of such models were identified and data for bioreactor scale-up studies involving CHO cell-generated mAb products collated from the literature and public sources for the development of unsupervised and supervised ML models. Comparison of bioreactor performance across scales identified similarities between the different processes and primary differences between small- and large-scale bioreactors. A series of three case studies were developed to assess the relationship between cell growth and scale-sensitive bioreactor features. An embedding layer improved the capability of artificial neural network models to predict cell growth at a large-scale, as this approach captured similarities between the processes. Further models constructed to predict scaling parameters demonstrated how ML models may be applied to assist the scaling process. The development of data sets that include more characterization data with greater variability under different gassing and agitation regimes will also assist the future development of ML tools for bioreactor scaling.
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Affiliation(s)
- Masih Karimi Alavijeh
- Department of Chemical EngineeringThe University of MelbourneParkvilleVictoriaAustralia
- The Bio21 Molecular Science and Biotechnology InstituteThe University of MelbourneParkvilleVictoriaAustralia
| | | | - Sally L. Gras
- Department of Chemical EngineeringThe University of MelbourneParkvilleVictoriaAustralia
- The Bio21 Molecular Science and Biotechnology InstituteThe University of MelbourneParkvilleVictoriaAustralia
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2
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Singh VK, Jiménez del Val I, Glassey J, Kavousi F. Integration Approaches to Model Bioreactor Hydrodynamics and Cellular Kinetics for Advancing Bioprocess Optimisation. Bioengineering (Basel) 2024; 11:546. [PMID: 38927782 PMCID: PMC11200465 DOI: 10.3390/bioengineering11060546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/17/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024] Open
Abstract
Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which in turn affects the efficiency and profitability of the process. Computational fluid dynamics (CFD) simulations are being widely embraced for their ability to simulate bioprocess performance, facilitate bioprocess upscaling, downsizing, and process optimisation. Recently, CFD approaches have been integrated with dynamic Cell reaction kinetic (CRK) modelling to generate valuable information about the cellular response to fluctuating hydrodynamic parameters inside large production processes. Such coupled approaches have the potential to facilitate informed decision-making in intelligent biomanufacturing, aligning with the principles of "Industry 4.0" concerning digitalisation and automation. In this review, we discuss the benefits of utilising integrated CFD-CRK models and the different approaches to integrating CFD-based bioreactor hydrodynamic models with cellular kinetic models. We also highlight the suitability of different coupling approaches for bioprocess modelling in the purview of associated computational loads.
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Affiliation(s)
- Vishal Kumar Singh
- Process and Chemical Engineering, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland;
| | - Ioscani Jiménez del Val
- School of Chemical & Bioprocess Engineering, University College Dublin, D04 V1W8 Dublin, Ireland;
| | - Jarka Glassey
- Process and Chemical Engineering, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland;
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Fatemeh Kavousi
- Process and Chemical Engineering, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland;
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3
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Reynoso-Cereceda GI, Valdez-Cruz NA, Pérez NO, Trujillo-Roldán MA. A comprehensive study of glucose and oxygen gradients in a scaled-down model of recombinant HuGM-CSF production in thermoinduced Escherichia coli fed-batch cultures. Prep Biochem Biotechnol 2024:1-12. [PMID: 38701182 DOI: 10.1080/10826068.2024.2347403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
The effect of gradients of elevated glucose and low dissolved oxygen in the addition zone of fed-batch E. coli thermoinduced recombinant high cell density cultures can be evaluated through two-compartment scale-down models. Here, glucose was fed in the inlet of a plug flow bioreactor (PFB) connected to a stirred tank bioreactor (STB). E. coli cells diminished growth from 48.2 ± 2.2 g/L in the stage of RP production if compared to control (STB) with STB-PFB experiments, when residence time inside the PFB was 25 s (34.1 ± 3.5 g/L) and 40 s (25.6 ± 5.1 g/L), respectively. The recombinant granulocyte-macrophage colony-stimulating factor (rHuGM-CSF) production decreased from 34 ± 7% of RP in inclusion bodies (IB) in control cultures to 21 ± 8%, and 7 ± 4% during the thermoinduction production phase when increasing residence time inside the PFB to 25 s and 40 s, respectively. This, along with the accumulation of acetic and formic acid (up to 4 g/L), indicates metabolic redirection of central carbon routes through metabolic flow and mixed acid fermentation. Special care must be taken when producing a recombinant protein in heat-induced E. coli, because the yield and productivity of the protein decreases as the size of the bioreactors increases, especially if they are carried at high cell density.
