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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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Puiman L, Almeida Benalcázar E, Picioreanu C, Noorman HJ, Haringa C. Downscaling Industrial-Scale Syngas Fermentation to Simulate Frequent and Irregular Dissolved Gas Concentration Shocks. Bioengineering (Basel) 2023; 10:bioengineering10050518. [PMID: 37237589 DOI: 10.3390/bioengineering10050518] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/12/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023] Open
Abstract
In large-scale syngas fermentation, strong gradients in dissolved gas (CO, H2) concentrations are very likely to occur due to locally varying mass transfer and convection rates. Using Euler-Lagrangian CFD simulations, we analyzed these gradients in an industrial-scale external-loop gas-lift reactor (EL-GLR) for a wide range of biomass concentrations, considering CO inhibition for both CO and H2 uptake. Lifeline analyses showed that micro-organisms are likely to experience frequent (5 to 30 s) oscillations in dissolved gas concentrations with one order of magnitude. From the lifeline analyses, we developed a conceptual scale-down simulator (stirred-tank reactor with varying stirrer speed) to replicate industrial-scale environmental fluctuations at bench scale. The configuration of the scale-down simulator can be adjusted to match a broad range of environmental fluctuations. Our results suggest a preference for industrial operation at high biomass concentrations, as this would strongly reduce inhibitory effects, provide operational flexibility and enhance the product yield. The peaks in dissolved gas concentration were hypothesized to increase the syngas-to-ethanol yield due to the fast uptake mechanisms in C. autoethanogenum. The proposed scale-down simulator can be used to validate such results and to obtain data for parametrizing lumped kinetic metabolic models that describe such short-term responses.
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Affiliation(s)
- Lars Puiman
- Department of Biotechnology, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 Delft, The Netherlands
| | - Eduardo Almeida Benalcázar
- Department of Biotechnology, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 Delft, The Netherlands
| | - Cristian Picioreanu
- Biological and Environmental Science and Engineering, Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
| | - Henk J Noorman
- Department of Biotechnology, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 Delft, The Netherlands
- Royal DSM, Alexander Fleminglaan 1, 2613 Delft, The Netherlands
| | - Cees Haringa
- Department of Biotechnology, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 Delft, The Netherlands
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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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Numerical and Experimental Investigation of the Hydrodynamics in the Single-Use Bioreactor Mobius® CellReady 3 L. Bioengineering (Basel) 2022; 9:bioengineering9050206. [PMID: 35621484 PMCID: PMC9137553 DOI: 10.3390/bioengineering9050206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 11/30/2022] Open
Abstract
Two-way Euler-Lagrange simulations are performed to characterize the hydrodynamics in the single-use bioreactor Mobius® CellReady 3 L. The hydrodynamics in stirred tank bioreactors are frequently modeled with the Euler–Euler approach, which cannot capture the trajectories of single bubbles. The present study employs the two-way coupled Euler–Lagrange approach, which accounts for the individual bubble trajectories through Langrangian equations and considers their impact on the Eulerian liquid phase equations. Hydrodynamic process characteristics that are relevant for cell cultivation including the oxygen mass transfer coefficient, the mixing time, and the hydrodynamic stress are evaluated for different working volumes, sparger types, impeller speeds, and sparging rates. A microporous sparger and an open pipe sparger are considered where bubbles of different sizes are generated, which has a pronounced impact on the bubble dispersion and the volumetric oxygen mass transfer coefficient. It is found that only the microporous sparger provides sufficiently high oxygen transfer to support typical suspended mammalian cell lines. The simulated mixing time and the volumetric oxygen mass transfer coefficient are successfully validated with experimental results. Due to the small reactor size, mixing times are below 25 s across all tested conditions. For the highest sparging rate of 100 mL min−1, the mixing time is found to be two seconds shorter than for a sparging rate of 50 mL min−1, which again, is 0.1 s longer than for a sparging rate of 10 mL min−1 at the same impeller speed of 100 rpm and the working volume of 1.7 L. The hydrodynamic stress in this bioreactor is found to be below critical levels for all investigated impeller speeds of up to 150 rpm, where the maximum levels are found in the region where the bubbles pass behind the impeller blades.
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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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6
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Haringa C. An analysis of organism lifelines in an industrial bioreactor using Lattice-Boltzmann CFD. Eng Life Sci 2022; 23:e2100159. [PMID: 36619885 PMCID: PMC9815090 DOI: 10.1002/elsc.202100159] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/03/2022] [Accepted: 02/24/2022] [Indexed: 01/11/2023] Open
Abstract
Euler-Lagrange CFD simulations, where the biotic phase is represented by computational particles (parcels), provide information on environmental gradients inside bioreactors from the microbial perspective. Such information is highly relevant for reactor scale-down and process optimization. One of the major challenges is the computational intensity of CFD simulations, especially when resolution of dynamics in the flowfield is required. Lattice-Boltzmann large-eddy simulations (LB-LES) form a very promising approach for simulating accurate, dynamic flowfields in stirred reactors, at strongly reduced computation times compared to finite volume approaches. In this work, the performance of LB-LES in resolving substrate gradients in large-scale bioreactors is explored, combined with the inclusion of a Lagrangian biotic phase to provide the microbial perspective. In addition, the hydrodynamic performance of the simulations is confirmed by verification of hydrodynamic characteristics (radial velocity, turbulent kinetic energy, energy dissipation) in the impeller discharge stream of a 29 cm diameter stirred tank. The results are compared with prior finite volume simulation results, both in terms of hydrodynamic and biokinetic observations, and time requirements.
