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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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2
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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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3
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Hanspal N, DeVincentis B, Thomas JA. Modeling multiphase fluid flow, mass transfer, and chemical reactions in bioreactors using large-eddy simulation. Eng Life Sci 2022; 23:e2200020. [PMID: 36751475 PMCID: PMC9893763 DOI: 10.1002/elsc.202200020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 09/20/2022] [Accepted: 10/22/2022] [Indexed: 11/13/2022] Open
Abstract
We present a transient large eddy simulation (LES) modeling approach for simulating the interlinked physics describing free surface hydrodynamics, multiphase mixing, reaction kinetics, and mass transport in bioreactor systems. Presented case-studies include non-reacting and reacting bioreactor systems, modeled through the inclusion of uniform reaction rates and more complex biochemical reactions described using Contois type kinetics. It is shown that the presence of reactions can result in a non-uniform spatially varying species concentration field, the magnitude and extent of which is directly related to the reaction rates and the underlying variations in the local volumetric mass transfer coefficient.
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4
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Janoska A, Buijs J, van Gulik WM. Predicting the influence of combined oxygen and glucose gradients based on scale-down and modelling approaches for the scale-up of penicillin fermentations. Process Biochem 2022. [DOI: 10.1016/j.procbio.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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5
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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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6
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Wodołażski A. Metaheurystic optimization of CFD–multiphase population balance and biokinetics aeration stirrer tank bioreactor of sludge flocs for scale-up study with bio(de/re)flocculation. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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7
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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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8
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Janoska A, Verheijen JJ, Tang W, Lee Q, Sikkema B, van Gulik WM. Influence of oxygen concentration on the metabolism of Penicillium chrysogenum. Eng Life Sci 2022; 23:e2100139. [PMID: 36619886 PMCID: PMC9815084 DOI: 10.1002/elsc.202100139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 02/17/2022] [Accepted: 03/08/2022] [Indexed: 01/11/2023] Open
Abstract
In large-scale bioreactors, there is often insufficient mixing and as a consequence, cells experience uneven substrate and oxygen levels that influence product formation. In this study, the influence of dissolved oxygen (DO) gradients on the primary and secondary metabolism of a high producing industrial strain of Penicillium chrysogenum was investigated. Within a wide range of DO concentrations, obtained under chemostat conditions, we observed different responses from P. chrysogenum: (i) no influence on growth or penicillin production (>0.025 mmol L-1); (ii) reduced penicillin production, but no growth limitation (0.013-0.025 mmol L-1); and (iii) growth and penicillin production limitations (<0.013 mmol L-1). In addition, scale down experiments were performed by oscillating the DO concentration in the bioreactor. We found that during DO oscillation, the penicillin production rate decreased below the value observed when a constant DO equal to the average oscillating DO value was used. To understand and predict the influence of oxygen levels on primary metabolism and penicillin production, we developed a black box model that was linked to a detailed kinetic model of the penicillin pathway. The model simulations represented the experimental data during the step experiments; however, during the oscillation experiments the predictions deviated, indicating the involvement of the central metabolism in penicillin production.
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Affiliation(s)
- Agnes Janoska
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
| | - Jelle J. Verheijen
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
| | - Wenjung Tang
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands,DSM Biotechnology CenterAlexander Fleminglaan 1DelftNetherlands
| | - Queenie Lee
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
| | - Baukje Sikkema
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
| | - Walter M. van Gulik
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
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9
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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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10
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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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11
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Ziegler M, Zieringer J, Takors R. Transcriptional profiling of the stringent response mutant strain E. coli SR reveals enhanced robustness to large-scale conditions. Microb Biotechnol 2021; 14:993-1010. [PMID: 33369128 PMCID: PMC8085953 DOI: 10.1111/1751-7915.13738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 12/08/2020] [Accepted: 12/08/2020] [Indexed: 12/05/2022] Open
Abstract
In large-scale fed-batch production processes, microbes are exposed to heterogeneous substrate availability caused by long mixing times. Escherichia coli, the most common industrial host for recombinant protein production, reacts by recurring accumulation of the alarmone ppGpp and energetically wasteful transcriptional strategies. Here, we compare the regulatory responses of the stringent response mutant strain E. coli SR and its parent strain E. coli MG1655 to repeated nutrient starvation in a two-compartment scale-down reactor. Our data show that E. coli SR can withstand these stress conditions without a ppGpp-mediated stress response maintaining fully functional ammonium uptake and biomass formation. Furthermore, E. coli SR exhibited a substantially reduced short-term transcriptional response compared to E. coli MG1655 (less than half as many differentially expressed genes). E. coli SR proceeded adaptation via more general SOS response pathways by initiating negative regulation of transcription, translation and cell division. Our results show that locally induced stress responses propagating through the bioreactor do not result in cyclical induction and repression of genes in E. coli SR, but in a reduced and coordinated response, which makes it potentially suitable for large-scale production processes.