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Affiliation(s)
- Greta I Reynoso-Cereceda
- Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Cd. Universitaria, Coyoacán, Ciudad de México, México
- Posgrado en Ciencias Biomédicas, Universidad Nacional Autónoma de México, México. Unidad de Posgrado, CDMX, México
| | - Norma A Valdez-Cruz
- Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Cd. Universitaria, Coyoacán, Ciudad de México, México
- Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de México, Baja California, Mexico
| | - Nestor O Pérez
- Probiomed S.A. de C.V. Planta Tenancingo, Cruce de Carreteras Acatzingo- Zumpahuacan SN, Tenancingo, México
| | - Mauricio A Trujillo-Roldán
- Departamento de Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Cd. Universitaria, Coyoacán, Ciudad de México, México
- Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de México, Baja California, Mexico
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4
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Losoi P, Konttinen J, Santala V. Modeling large-scale bioreactors with diffusion equations. Part II: Characterizing substrate, oxygen, temperature, carbon dioxide, and pH profiles. Biotechnol Bioeng 2024; 121:1102-1117. [PMID: 38151906 DOI: 10.1002/bit.28635] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 11/23/2023] [Accepted: 12/09/2023] [Indexed: 12/29/2023]
Abstract
Large-scale fermentation processes involve complex dynamic interactions between mixing, reaction, mass transfer, and the suspended biomass. Empirical correlations or case-specific computational simulations are usually used to predict and estimate the performance of large-scale bioreactors based on data acquired at bench scale. In this two-part-study, one-dimensional axial diffusion equations were studied as a general and predictive model of large-scale bioreactors. This second part focused on typical fed-batch operations where substrate gradients are known to occur, and characterized the profiles of substrate, pH, oxygen, carbon dioxide, and temperature. The physically grounded steady-state axial diffusion equations with first- and zeroth-order kinetics yielded analytical solutions to the relevant variables. The results were compared with large-scale Escherichia coli and Saccharomyces cerevisiae experiments and simulations from the literature, and good agreement was found in substrate profiles. The analytical profiles obtained for dissolved oxygen, temperature, pH, andCO 2 ${\text{CO}}_{2}$ were also consistent with the available data. Distribution functions for the substrate were defined, and efficiency factors for biomass growth and oxygen uptake rate were derived. In conclusion, this study demonstrated that axial diffusion equations can be used to model the effects of mixing and reaction on the relevant variables of typical large-scale fed-batch fermentations.
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Affiliation(s)
- Pauli Losoi
- Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland
| | - Jukka Konttinen
- Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland
| | - Ville Santala
- Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland
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5
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Reddy JV, Raudenbush K, Papoutsakis ET, Ierapetritou M. Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation. Biotechnol Adv 2023; 67:108179. [PMID: 37257729 DOI: 10.1016/j.biotechadv.2023.108179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 05/18/2023] [Accepted: 05/21/2023] [Indexed: 06/02/2023]
Abstract
In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.
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Affiliation(s)
- Jayanth Venkatarama Reddy
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Katherine Raudenbush
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Eleftherios Terry Papoutsakis
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA; Delaware Biotechnology Institute, Department of Biological Sciences, University of Delaware, USA.
| | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA.
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6
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Wang X, Mohsin A, Sun Y, Li C, Zhuang Y, Wang G. From Spatial-Temporal Multiscale Modeling to Application: Bridging the Valley of Death in Industrial Biotechnology. Bioengineering (Basel) 2023; 10:744. [PMID: 37370675 DOI: 10.3390/bioengineering10060744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.
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Affiliation(s)
- Xueting Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Ali Mohsin
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Yifei Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Chao Li
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Guan Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
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7
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Elmore JR, Dexter GN, Baldino H, Huenemann JD, Francis R, Peabody GL, Martinez-Baird J, Riley LA, Simmons T, Coleman-Derr D, Guss AM, Egbert RG. High-throughput genetic engineering of nonmodel and undomesticated bacteria via iterative site-specific genome integration. SCIENCE ADVANCES 2023; 9:eade1285. [PMID: 36897939 PMCID: PMC10005180 DOI: 10.1126/sciadv.ade1285] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 02/01/2023] [Indexed: 05/31/2023]
Abstract
Efficient genome engineering is critical to understand and use microbial functions. Despite recent development of tools such as CRISPR-Cas gene editing, efficient integration of exogenous DNA with well-characterized functions remains limited to model bacteria. Here, we describe serine recombinase-assisted genome engineering, or SAGE, an easy-to-use, highly efficient, and extensible technology that enables selection marker-free, site-specific genome integration of up to 10 DNA constructs, often with efficiency on par with or superior to replicating plasmids. SAGE uses no replicating plasmids and thus lacks the host range limitations of other genome engineering technologies. We demonstrate the value of SAGE by characterizing genome integration efficiency in five bacteria that span multiple taxonomy groups and biotechnology applications and by identifying more than 95 heterologous promoters in each host with consistent transcription across environmental and genetic contexts. We anticipate that SAGE will rapidly expand the number of industrial and environmental bacteria compatible with high-throughput genetics and synthetic biology.
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Affiliation(s)
- Joshua R. Elmore
- Biological Science Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Gara N. Dexter
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
| | - Henri Baldino
- Biological Science Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Jay D. Huenemann
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, TN 37996,USA
| | - Ryan Francis
- Biological Science Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - George L. Peabody
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
| | - Jessica Martinez-Baird
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
| | - Lauren A. Riley
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, TN 37996,USA
| | - Tuesday Simmons
- Plant and Microbial Biology Department, University of California, Berkeley, CA 94701, USA
| | - Devin Coleman-Derr
- Plant and Microbial Biology Department, University of California, Berkeley, CA 94701, USA
- Plant Gene Expression Center, USDA-ARS, Albany, CA 94710, USA
| | - Adam M. Guss
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
| | - Robert G. Egbert
- Biological Science Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
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8
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Mu X, Zhang F. Diverse mechanisms of bioproduction heterogeneity in fermentation and their control strategies. J Ind Microbiol Biotechnol 2023; 50:kuad033. [PMID: 37791393 PMCID: PMC10583207 DOI: 10.1093/jimb/kuad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 09/28/2023] [Indexed: 10/05/2023]
Abstract
Microbial bioproduction often faces challenges related to populational heterogeneity, where cells exhibit varying biosynthesis capabilities. Bioproduction heterogeneity can stem from genetic and non-genetic factors, resulting in decreased titer, yield, stability, and reproducibility. Consequently, understanding and controlling bioproduction heterogeneity are crucial for enhancing the economic competitiveness of large-scale biomanufacturing. In this review, we provide a comprehensive overview of current understandings of the various mechanisms underlying bioproduction heterogeneity. Additionally, we examine common strategies for controlling bioproduction heterogeneity based on these mechanisms. By implementing more robust measures to mitigate heterogeneity, we anticipate substantial enhancements in the scalability and stability of bioproduction processes. ONE-SENTENCE SUMMARY This review summarizes current understandings of different mechanisms of bioproduction heterogeneity and common control strategies based on these mechanisms.