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Affiliation(s)
- Cees Haringa
- Bioprocess EngineeringBiotechnology DepartmentDelft University of TechnologyDelftthe Netherlands
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7
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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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Xia J, Wang G, Fan M, Chen M, Wang Z, Zhuang Y. Understanding the scale-up of fermentation processes from the viewpoint of the flow field in bioreactors and the physiological response of strains. Chin J Chem Eng 2021. [DOI: 10.1016/j.cjche.2020.12.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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9
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Usage of Digital Twins Along a Typical Process Development Cycle. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2020. [PMID: 33346864 DOI: 10.1007/10_2020_149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Digital methods for process design, monitoring, and control can convert classical trial-and-error bioprocess development to a quantitative engineering approach. By interconnecting hardware, software, data, and humans currently untapped process optimization potential can be accessed. The key component within such a framework is a digital twin interacting with its physical process counterpart. In this chapter, we show how digital twin guided process development can be applied on an exemplary microbial cultivation process. The usage of digital twins is described along a typical process development cycle, ranging from early strain characterization to real-time control applications. Along an illustrative case study on microbial upstream bioprocessing, we emphasize that digital twins can integrate entire process development cycles if the digital twin itself and the underlying models are continuously adapted to newly available data. Therefore, the digital twin can be regarded as a powerful knowledge management tool and a decision support system for efficient process development. Its full potential can be deployed in a real-time environment where targeted control actions can further improve process performance.
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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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Täuber S, von Lieres E, Grünberger A. Dynamic Environmental Control in Microfluidic Single-Cell Cultivations: From Concepts to Applications. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e1906670. [PMID: 32157796 DOI: 10.1002/smll.201906670] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Revised: 01/16/2020] [Indexed: 06/10/2023]
Abstract
Microfluidic single-cell cultivation (MSCC) is an emerging field within fundamental as well as applied biology. During the last years, most MSCCs were performed at constant environmental conditions. Recently, MSCC at oscillating and dynamic environmental conditions has started to gain significant interest in the research community for the investigation of cellular behavior. Herein, an overview of this topic is given and microfluidic concepts that enable oscillating and dynamic control of environmental conditions with a focus on medium conditions are discussed, and their application in single-cell research for the cultivation of both mammalian and microbial cell systems is demonstrated. Furthermore, perspectives for performing MSCC at complex dynamic environmental profiles of single parameters and multiparameters (e.g., pH and O2 ) in amplitude and time are discussed. The technical progress in this field provides completely new experimental approaches and lays the foundation for systematic analysis of cellular metabolism at fluctuating environments.
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Affiliation(s)
- Sarah Täuber
- Multiscale Bioengineering, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - Eric von Lieres
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
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12
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Wang G, Haringa C, Tang W, Noorman H, Chu J, Zhuang Y, Zhang S. Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses. Biotechnol Bioeng 2019; 117:844-867. [PMID: 31814101 DOI: 10.1002/bit.27243] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 11/28/2019] [Accepted: 11/30/2019] [Indexed: 12/13/2022]
Abstract
Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.
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Affiliation(s)
- Guan Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Cees Haringa
- Transport Phenomena, Chemical Engineering Department, Delft University of Technology, Delft, The Netherlands.,DSM Biotechnology Center, Delft, The Netherlands
| | - Wenjun Tang
- DSM Biotechnology Center, Delft, The Netherlands
| | - Henk Noorman
- DSM Biotechnology Center, Delft, The Netherlands.,Bioprocess Engineering, Department of Biotechnology, Delft University of Technology, Delft, The Netherlands
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Siliang Zhang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
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Lauterbach T, Ziebart N, Bley T, Walther T, Lenk F. Mobile Sensoren für die Biotechnologie – Ortsunabhängige, miniaturisierte Prozessmessung. CHEM-ING-TECH 2019. [DOI: 10.1002/cite.201900114] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Tim Lauterbach
- Technische Universität DresdenInstitute of Natural Materials Technology Bergstraße 120 01069 Dresden Deutschland
| | - Nandor Ziebart
- Technische Universität DresdenInstitute of Natural Materials Technology Bergstraße 120 01069 Dresden Deutschland
| | - Thomas Bley
- Technische Universität DresdenInstitute of Natural Materials Technology Bergstraße 120 01069 Dresden Deutschland
| | - Thomas Walther
- Technische Universität DresdenInstitute of Natural Materials Technology Bergstraße 120 01069 Dresden Deutschland
| | - Felix Lenk
- Technische Universität DresdenInstitute of Natural Materials Technology Bergstraße 120 01069 Dresden Deutschland
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14
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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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15
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Kaboré AK, Delaunay S, Blanchard F, Guedon E, Fick M, Olmos E. Study and modeling of fluctuating dissolved oxygen concentration impact on Corynebacterium glutamicum growth in a scale-down bioreactor. Process Biochem 2019. [DOI: 10.1016/j.procbio.2018.10.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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16
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de Lamotte A, Delafosse A, Calvo S, Toye D. Identifying dominant spatial and time characteristics of flow dynamics within free-surface baffled stirred-tanks from CFD simulations. Chem Eng Sci 2018. [DOI: 10.1016/j.ces.2018.07.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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17
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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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18
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Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization. Chem Eng Sci 2018. [DOI: 10.1016/j.ces.2017.09.020] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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19
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20
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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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21
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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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