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Affiliation(s)
- Martin Ziegler
- Institute of Biochemical EngineeringUniversity of StuttgartStuttgartGermany
| | - Julia Zieringer
- Institute of Biochemical EngineeringUniversity of StuttgartStuttgartGermany
| | - Ralf Takors
- Institute of Biochemical EngineeringUniversity of StuttgartStuttgartGermany
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12
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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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Böl M, Schrinner K, Tesche S, Krull R. Challenges of influencing cellular morphology by morphology engineering techniques and mechanical induced stress on filamentous pellet systems-A critical review. Eng Life Sci 2021; 21:51-67. [PMID: 33716605 PMCID: PMC7923580 DOI: 10.1002/elsc.202000060] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/25/2020] [Accepted: 09/29/2020] [Indexed: 11/30/2022] Open
Abstract
Filamentous microorganisms are main producers of organic acids, enzymes, and pharmaceutical agents such as antibiotics and other active pharmaceutical ingredients. With their complex cell morphology, ranging from dispersed mycelia to dense pellets, the cultivation is challenging. In recent years, various techniques for tailor-made cell morphologies of filamentous microorganisms have been developed to increase product formation and have been summarised under the term morphology engineering. These techniques, namely microparticle-enhanced cultivation, macroparticle-enhanced cultivation, and alteration of the osmolality of the culture medium by addition of inorganic salts, the salt-enhanced cultivation, are presented and discussed in this review. These techniques have already proven to be useful and now await further proof-of-concept. Furthermore, the mechanical behaviour of individual pellets is of special interest for a general understanding of pellet mechanics and the productivity of biotechnological processes with filamentous microorganisms. Correlating them with substrate uptake and finally with productivity would be a breakthrough not to be underestimated for the comprehensive characterisation of filamentous systems. So far, this research field is under-represented. First results on filamentous pellet mechanics are discussed and important future aspects, which the filamentous expert community should deal with, will be presented and critically discussed.
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Affiliation(s)
- Markus Böl
- Institute of Mechanics and AdaptronicsTechnische Universität BraunschweigBraunschweigGermany
- Center of Pharmaceutical Engineering (PVZ)Technische Universität BraunschweigBraunschweigGermany
| | - Kathrin Schrinner
- Center of Pharmaceutical Engineering (PVZ)Technische Universität BraunschweigBraunschweigGermany
- Institute of Biochemical EngineeringTechnische Universität BraunschweigBraunschweigGermany
| | - Sebastian Tesche
- Center of Pharmaceutical Engineering (PVZ)Technische Universität BraunschweigBraunschweigGermany
- Institute of Biochemical EngineeringTechnische Universität BraunschweigBraunschweigGermany
| | - Rainer Krull
- Center of Pharmaceutical Engineering (PVZ)Technische Universität BraunschweigBraunschweigGermany
- Institute of Biochemical EngineeringTechnische Universität BraunschweigBraunschweigGermany
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14
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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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15
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Zieringer J, Wild M, Takors R. Data-driven in silico prediction of regulation heterogeneity and ATP demands of Escherichia coli in large-scale bioreactors. Biotechnol Bioeng 2020; 118:265-278. [PMID: 32940924 DOI: 10.1002/bit.27568] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 09/11/2020] [Accepted: 09/11/2020] [Indexed: 12/31/2022]
Abstract
Escherichia coli exposed to industrial-scale heterogeneous mixing conditions respond to external stress by initiating short-term metabolic and long-term strategic transcriptional programs. In native habitats, long-term strategies allow survival in severe stress but are of limited use in large bioreactors, where microenvironmental conditions may change right after said programs are started. Related on/off switching of genes causes additional ATP burden that may reduce the cellular capacity for producing the desired product. Here, we present an agent-based data-driven model linked to computational fluid dynamics, finally allowing to predict additional ATP needs of Escherichia coli K12 W3110 exposed to realistic large-scale bioreactor conditions. The complex model describes transcriptional up- and downregulation dynamics of about 600 genes starting from subminute range covering 28 h. The data-based approach was extracted from comprehensive scale-down experiments. Simulating mixing and mass transfer conditions in a 54 m3 stirred bioreactor, 120,000 E. coli cells were tracked while fluctuating between different zones of glucose availability. It was found that cellular ATP demands rise between 30% and 45% of growth decoupled maintenance needs, which may limit the production of ATP-intensive product formation accordingly. Furthermore, spatial analysis of individual cell transcriptional patterns reveal very heterogeneous gene amplifications with hot spots of 50%-80% messenger RNA upregulation in the upper region of the bioreactor. The phenomenon reflects the time-delayed regulatory response of the cells that propagate through the stirred tank. After 4.2 h, cells adapt to environmental changes but still have to bear an additional 6% ATP demand.