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Affiliation(s)
- Xinyue Mu
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Division of Biological & Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA
- Institute of Materials Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
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9
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Blöbaum L, Haringa C, Grünberger A. Microbial lifelines in bioprocesses: From concept to application. Biotechnol Adv 2023; 62:108071. [PMID: 36464144 DOI: 10.1016/j.biotechadv.2022.108071] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022]
Abstract
Bioprocesses are scaled up for the production of large product quantities. With larger fermenter volumes, mixing becomes increasingly inefficient and environmental gradients get more prominent than in smaller scales. Environmental gradients have an impact on the microorganism's metabolism, which makes the prediction of large-scale performance difficult and can lead to scale-up failure. A promising approach for improved understanding and estimation of dynamics of microbial populations in large-scale bioprocesses is the analysis of microbial lifelines. The lifeline of a microbe in a bioprocess is the experience of environmental gradients from a cell's perspective, which can be described as a time series of position, environment and intracellular condition. Currently, lifelines are predominantly determined using models with computational fluid dynamics, but new technical developments in flow-following sensor particles and microfluidic single-cell cultivation open the door to a more interdisciplinary concept. We critically review the current concepts and challenges in lifeline determination and application of lifeline analysis, as well as strategies for the integration of these techniques into bioprocess development. Lifelines can contribute to a successful scale-up by guiding scale-down experiments and identifying strain engineering targets or bioreactor optimisations.
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Affiliation(s)
- Luisa Blöbaum
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Bielefeld, Germany; CeBiTec, Bielefeld University, Bielefeld, Germany
| | - Cees Haringa
- Bioprocess Engineering, Applied Sciences/Biotechnology, TU, Delft, Netherlands
| | - Alexander Grünberger
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Bielefeld, Germany; CeBiTec, Bielefeld University, Bielefeld, Germany; Microsystems in Bioprocess Engineering, Institute of Process Engineering in Life Sciences, Karlsruhe Institute of Technology, Karlsruhe, Germany.
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10
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Ngu V, Fletcher DF, Kavanagh JM, Rafrafi Y, Dumas C, Morchain J, Cockx A. H2 mass transfer – a key factor for efficient biological methanation: Comparison between pilot-scale experimental data, 1D and CFD models. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.118382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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11
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Losoi P, Konttinen J, Santala V. Substantial gradient mitigation in simulated large-scale bioreactors by optimally placed multiple feed points. Biotechnol Bioeng 2022; 119:3549-3566. [PMID: 36110051 PMCID: PMC9828524 DOI: 10.1002/bit.28232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/29/2022] [Accepted: 09/11/2022] [Indexed: 01/12/2023]
Abstract
The performance of large-scale stirred tank and bubble column bioreactors is often hindered by insufficient macromixing of feeds, leading to heterogeneities in pH, substrate, and oxygen, which complicates process scale-up. Appropriate feed placement or the use of multiple feed points could improve mixing. Here, theoretically optimal placement of feed points was derived using one-dimensional diffusion equations. The utility of optimal multipoint feeds was evaluated with mixing, pH control, and bioreaction simulations using three-dimensional compartment models of four industrially relevant bioreactors with working volumes ranging from 8 to 237 m3 . Dividing the vessel axially in equal-sized compartments and locating a feed point or multiple feed points symmetrically in each compartment reduced the mixing time substantially by more than a minute and mitigated gradients of pH, substrate, and oxygen. Performance of the large-scale bioreactors was consequently restored to ideal, homogeneous reactor performance: oxygen consumption and biomass yield were recovered and the phenotypical heterogeneity of the biomass population was diminished.
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Affiliation(s)
- Pauli Losoi
- Faculty of Engineering and Natural SciencesTampere UniversityTampereFinland
| | - Jukka Konttinen
- Faculty of Engineering and Natural SciencesTampere UniversityTampereFinland
| | - Ville Santala
- Faculty of Engineering and Natural SciencesTampere UniversityTampereFinland
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12
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Hartmann FSF, Udugama IA, Seibold GM, Sugiyama H, Gernaey KV. Digital models in biotechnology: Towards multi-scale integration and implementation. Biotechnol Adv 2022; 60:108015. [PMID: 35781047 DOI: 10.1016/j.biotechadv.2022.108015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/03/2022] [Accepted: 06/27/2022] [Indexed: 12/28/2022]
Abstract
Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.
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Affiliation(s)
- Fabian S F Hartmann
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800 Kgs. Lyngby, Denmark
| | - Isuru A Udugama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan; Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 228 A, 2800 Kgs. Lyngby, Denmark.
| | - Gerd M Seibold
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800 Kgs. Lyngby, Denmark
| | - Hirokazu Sugiyama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Krist V Gernaey
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 228 A, 2800 Kgs. Lyngby, Denmark.
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13
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Zakrzewski R, Lee K, Lye GJ. Development of a miniature bioreactor model to study the impact of pH and DOT fluctuations on CHO cell culture performance as a tool to understanding heterogeneity effects at large-scale. Biotechnol Prog 2022; 38:e3264. [PMID: 35441833 PMCID: PMC9542549 DOI: 10.1002/btpr.3264] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 04/07/2022] [Indexed: 12/04/2022]
Abstract
Understanding the impact of spatial heterogeneities that are known to occur in large‐scale cell culture bioreactors remains a significant challenge. This work presents a novel methodology for mimicking the effects of pH and dissolved oxygen heterogeneities on Chinese hamster ovary (CHO) cell culture performance and antibody quality characteristics, using an automated miniature bioreactor system. Cultures of 4 different cell lines, expressing 3 IgG molecules and one fusion protein, were exposed to repeated pH and dissolved oxygen tension (DOT) fluctuations between pH 7.0–7.5 and DOT 10%–30%, respectively, for durations of 15, 30, and 60 min. Fluctuations in pH had a minimal impact on growth, productivity, and product quality although some changes in lactate metabolism were observed. DOT fluctuations were found to have a more significant impact; a 35% decrease in cell growth and product titre was observed in the fastest growing cell line tested, while all cell lines exhibited a significant increase in lactate accumulation. Product quality analysis yielded varied results; two cell lines showed an increase in the G0F glycan and decrease in G1F, G2F, and Man5; however, another line showed the opposite trend. The study suggests that the response of CHO cells to the effects of fluctuating culture conditions is cell line specific and that higher growing cell lines are most impacted. The miniature bioreactor system described in this work therefore provides a platform for use during early stage cell culture process development to identify cell lines that may be adversely impacted by the pH and DOT heterogeneities encountered on scale‐up. This experimental data can be combined with computational modeling approaches to predict overall cell culture performance in large‐scale bioreactors.
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Affiliation(s)
- Roman Zakrzewski
- The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, University College London, London, UK
| | - Kenneth Lee
- Cell Culture and Fermentation Science, R&D, AstraZeneca, Franklin Building, Granta Park, Cambridge, UK
| | - Gary J Lye
- The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, University College London, London, UK
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14
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Spatio-temporal 1D gas–liquid model for biological methanation in lab scale and industrial bubble column. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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15
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Haringa C, Tang W, Noorman HJ. Stochastic parcel tracking in an Euler-Lagrange compartment model for fast simulation of fermentation processes. Biotechnol Bioeng 2022; 119:1849-1860. [PMID: 35352339 PMCID: PMC9321588 DOI: 10.1002/bit.28094] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 02/02/2022] [Indexed: 11/23/2022]
Abstract
The compartment model (CM) is a well‐known approach for computationally affordable, spatially resolved hydrodynamic modeling of unit operations. Recent implementations use flow profiles based on Computational Fluid Dynamics (CFD) simulations, and several authors included microbial kinetics to simulate gradients in bioreactors. However, these studies relied on black‐box kinetics that do not account for intracellular changes and cell population dynamics in response to heterogeneous environments. In this paper, we report the implementation of a Lagrangian reaction model, where the microbial phase is tracked as a set of biomass‐parcels, each linked with an intracellular composition vector and a structured reaction model describing their intracellular response to extracellular variations. A stochastic parcel tracking approach is adopted, in contrast to the resolved trajectories used in CFD implementations. A penicillin production process is used as a case study. We show good performance of the model compared with full CFD simulations, both regarding the extracellular gradients and intracellular pool response, using the mixing time as a matching criterion and taking into account that the mixing time is sensitive to the number of compartments. The sensitivity of the model output towards some of the inputs is explored. The coarsest representative CM requires a few minutes to solve 80 h of flow time, compared with approximately 2 weeks for a full Euler–Lagrange CFD simulation of the same case. This alleviates one of the major bottlenecks for the application of such CFD simulations towards the analysis and optimization of industrial fermentation processes.
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Affiliation(s)
- Cees Haringa
- Biotechnology Department, Bioprocess EngineeringDelft University of TechnologyDelftThe Netherlands
| | - Wenjun Tang
- Biotechnology Department, Bioprocess EngineeringDelft University of TechnologyDelftThe Netherlands
- Department of Biotechnology, Bioprocess Engineering group, Faculty of Applied Sciences, Delft University of TechnologyRoyal DSMDelftThe Netherlands
| | - Henk J. Noorman
- Biotechnology Department, Bioprocess EngineeringDelft University of TechnologyDelftThe Netherlands
- Department of Biotechnology, Bioprocess Engineering group, Faculty of Applied Sciences, Delft University of TechnologyRoyal DSMDelftThe Netherlands
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16
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Non-Idealities in Lab-Scale Kinetic Testing: A Theoretical Study of a Modular Temkin Reactor. Catalysts 2022. [DOI: 10.3390/catal12030349] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The Temkin reactor can be applied for industrial relevant catalyst testing with unmodified catalyst particles. It was assumed in the literature that this reactor behaves as a cascade of continuously stirred tank reactors (CSTR). However, this assumption was based only on outlet gas composition or inert residence time distribution measurements. The present work theoretically investigates the catalytic CO2 methanation as a test case on different catalyst geometries, a sphere, and a ring, inside a single Temkin reaction chamber under isothermal conditions. Axial gas-phase species profiles from detailed computational fluid dynamics (CFD) are compared with a CSTR and 1D plug-flow reactor (PFR) model using a sophisticated microkinetic model. In addition, a 1D chemical reactor network (CRN) model was developed, and model parameters were adjusted based on the CFD simulations. Whereas the ideal reactor models overpredict the axial product concentrations, the CRN model results agree well with the CFD simulations, especially under low to medium flow rates. This study shows that complex flow patterns greatly influence species fields inside the Temkin reactor. Although residence time measurements suggest CSTR-like behavior, the reactive flow cannot be described by either a CSTR or PFR model but with the developed CRN model.