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Affiliation(s)
- Julia Zieringer
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany
| | - Moritz Wild
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany
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16
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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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17
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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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18
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Vasilakou E, van Loosdrecht MCM, Wahl SA. Escherichia coli metabolism under short-term repetitive substrate dynamics: adaptation and trade-offs. Microb Cell Fact 2020; 19:116. [PMID: 32471427 PMCID: PMC7260802 DOI: 10.1186/s12934-020-01379-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 05/25/2020] [Indexed: 12/04/2022] Open
Abstract
Background Microbial metabolism is highly dependent on the environmental conditions. Especially, the substrate concentration, as well as oxygen availability, determine the metabolic rates. In large-scale bioreactors, microorganisms encounter dynamic conditions in substrate and oxygen availability (mixing limitations), which influence their metabolism and subsequently their physiology. Earlier, single substrate pulse experiments were not able to explain the observed physiological changes generated under large-scale industrial fermentation conditions. Results In this study we applied a repetitive feast–famine regime in an aerobic Escherichia coli culture in a time-scale of seconds. The regime was applied for several generations, allowing cells to adapt to the (repetitive) dynamic environment. The observed response was highly reproducible over the cycles, indicating that cells were indeed fully adapted to the regime. We observed an increase of the specific substrate and oxygen consumption (average) rates during the feast–famine regime, compared to a steady-state (chemostat) reference environment. The increased rates at same (average) growth rate led to a reduced biomass yield (30% lower). Interestingly, this drop was not followed by increased by-product formation, pointing to the existence of energy-spilling reactions. During the feast–famine cycle, the cells rapidly increased their uptake rate. Within 10 s after the beginning of the feeding, the substrate uptake rate was higher (4.68 μmol/gCDW/s) than reported during batch growth (3.3 μmol/gCDW/s). The high uptake led to an accumulation of several intracellular metabolites, during the feast phase, accounting for up to 34% of the carbon supplied. Although the metabolite concentrations changed rapidly, the cellular energy charge remained unaffected, suggesting well-controlled balance between ATP producing and ATP consuming reactions. Conclusions The adaptation of the physiology and metabolism of E. coli under substrate dynamics, representative for large-scale fermenters, revealed the existence of several cellular mechanisms coping with stress. Changes in the substrate uptake system, storage potential and energy-spilling processes resulted to be of great importance. These metabolic strategies consist a meaningful step to further tackle reduced microbial performance, observed under large-scale cultivations.
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Affiliation(s)
- Eleni Vasilakou
- Department of Biotechnology, Delft University of Technology, Van der Maasweg, 2629 HZ, Delft, The Netherlands.
| | - Mark C M van Loosdrecht
- Department of Biotechnology, Delft University of Technology, Van der Maasweg, 2629 HZ, Delft, The Netherlands
| | - S Aljoscha Wahl
- Department of Biotechnology, Delft University of Technology, Van der Maasweg, 2629 HZ, Delft, The Netherlands.
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19
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Cappello V, Plais C, Vial C, Augier F. Bubble size and liquid-side mass transfer coefficient measurements in aerated stirred tank reactors with non-Newtonian liquids. Chem Eng Sci 2020. [DOI: 10.1016/j.ces.2019.115280] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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20
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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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21
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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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22
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Motion, fixation probability and the choice of an evolutionary process. PLoS Comput Biol 2019; 15:e1007238. [PMID: 31381556 PMCID: PMC6746388 DOI: 10.1371/journal.pcbi.1007238] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 09/16/2019] [Accepted: 07/02/2019] [Indexed: 11/21/2022] Open
Abstract
Seemingly minor details of mathematical and computational models of evolution are known to change the effect of population structure on the outcome of evolutionary processes. For example, birth-death dynamics often result in amplification of selection, while death-birth processes have been associated with suppression. In many biological populations the interaction structure is not static. Instead, members of the population are in motion and can interact with different individuals at different times. In this work we study populations embedded in a flowing medium; the interaction network is then time dependent. We use computer simulations to investigate how this dynamic structure affects the success of invading mutants, and compare these effects for different coupled birth and death processes. Specifically, we show how the speed of the motion impacts the fixation probability of an invading mutant. Flows of different speeds interpolate between evolutionary dynamics on fixed heterogeneous graphs and well-stirred populations; this allows us to systematically compare against known results for static structured populations. We find that motion has an active role in amplifying or suppressing selection by fragmenting and reconnecting the interaction graph. While increasing flow speeds suppress selection for most evolutionary models, we identify characteristic responses to flow for the different update rules we test. In particular we find that selection can be maximally enhanced or suppressed at intermediate flow speeds. Whether a mutation spreads in a population or not is one of the most important questions in biology. The evolution of cancer and antibiotic resistance, for example, are mediated by invading mutants. Recent work has shown that population structure can have important consequences for the outcome of evolution. For instance, a mutant can have a higher or a lower chance of invasion than in unstructured populations. These effects can depend on seemingly minor details of the evolutionary model, such as the order of birth and death events. Many biological populations are in motion, for example due to external stirring. Experimentally this is known to be important; the performance of mutants in E. coli populations, for example, depends on the rate of mixing. Here, we focus on simulations of populations in a flowing medium, and compare the success of a mutant for different flow speeds. We contrast different evolutionary models, and identify what features of the evolutionary model affect mutant success for different speeds of the flow. We find that the chance of mutant invasion can be at its highest (or lowest) at intermediate flow speeds, depending on the order in which birth and death events occur in the evolutionary process.