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17
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Xu Y, Wu Y, Lv X, Sun G, Zhang H, Chen T, Du G, Li J, Liu L. Design and construction of novel biocatalyst for bioprocessing: Recent advances and future outlook. BIORESOURCE TECHNOLOGY 2021; 332:125071. [PMID: 33826982 DOI: 10.1016/j.biortech.2021.125071] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/19/2021] [Accepted: 03/23/2021] [Indexed: 06/12/2023]
Abstract
Bioprocess, a biocatalysis-based technology, is becoming popular in many research fields and widely applied in industrial manufacturing. However, low bioconversion, low productivity, and high costs during industrial processes are usually the limitation in bioprocess. Therefore, many biocatalyst strategies have been developed to meet these challenges in recent years. In this review, we firstly discuss protein engineering strategies, which are emerged for improving the biocatalysis activity of biocatalysts. Then, we summarize metabolic engineering strategies that are promoting the development of microbial cell factories. Next, we illustrate the necessity of using the combining strategy of protein engineering and metabolic engineering for efficient biocatalysts. Lastly, future perspectives about the development and application of novel biocatalyst strategies are discussed. This review provides theoretical guidance for the development of efficient, sustainable, and economical bioprocesses mediated by novel biocatalysts.
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Affiliation(s)
- Yameng Xu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Yaokang Wu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Guoyun Sun
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Hongzhi Zhang
- Shandong Runde Biotechnology Co., Ltd., Tai'an 271000, PR China
| | - Taichi Chen
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China.
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18
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Morchain J, Quedeville V, Fox RO, Villedieu P. The closure issue related to liquid-cell mass transfer and substrate uptake dynamics in biological systems. Biotechnol Bioeng 2021; 118:2435-2447. [PMID: 33713345 DOI: 10.1002/bit.27752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 01/12/2021] [Accepted: 03/11/2021] [Indexed: 11/11/2022]
Abstract
An original dynamic model for substrate uptake under transient conditions is established and used to simulate a variety of biological responses to external perturbations. The actual uptake and growth rates, treated as cell properties, are part of the model variables as well as the substrate concentration at the cell-liquid interface. Several regulatory loops inspired by the structure of the glycolytic chain are considered to establish a set of ordinary differential equations. The uptake rate evolves so as to reach an equilibrium between the cell demand and the environmental supply. This model does not contain any of the usual algebraic closure laws relating to the instantaneous uptake, growth rates, and the substrate concentration, nor does it enforce the continuity of mass fluxes at the liquid-cell interface. However, these relationships are found in the steady-state solution. Previously unexplained experimental observations are well reproduced by this model. Also, the model structure is suitable for further coupling with flux-based metabolic models and fluid-flow equations.
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Affiliation(s)
- Jérôme Morchain
- TBI, CNRS, INRA, INSA, Université de Toulouse, Toulouse, France.,FERMaT, CNRS, INPT, INSA, UPS, Université de Toulouse, Toulouse, France
| | - Vincent Quedeville
- TBI, CNRS, INRA, INSA, Université de Toulouse, Toulouse, France.,FERMaT, CNRS, INPT, INSA, UPS, Université de Toulouse, Toulouse, France
| | - Rodney O Fox
- FERMaT, CNRS, INPT, INSA, UPS, Université de Toulouse, Toulouse, France.,Department of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA
| | - Philippe Villedieu
- Institut de Mathématiques de Toulouse, Université de Toulouse, Toulouse, France.,ONERA/DMPE, Université de Toulouse, Toulouse, France
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19
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Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2021. [PMID: 32978650 DOI: 10.1007/10_2020_133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Eulerian-Lagrangian approach to investigate cellular responses in a bioreactor has become the center of attention in recent years. It was introduced to biotechnological processes about two decades ago, but within the last few years, it proved itself as a powerful tool to address scale-up and -down topics of bioprocesses. It can capture the history of a cell and reveal invaluable information for, not only, bioprocess control and design but also strain engineering. This way it will be possible to shed light on the actual environment that cell experiences throughout its lifespan. Lifelines of a microorganism in a bioreactor can serve as the missing link that encompasses the biological timescales and the physical timescales. For this purpose digitalization of bioreactors provides us with new insights that are not achievable in industrial reactors easily if at all, namely, substrate and product gradients; high-shear regions are among the most interesting factors that can be reproduced adequately with help of a digital twin. In this chapter basic principles of this method will be introduced, and later on some practical aspects of particle tracking technique will be illustrated. In the final section, some of the advantages and challenges associated with this method will be discussed.
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20
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Ma R, Fang H, Liu H, Pan L, Wang H, Zhang H. Overexpression of uracil permease and nucleoside transporter from Bacillus amyloliquefaciens improves cytidine production in Escherichia coli. Biotechnol Lett 2021; 43:1211-1219. [PMID: 33646457 DOI: 10.1007/s10529-021-03103-3] [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: 08/08/2020] [Accepted: 02/08/2021] [Indexed: 11/28/2022]
Abstract
Cytidine is an important raw material for nucleic acid health food and genetic engineering research. In recent years, it has shown irreplaceable effects in anti-virus, anti-tumor, and AIDS drugs. Its biosynthetic pathway is complex and highly regulated. In this study, overexpression of uracil permease and a nucleoside transporter from Bacillus amyloliquefaciens related to cell membrane transport in Escherichia coli strain BG-08 was found to increase cytidine production in shake flask cultivation by 1.3-fold (0.91 ± 0.03 g/L) and 1.8-fold (1.26 ± 0.03 g/L) relative to that of the original strain (0.70 ± 0.03 g/L), respectively. Co-overexpression of uracil permease and a nucleoside transporter further increased cytidine yield by 2.7-fold (1.59 ± 0.05 g/L) compared with that of the original strain. These results indicate that the overexpressed uracil permease and nucleoside transporter can promote the accumulation of cytidine, and the two proteins play a synergistic role in the secretion of cytidine in Escherichia coli.
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Affiliation(s)
- Ruoshuang Ma
- Ningxia Key Laboratory for Food Microbial-Applications Technology and Safety Control, School of Agriculture, Ningxia University, Yinchuan, 750021, China
| | - Haitian Fang
- Ningxia Key Laboratory for Food Microbial-Applications Technology and Safety Control, School of Agriculture, Ningxia University, Yinchuan, 750021, China
| | - Huiyan Liu
- Ningxia Key Laboratory for Food Microbial-Applications Technology and Safety Control, School of Agriculture, Ningxia University, Yinchuan, 750021, China.
| | - Lin Pan
- Ningxia Key Laboratory for Food Microbial-Applications Technology and Safety Control, School of Agriculture, Ningxia University, Yinchuan, 750021, China
| | - Hongyan Wang
- Ningxia Key Laboratory for Food Microbial-Applications Technology and Safety Control, School of Agriculture, Ningxia University, Yinchuan, 750021, China
| | - Heng Zhang
- Ningxia Key Laboratory for Food Microbial-Applications Technology and Safety Control, School of Agriculture, Ningxia University, Yinchuan, 750021, China
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21
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Hartline CJ, Schmitz AC, Han Y, Zhang F. Dynamic control in metabolic engineering: Theories, tools, and applications. Metab Eng 2021; 63:126-140. [PMID: 32927059 PMCID: PMC8015268 DOI: 10.1016/j.ymben.2020.08.015] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/15/2020] [Accepted: 08/26/2020] [Indexed: 12/12/2022]
Abstract
Metabolic engineering has allowed the production of a diverse number of valuable chemicals using microbial organisms. Many biological challenges for improving bio-production exist which limit performance and slow the commercialization of metabolically engineered systems. Dynamic metabolic engineering is a rapidly developing field that seeks to address these challenges through the design of genetically encoded metabolic control systems which allow cells to autonomously adjust their flux in response to their external and internal metabolic state. This review first discusses theoretical works which provide mechanistic insights and design choices for dynamic control systems including two-stage, continuous, and population behavior control strategies. Next, we summarize molecular mechanisms for various sensors and actuators which enable dynamic metabolic control in microbial systems. Finally, important applications of dynamic control to the production of several metabolite products are highlighted, including fatty acids, aromatics, and terpene compounds. Altogether, this review provides a comprehensive overview of the progress, advances, and prospects in the design of dynamic control systems for improved titer, rate, and yield metrics in metabolic engineering.
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Affiliation(s)
- Christopher J Hartline
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA
| | - Alexander C Schmitz
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA
| | - Yichao Han
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA; Division of Biological & Biomedical Sciences, Washington University in St. Louis, Saint Louis, MO, 63130, USA; Institute of Materials Science & Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA.
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22
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Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2021. [PMID: 33381857 DOI: 10.1007/10_2020_154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Typically, bioprocesses on an industrial scale are dynamic systems with a certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale and vice versa is not a trivial task. Traditional scale-down methodologies consider several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial bioreactor over the entire process, from the perspective of a cell. Supported by the enormous increase in computing power, the latest scientific focus is on the application of dynamic models, in combination with computational fluid dynamics to quantitatively describe cell behavior. These models allow the description of possible cellular lifelines which in turn can be used to derive a regime analysis for scale-down experiments. However, the approaches described so far, which were for a very few process examples, are very labor- and time-intensive and cannot be validated easily. In parallel, alternatives have been developed based on the description of the industrial process with hybrid process models, which describe a process mechanistically as far as possible in order to determine the essential process parameters with their respective variances. On-line analytical methods allow the characterization of population heterogeneity directly in the process. This detailed information from the industrial process can be used in laboratory screening systems to select relevant conditions in which the cell and process related parameters reflect the situation in the industrial scale. In our opinion, these technologies, which are available in research for modeling biological systems, in combination with process analytical techniques are so far developed that they can be implemented in industrial routines for faster development of new processes and optimization of existing ones.
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23
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Predicting By-Product Gradients of Baker’s Yeast Production at Industrial Scale: A Practical Simulation Approach. Processes (Basel) 2020. [DOI: 10.3390/pr8121554] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.
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24
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Understanding gradients in industrial bioreactors. Biotechnol Adv 2020; 46:107660. [PMID: 33221379 DOI: 10.1016/j.biotechadv.2020.107660] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 10/22/2020] [Accepted: 11/14/2020] [Indexed: 01/07/2023]
Abstract
Gradients in industrial bioreactors have attracted substantial research attention since exposure to fluctuating environmental conditions has been shown to lead to changes in the metabolome, transcriptome as well as population heterogeneity in industrially relevant microorganisms. Such changes have also been found to impact key process parameters like the yield on substrate and the productivity. Hence, understanding gradients is important from both the academic and industrial perspectives. In this review the causes of gradients are outlined, along with their impact on microbial physiology. Quantifying the impact of gradients requires a detailed understanding of both fluid flow inside industrial equipment and microbial physiology. This review critically examines approaches used to investigate gradients including large-scale experimental work, computational methods and scale-down approaches. Avenues for future work have been highlighted, particularly the need for further coordinated development of both in silico and experimental tools which can be used to further the current understanding of gradients in industrial equipment.