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23
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Spann R, Glibstrup J, Pellicer-Alborch K, Junne S, Neubauer P, Roca C, Kold D, Lantz AE, Sin G, Gernaey KV, Krühne U. CFD predicted pH gradients in lactic acid bacteria cultivations. Biotechnol Bioeng 2018; 116:769-780. [DOI: 10.1002/bit.26868] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 10/26/2018] [Indexed: 12/20/2022]
Affiliation(s)
- Robert Spann
- Department of Chemical and Biochemical Engineering; Technical University of Denmark, Kgs.; Lyngby Denmark
| | - Jens Glibstrup
- Department of Chemical and Biochemical Engineering; Technical University of Denmark, Kgs.; Lyngby Denmark
| | - Klaus Pellicer-Alborch
- Department of Biotechnology; Chair of Bioprocess Engineering, Technische Universität Berlin; Berlin Germany
| | - Stefan Junne
- Department of Biotechnology; Chair of Bioprocess Engineering, Technische Universität Berlin; Berlin Germany
| | - Peter Neubauer
- Department of Biotechnology; Chair of Bioprocess Engineering, Technische Universität Berlin; Berlin Germany
| | | | | | - Anna Eliasson Lantz
- Department of Chemical and Biochemical Engineering; Technical University of Denmark, Kgs.; Lyngby Denmark
| | - Gürkan Sin
- Department of Chemical and Biochemical Engineering; Technical University of Denmark, Kgs.; Lyngby Denmark
| | - Krist V. Gernaey
- Department of Chemical and Biochemical Engineering; Technical University of Denmark, Kgs.; Lyngby Denmark
| | - Ulrich Krühne
- Department of Chemical and Biochemical Engineering; Technical University of Denmark, Kgs.; Lyngby Denmark
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24
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Wright MR, Bach C, Gernaey KV, Krühne U. Investigation of the effect of uncertain growth kinetics on a CFD based model for the growth of S. cerevisiae in an industrial bioreactor. Chem Eng Res Des 2018. [DOI: 10.1016/j.cherd.2018.09.040] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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25
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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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26
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Gradov DV, Han M, Tervasmäki P, Latva-Kokko M, Vaittinen J, Pihlajamäki A, Koiranen T. Numerical Simulation of Biomass Growth in OKTOP®9000 Reactor at Industrial Scale. Ind Eng Chem Res 2018; 57:13300-13311. [PMID: 30416255 PMCID: PMC6219852 DOI: 10.1021/acs.iecr.8b02765] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 09/06/2018] [Accepted: 09/10/2018] [Indexed: 11/28/2022]
Abstract
![]()
Computational
fluid dynamics is a powerful method for scale-up
of reactors although it is still challenging to fully embrace hydrodynamics
and biological complexities. In this article, an aerobic fermentation
of Pichia pastoris cells is modeled in a batch OKTOP®9000
reactor. The 800 m3 industrial scale reactor is equipped
with a radial impeller, designed by Outotec Oy for gas dispersion
in the draft tube reactor. Measured Np of the impeller is used in hydrodynamics validation.
The resolved energy dissipation rate is compensated, and its influence
on mass transfer is analyzed and discussed. Gas–liquid drag
force is modified to simulate effects of liquid turbulence and bubble
swarms. Resolved steady state multiphase hydrodynamics is used to
simulate the fermentation process. Temporal evolution of species concentrations
is compared to experimental data measured in a small copy of the reactor
at lab scale (14 L). The effect of oxygenation on the P. pastoris cells cultivation is considered.