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25
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Wang G, Haringa C, Noorman H, Chu J, Zhuang Y. Developing a Computational Framework To Advance Bioprocess Scale-Up. Trends Biotechnol 2020; 38:846-856. [DOI: 10.1016/j.tibtech.2020.01.009] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 01/27/2020] [Accepted: 01/29/2020] [Indexed: 01/10/2023]
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26
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Kuschel M, Takors R. Simulated oxygen and glucose gradients as a prerequisite for predicting industrial scale performance a priori. Biotechnol Bioeng 2020; 117:2760-2770. [DOI: 10.1002/bit.27457] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 05/03/2020] [Accepted: 06/05/2020] [Indexed: 12/14/2022]
Affiliation(s)
- Maike Kuschel
- Institute of Biochemical EngineeringUniversity of Stuttgart Stuttgart Germany
| | - Ralf Takors
- Institute of Biochemical EngineeringUniversity of Stuttgart Stuttgart Germany
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27
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Siebler F, Lapin A, Takors R. Synergistically applying 1-D modeling and CFD for designing industrial scale bubble column syngas bioreactors. Eng Life Sci 2020; 20:239-251. [PMID: 32647503 PMCID: PMC7336164 DOI: 10.1002/elsc.201900132] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 01/08/2020] [Accepted: 01/31/2020] [Indexed: 01/04/2023] Open
Abstract
The reduction of greenhouse gas emissions and future perspectives of circular economy ask for new solutions to produce commodities and fine chemicals. Large-scale bubble columns operated by gaseous substrates such as CO, CO2, and H2 to feed acetogens for product formations could be promising approaches. Valid in silico predictions of large-scale performance are needed to dimension bioreactors properly taking into account biological constraints, too. This contribution deals with the trade-off between sophisticated spatiotemporally resolved large-scale simulations using computationally intensive Euler-Euler and Euler-Lagrange approaches and coarse-grained 1-D models enabling fast performance evaluations. It is shown that proper consideration of gas hold-up is key to predict biological performance. Intrinsic bias of 1-D models can be compensated by reconsideration of Sauter diameters derived from uniquely performed Euler-Lagrange computational fluid dynamics.
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Affiliation(s)
- Flora Siebler
- Institute of Biochemical EngineeringUniversity of StuttgartStuttgartGermany
| | - Alexey Lapin
- Stuttgart Research Centre Systems BiologyUniversity of StuttgartStuttgartGermany
| | - Ralf Takors
- Institute of Biochemical EngineeringUniversity of StuttgartStuttgartGermany
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28
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Paul K, Herwig C. Scale-down simulators for mammalian cell culture as tools to access the impact of inhomogeneities occurring in large-scale bioreactors. Eng Life Sci 2020; 20:197-204. [PMID: 32874183 PMCID: PMC7447876 DOI: 10.1002/elsc.201900162] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 12/23/2019] [Accepted: 12/27/2019] [Indexed: 12/19/2022] Open
Abstract
During the scale-up of a bioprocess, not all characteristics of the process can be kept constant throughout the different scales. This typically results in increased mixing times with increasing reactor volumes. The poor mixing leads in turn to the formation of concentration gradients throughout the reactor and exposes cells to varying external conditions based on their location in the bioreactor. This can affect process performance and complicate process scale-up. Scale-down simulators, which aim at replicating the large-scale environment, expose the cells to changing environmental conditions. This has the potential to reveal adaptation mechanisms, which cells are using to adjust to rapidly fluctuating environmental conditions and can identify possible root causes for difficulties maintaining similar process performance at different scales. This understanding is of utmost importance in process validation. Additionally, these simulators also have the potential to be used for selecting cells, which are most robust when encountering changing extracellular conditions. The aim of this review is to summarize recent work in this interesting and promising area with the focus on mammalian bioprocesses, since microbial processes have been extensively reviewed.
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Affiliation(s)
- Katrin Paul
- Institute of Chemical, Environmental and Bioscience EngineeringViennaAustria
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved BioprocessesViennaAustria
| | - Christoph Herwig
- Institute of Chemical, Environmental and Bioscience EngineeringViennaAustria
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved BioprocessesViennaAustria
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29
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Maluta F, Pigou M, Montante G, Morchain J. Modeling the effects of substrate fluctuations on the maintenance rate in bioreactors with a probabilistic approach. Biochem Eng J 2020. [DOI: 10.1016/j.bej.2020.107536] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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30
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Jourdan N, Neveux T, Potier O, Kanniche M, Wicks J, Nopens I, Rehman U, Le Moullec Y. Compartmental Modelling in chemical engineering: A critical review. Chem Eng Sci 2019. [DOI: 10.1016/j.ces.2019.115196] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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31
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The impact of CO gradients on C. ljungdahlii in a 125 m3 bubble column: Mass transfer, circulation time and lifeline analysis. Chem Eng Sci 2019. [DOI: 10.1016/j.ces.2019.06.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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32
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Haringa C, Mudde RF, Noorman HJ. From industrial fermentor to CFD-guided downscaling: what have we learned? Biochem Eng J 2018. [DOI: 10.1016/j.bej.2018.09.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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33
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A Novel Framework for Parameter and State Estimation of Multicellular Systems Using Gaussian Mixture Approximations. Processes (Basel) 2018. [DOI: 10.3390/pr6100187] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Multicellular systems play an important role in many biotechnological processes. Typically, these exhibit cell-to-cell variability, which has to be monitored closely for process control and optimization. However, some properties may not be measurable due to technical and financial restrictions. To improve the monitoring, model-based online estimators can be designed for their reconstruction. The multicellular dynamics is accounted for in the framework of population balance models (PBMs). These models are based on single cell kinetics, and each cellular state translates directly into an additional dimension of the obtained partial differential equations. As multicellular dynamics often require detailed single cell models and feature a high number of cellular components, the resulting population balance equations are often high-dimensional. Therefore, established state estimation concepts for PBMs based on discrete grids are not recommended due to the large computational effort. In this contribution a novel approach is proposed, which is based on the approximation of the underlying number density functions as the weighted sum of Gaussian distributions. Thus, the distribution is described by the characteristic properties of the individual Gaussians, like the mean and covariance. Thereby, the complex infinite dimensional estimation problem can be reduced to a finite dimension. The characteristic properties are estimated in a recursive approach. The method is evaluated for two academic benchmark examples, and the results indicate its potential for model-based online reconstruction for multicellular systems.
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34
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A Framework for the Development of Integrated and Computationally Feasible Models of Large-Scale Mammalian Cell Bioreactors. Processes (Basel) 2018. [DOI: 10.3390/pr6070082] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
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35
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Camarasa C, Chiron H, Daboussi F, Della Valle G, Dumas C, Farines V, Floury J, Gagnaire V, Gorret N, Leonil J, Mouret JR, O'Donohue MJ, Sablayrolles JM, Salmon JM, Saulnier L, Truan G. INRA's research in industrial biotechnology: For food, chemicals, materials and fuels. INNOV FOOD SCI EMERG 2018. [DOI: 10.1016/j.ifset.2017.11.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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Quedeville V, Ouazaite H, Polizzi B, Fox R, Villedieu P, Fede P, Létisse F, Morchain J. A two-dimensional population balance model for cell growth including multiple uptake systems. Chem Eng Res Des 2018. [DOI: 10.1016/j.cherd.2018.02.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Pigou M, Morchain J, Fede P, Penet MI, Laronze G. An assessment of methods of moments for the simulation of population dynamics in large-scale bioreactors. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2017.05.026] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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A population balance model for bioreactors combining interdivision time distributions and micromixing concepts. Biochem Eng J 2017. [DOI: 10.1016/j.bej.2016.09.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Haringa C, Deshmukh AT, Mudde RF, Noorman HJ. Euler-Lagrange analysis towards representative down-scaling of a 22 m 3 aerobic S. cerevisiae fermentation. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2017.01.014] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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González-Cabaleiro R, Mitchell AM, Smith W, Wipat A, Ofiţeru ID. Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling. Front Microbiol 2017; 8:1813. [PMID: 28970826 PMCID: PMC5609101 DOI: 10.3389/fmicb.2017.01813] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 09/05/2017] [Indexed: 01/02/2023] Open
Abstract
Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale.
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Affiliation(s)
- Rebeca González-Cabaleiro
- School of Engineering, Chemical Engineering, Newcastle UniversityNewcastle upon Tyne, United Kingdom
| | - Anca M Mitchell
- School of Engineering, Chemical Engineering, Newcastle UniversityNewcastle upon Tyne, United Kingdom
| | - Wendy Smith
- Interdisciplinary Computing and Complex BioSystems (ICOS), School of ComputingNewcastle University, Newcastle upon Tyne, United Kingdom
| | - Anil Wipat
- Interdisciplinary Computing and Complex BioSystems (ICOS), School of ComputingNewcastle University, Newcastle upon Tyne, United Kingdom
| | - Irina D Ofiţeru
- School of Engineering, Chemical Engineering, Newcastle UniversityNewcastle upon Tyne, United Kingdom
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Farzan P, Ierapetritou MG. Integrated modeling to capture the interaction of physiology and fluid dynamics in biopharmaceutical bioreactors. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2016.11.037] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Haringa C, Noorman HJ, Mudde RF. Lagrangian modeling of hydrodynamic–kinetic interactions in (bio)chemical reactors: Practical implementation and setup guidelines. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2016.07.031] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Haringa C, Tang W, Deshmukh AT, Xia J, Reuss M, Heijnen JJ, Mudde RF, Noorman HJ. Euler-Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines. Eng Life Sci 2016; 16:652-663. [PMID: 27917102 PMCID: PMC5129516 DOI: 10.1002/elsc.201600061] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2016] [Revised: 07/11/2016] [Accepted: 07/18/2016] [Indexed: 12/28/2022] Open
Abstract
The trajectories, referred to as lifelines, of individual microorganisms in an industrial scale fermentor under substrate limiting conditions were studied using an Euler‐Lagrange computational fluid dynamics approach. The metabolic response to substrate concentration variations along these lifelines provides deep insight in the dynamic environment inside a large‐scale fermentor, from the point of view of the microorganisms themselves. We present a novel methodology to evaluate this metabolic response, based on transitions between metabolic “regimes” that can provide a comprehensive statistical insight in the environmental fluctuations experienced by microorganisms inside an industrial bioreactor. These statistics provide the groundwork for the design of representative scale‐down simulators, mimicking substrate variations experimentally. To focus on the methodology we use an industrial fermentation of Penicillium chrysogenum in a simplified representation, dealing with only glucose gradients, single‐phase hydrodynamics, and assuming no limitation in oxygen supply, but reasonably capturing the relevant timescales. Nevertheless, the methodology provides useful insight in the relation between flow and component fluctuation timescales that are expected to hold in physically more thorough simulations. Microorganisms experience substrate fluctuations at timescales of seconds, in the order of magnitude of the global circulation time. Such rapid fluctuations should be replicated in truly industrially representative scale‐down simulators.
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Affiliation(s)
- Cees Haringa
- Transport Phenomena Section Department of Chemical Engineering Delft University of Technology Delft The Netherlands
| | - Wenjun Tang
- State key laboratory of Bioreactor Engineering East China University of Science and Technology (ECUST) Shanghai People's Republic of China
| | | | - Jianye Xia
- State key laboratory of Bioreactor Engineering East China University of Science and Technology (ECUST) Shanghai People's Republic of China
| | - Matthias Reuss
- Stuttgart Research Center Systems Biology (SRCSB) University of Stuttgart Stuttgart Germany
| | - Joseph J Heijnen
- Cell Systems Engineering Department of Biotechnology Delft University of Technology Delft The Netherlands
| | - Robert F Mudde
- Transport Phenomena Section Department of Chemical Engineering Delft University of Technology Delft The Netherlands
| | - Henk J Noorman
- DSM Biotechnology Center Delft The Netherlands; Bio Separation Technology Department of Biotechnology Delft University of Technology Delft The Netherlands
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