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Affiliation(s)
- Dmitry Vladimirovich Gradov
- School of Engineering Science, Lappeenranta University of Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland
| | - Mei Han
- School of Engineering Science, Lappeenranta University of Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland
| | - Petri Tervasmäki
- Chemical Process Engineering, University of Oulu, P.O. Box 4000, FI-90014 Oulu, Finland
| | - Marko Latva-Kokko
- Outotec (Finland) Oy, Outotec Research Center, P.O. Box 69, FI-23101 Pori, Finland
| | - Johanna Vaittinen
- Neste Engineering Solutions, NAPCON, P.O. Box 310, FI-06101 Porvoo, Finland
| | - Arto Pihlajamäki
- School of Engineering Science, Lappeenranta University of Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland
| | - Tuomas Koiranen
- School of Engineering Science, Lappeenranta University of Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland
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27
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Zieringer J, Takors R. In Silico Prediction of Large-Scale Microbial Production Performance: Constraints for Getting Proper Data-Driven Models. Comput Struct Biotechnol J 2018; 16:246-256. [PMID: 30105090 PMCID: PMC6077756 DOI: 10.1016/j.csbj.2018.06.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 06/11/2018] [Accepted: 06/12/2018] [Indexed: 12/20/2022] Open
Abstract
Industrial bioreactors range from 10.000 to 700.000 L and characteristically show different zones of substrate availabilities, dissolved gas concentrations and pH values reflecting physical, technical and economic constraints of scale-up. Microbial producers are fluctuating inside the bioreactors thereby experiencing frequently changing micro-environmental conditions. The external stimuli induce responses on microbial metabolism and on transcriptional regulation programs. Both may deteriorate the expected microbial production performance in large scale compared to expectations deduced from ideal, well-mixed lab-scale conditions. Accordingly, predictive tools are needed to quantify large-scale impacts considering bioreactor heterogeneities. The review shows that the time is right to combine simulations of microbial kinetics with calculations of large-scale environmental conditions to predict the bioreactor performance. Accordingly, basic experimental procedures and computational tools are presented to derive proper microbial models and hydrodynamic conditions, and to link both for bioreactor modeling. Particular emphasis is laid on the identification of gene regulatory networks as the implementation of such models will surely gain momentum in future studies.
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28
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Importance of the cultivation history for the response of Escherichia coli to oscillations in scale-down experiments. Bioprocess Biosyst Eng 2018; 41:1305-1313. [PMID: 29808419 DOI: 10.1007/s00449-018-1958-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/23/2018] [Indexed: 10/14/2022]
Abstract
Large-scale bioreactors are inhomogeneous systems, in which the fluid phase expresses concentration gradients. They depend on the mass transfer and fluid dynamics in the reactor, the feeding strategy, the cell-specific substrate uptake parameters, and the cell density. As high cell densities are only obtained at low specific growth rates, it is necessary to investigate the cellular responses to oscillations in particular under such conditions, an issue which is mostly neglected. Instead, the feed oscillations are often started directly after the batch phase, when the specific growth rate is close to the maximum. We show here that the cultivation mode before oscillations are started has a tremendous effect on the metabolic responses. In difference to cells, which were pre-grown under batch conditions at a high growth rate, Escherichia coli cells that were pre-grown under glucose limitation at a low growth rate accumulate short-chain fatty acids (acetate, lactate, succinate) and glycolysis-related amino acids to a higher extent in a two-compartment scale-down bioreactor. Thus, cells which enter oscillations from a lower specific growth rate seem to react more sensitive to oscillations than cells that are subjected to oscillations directly after a batch phase. These results are interesting in designing reliable scale-down systems, which better reflect large-scale bioprocesses.
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29
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Investigation of vertical mixing in thin-layer cascade reactors using computational fluid dynamics. Chem Eng Res Des 2018. [DOI: 10.1016/j.cherd.2018.01.036] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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30
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Herrerías-Azcué F, Pérez-Muñuzuri V, Galla T. Stirring does not make populations well mixed. Sci Rep 2018; 8:4068. [PMID: 29511246 PMCID: PMC5840425 DOI: 10.1038/s41598-018-22062-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 02/09/2018] [Indexed: 12/02/2022] Open
Abstract
In evolutionary dynamics, the notion of a ‘well-mixed’ population is usually associated with all-to-all interactions at all times. This assumption simplifies the mathematics of evolutionary processes, and makes analytical solutions possible. At the same time the term ‘well-mixed’ suggests that this situation can be achieved by physically stirring the population. Using simulations of populations in chaotic flows, we show that in most cases this is not true: conventional well-mixed theories do not predict fixation probabilities correctly, regardless of how fast or thorough the stirring is. We propose a new analytical description in the fast-flow limit. This approach is valid for processes with global and local selection, and accurately predicts the suppression of selection as competition becomes more local. It provides a modelling tool for biological or social systems with individuals in motion.
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Affiliation(s)
- Francisco Herrerías-Azcué
- Theoretical Physics, School of Physics and Astronomy, The University of Manchester, M13 9PL, Manchester, United Kingdom.
| | - Vicente Pérez-Muñuzuri
- Group of Nonlinear Physics, Faculty of Physics, University of Santiago de Compostela, E-15782, Santiago de Compostela, Spain.
| | - Tobias Galla
- Theoretical Physics, School of Physics and Astronomy, The University of Manchester, M13 9PL, Manchester, United Kingdom.
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31
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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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32
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33
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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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34
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Binder D, Drepper T, Jaeger KE, Delvigne F, Wiechert W, Kohlheyer D, Grünberger A. Homogenizing bacterial cell factories: Analysis and engineering of phenotypic heterogeneity. Metab Eng 2017. [DOI: 10.1016/j.ymben.2017.06.009] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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35
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Lemoine A, Delvigne F, Bockisch A, Neubauer P, Junne S. Tools for the determination of population heterogeneity caused by inhomogeneous cultivation conditions. J Biotechnol 2017; 251:84-93. [DOI: 10.1016/j.jbiotec.2017.03.020] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 03/17/2017] [Accepted: 03/21/2017] [Indexed: 01/01/2023]
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36
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Delvigne F, Noorman H. Scale-up/Scale-down of microbial bioprocesses: a modern light on an old issue. Microb Biotechnol 2017; 10:685-687. [PMID: 28556613 PMCID: PMC5481528 DOI: 10.1111/1751-7915.12732] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 04/26/2017] [Indexed: 12/22/2022] Open
Abstract
The bio‐economy is in transit from innovation to commercialization. The bioprocess industry is expected to increasingly deliver bio‐products to the market, in large amounts, at high quality and at competitive cost levels. This requires flawless start‐up of new large‐scale bioprocesses and continuous improvement of running processes. Fermentation scale‐up and operation can benefit from recent advances in three areas: 1. computation‐driven design of scale‐down simulators, 2. omics‐driven metabolic engineering and 3. sensing and understanding of population heterogeneity. Integration of these fields requires a unified computational approach, linked to big data and simulated reality frameworks, of which the contours are becoming visible today.
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Affiliation(s)
- Frank Delvigne
- TERRA Research Center, Microbial Processes and Interactions (MiPI), University of Liège, Liège, Belgium
| | - Henk Noorman
- DSM Biotechnology Center, Delft, The Netherlands.,Department of Biotechnology, Technical University Delft, Delft, The Netherlands
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37
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Tang W, Deshmukh AT, Haringa C, Wang G, van Gulik W, van Winden W, Reuss M, Heijnen JJ, Xia J, Chu J, Noorman HJ. A 9-pool metabolic structured kinetic model describing days to seconds dynamics of growth and product formation byPenicillium chrysogenum. Biotechnol Bioeng 2017; 114:1733-1743. [DOI: 10.1002/bit.26294] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 02/26/2017] [Accepted: 03/14/2017] [Indexed: 12/30/2022]
Affiliation(s)
- Wenjun Tang
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
| | | | - Cees Haringa
- Cell Systems Engineering; Department of Biotechnology; Delft University of Technology; Delft The Netherlands
| | - Guan Wang
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
| | - Walter van Gulik
- Cell Systems Engineering; Department of Biotechnology; Delft University of Technology; Delft The Netherlands
| | | | - Matthias Reuss
- Institute of Biochemical Engineering; University of Stuttgart; Stuttgart Germany
| | - Joseph J. Heijnen
- Cell Systems Engineering; Department of Biotechnology; Delft University of Technology; Delft The Netherlands
| | - Jianye Xia
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering; East China University of Science and Technology; P.O. Box 329#, No.130, Meilong Road Shanghai P.R. China
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38
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Simen JD, Löffler M, Jäger G, Schäferhoff K, Freund A, Matthes J, Müller J, Takors R. Transcriptional response of Escherichia coli to ammonia and glucose fluctuations. Microb Biotechnol 2017; 10:858-872. [PMID: 28447391 PMCID: PMC5481515 DOI: 10.1111/1751-7915.12713] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2016] [Revised: 03/11/2017] [Accepted: 03/15/2017] [Indexed: 01/22/2023] Open
Abstract
In large‐scale production processes, metabolic control is typically achieved by limited supply of essential nutrients such as glucose or ammonia. With increasing bioreactor dimensions, microbial producers such as Escherichia coli are exposed to changing substrate availabilities due to limited mixing. In turn, cells sense and respond to these dynamic conditions leading to frequent activation of their regulatory programmes. Previously, we characterized short‐ and long‐term strategies of cells to adapt to glucose fluctuations. Here, we focused on fluctuating ammonia supply while studying a continuously running two‐compartment bioreactor system comprising a stirred tank reactor (STR) and a plug‐flow reactor (PFR). The alarmone ppGpp rapidly accumulated in PFR, initiating considerable transcriptional responses after 70 s. About 400 genes were repeatedly switched on/off when E. coli returned to the STR. E. coli revealed highly diverging long‐term transcriptional responses in ammonia compared to glucose fluctuations. In contrast, the induction of stringent regulation was a common feature of both short‐term responses. Cellular ATP demands for coping with fluctuating ammonia supply were found to increase maintenance by 15%. The identification of genes contributing to the increased ATP demand together with the elucidation of regulatory mechanisms may help to create robust cells and processes for large‐scale application.
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Affiliation(s)
- Joana Danica Simen
- Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Michael Löffler
- Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Günter Jäger
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Calwerstr. 7, 72076, Tübingen, Germany
| | - Karin Schäferhoff
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Calwerstr. 7, 72076, Tübingen, Germany
| | - Andreas Freund
- Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Jakob Matthes
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Calwerstr. 7, 72076, Tübingen, Germany
| | - Jan Müller
- Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
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39
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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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40
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Affiliation(s)
- Rob Mudde
- Universität Stuttgart; Research Center Systems Biology; Nobelstr. 15 70569 Stuttgart Germany
| | - Henk Noorman
- Universität Stuttgart; Research Center Systems Biology; Nobelstr. 15 70569 Stuttgart Germany
| | - Matthias Reuss
- Universität Stuttgart; Research Center Systems Biology; Nobelstr. 15 70569 Stuttgart Germany
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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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Löffler M, Simen JD, Jäger G, Schäferhoff K, Freund A, Takors R. Engineering E. coli for large-scale production - Strategies considering ATP expenses and transcriptional responses. Metab Eng 2016; 38:73-85. [PMID: 27378496 DOI: 10.1016/j.ymben.2016.06.008] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2016] [Revised: 06/20/2016] [Accepted: 06/30/2016] [Indexed: 01/18/2023]
Abstract
Microbial producers such as Escherichia coli are evolutionarily trained to adapt to changing substrate availabilities. Being exposed to large-scale production conditions, their complex, multilayered regulatory programs are frequently activated because they face changing substrate supply due to limited mixing. Here, we show that E. coli can adopt both short- and long-term strategies to withstand these stress conditions. Experiments in which glucose availability was changed over a short time scale were performed in a two-compartment bioreactor system. Quick metabolic responses were observed during the first 30s of glucose shortage, and after 70s, fundamental transcriptional programs were initiated. Since cells are fluctuating under simulated large-scale conditions, this scenario represents a continuous on/off switching of about 600 genes. Furthermore, the resulting ATP maintenance demands were increased by about 40-50%, allowing us to conclude that hyper-producing strains could become ATP-limited under large-scale production conditions. Based on the observed transcriptional patterns, we identified a number of candidate gene deletions that may reduce unwanted ATP losses. In summary, we present a theoretical framework that provides biological targets that could be used to engineer novel E. coli strains such that large-scale performance equals laboratory-scale expectations.
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Affiliation(s)
- Michael Löffler
- University of Stuttgart, Institute of Biochemical Engineering, Allmandring 31, 70569 Stuttgart, Germany
| | - Joana Danica Simen
- University of Stuttgart, Institute of Biochemical Engineering, Allmandring 31, 70569 Stuttgart, Germany
| | - Günter Jäger
- University of Tübingen, Institute of Medical Genetics and Applied Genomics, Calwerstr. 7, 72076 Tübingen, Germany
| | - Karin Schäferhoff
- University of Tübingen, Institute of Medical Genetics and Applied Genomics, Calwerstr. 7, 72076 Tübingen, Germany
| | - Andreas Freund
- University of Stuttgart, Institute of Biochemical Engineering, Allmandring 31, 70569 Stuttgart, Germany
| | - Ralf Takors
- University of Stuttgart, Institute of Biochemical Engineering, Allmandring 31, 70569 Stuttgart, Germany.
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Wutz J, Lapin A, Siebler F, Schäfer JE, Wucherpfennig T, Berger M, Takors R. Predictability ofkLain stirred tank reactors under multiple operating conditions using an Euler-Lagrange approach. Eng Life Sci 2016. [DOI: 10.1002/elsc.201500135] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Johannes Wutz
- Institute of Biochemical Engineering; University of Stuttgart; Stuttgart Germany
| | - Alexey Lapin
- Institute of Biochemical Engineering; University of Stuttgart; Stuttgart Germany
| | - Flora Siebler
- Institute of Biochemical Engineering; University of Stuttgart; Stuttgart Germany
| | - Jan Erik Schäfer
- BP Process Development Germany; Boehringer Ingelheim Pharma GmbH & Co. KG; Biberach/Riß Germany
| | - Thomas Wucherpfennig
- BP Process Development Germany; Boehringer Ingelheim Pharma GmbH & Co. KG; Biberach/Riß Germany
| | - Martina Berger
- BP Process Development Germany; Boehringer Ingelheim Pharma GmbH & Co. KG; Biberach/Riß Germany
| | - Ralf Takors
- Institute of Biochemical Engineering; University of Stuttgart; Stuttgart Germany
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45
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McClure DD, Kavanagh JM, Fletcher DF, Barton GW. Characterizing bubble column bioreactor performance using computational fluid dynamics. Chem Eng Sci 2016. [DOI: 10.1016/j.ces.2016.01.016] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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46
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Azargoshasb H, Mousavi SM, Jamialahmadi O, Shojaosadati SA, Mousavi SB. Experiments and a three-phase computational fluid dynamics (CFD) simulation coupled with population balance equations of a stirred tank bioreactor for high cell density cultivation. CAN J CHEM ENG 2015. [DOI: 10.1002/cjce.22352] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Hamidreza Azargoshasb
- Biotechnology Group, Chemical Engineering Department; Tarbiat Modares University; Tehran Iran
| | - Seyyed Mohammad Mousavi
- Biotechnology Group, Chemical Engineering Department; Tarbiat Modares University; Tehran Iran
| | - Oveis Jamialahmadi
- Biotechnology Group, Chemical Engineering Department; Tarbiat Modares University; Tehran Iran
| | | | - Seyyed Babak Mousavi
- Biotechnology Group, Chemical Engineering Department; Tarbiat Modares University; Tehran Iran
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47
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Investigating the interactions between physical and biological heterogeneities in bioreactors using compartment, population balance and metabolic models. Chem Eng Sci 2015. [DOI: 10.1016/j.ces.2014.11.035] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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48
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Kaul H, Ventikos Y. On the genealogy of tissue engineering and regenerative medicine. TISSUE ENGINEERING. PART B, REVIEWS 2015; 21:203-17. [PMID: 25343302 PMCID: PMC4390213 DOI: 10.1089/ten.teb.2014.0285] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
In this article, we identify and discuss a timeline of historical events and scientific breakthroughs that shaped the principles of tissue engineering and regenerative medicine (TERM). We explore the origins of TERM concepts in myths, their application in the ancient era, their resurgence during Enlightenment, and, finally, their systematic codification into an emerging scientific and technological framework in recent past. The development of computational/mathematical approaches in TERM is also briefly discussed.
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Affiliation(s)
- Himanshu Kaul
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Yiannis Ventikos
- Department of Mechanical Engineering, University College London, London, United Kingdom
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Lemoine A, Maya Martίnez-Iturralde N, Spann R, Neubauer P, Junne S. Response ofCorynebacterium glutamicumexposed to oscillating cultivation conditions in a two- and a novel three-compartment scale-down bioreactor. Biotechnol Bioeng 2015; 112:1220-31. [DOI: 10.1002/bit.25543] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Revised: 01/02/2015] [Accepted: 01/08/2015] [Indexed: 11/10/2022]
Affiliation(s)
- Anja Lemoine
- Chair of Bioprocess Engineering; Department of Biotechnology; Technische Universität Berlin; Berlin Germany
| | | | - Robert Spann
- Chair of Bioprocess Engineering; Department of Biotechnology; Technische Universität Berlin; Berlin Germany
| | - Peter Neubauer
- Chair of Bioprocess Engineering; Department of Biotechnology; Technische Universität Berlin; Berlin Germany
| | - Stefan Junne
- Chair of Bioprocess Engineering; Department of Biotechnology; Technische Universität Berlin; Berlin Germany
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Xia J, Wang G, Lin J, Wang Y, Chu J, Zhuang Y, Zhang S. Advances and Practices of Bioprocess Scale-up. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2015; 152:137-51. [PMID: 25636486 DOI: 10.1007/10_2014_293] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
: This chapter addresses the update progress in bioprocess engineering. In addition to an overview of the theory of multi-scale analysis for fermentation process, examples of scale-up practice combining microbial physiological parameters with bioreactor fluid dynamics are also described. Furthermore, the methodology for process optimization and bioreactor scale-up by integrating fluid dynamics with biokinetics is highlighted. In addition to a short review of the heterogeneous environment in large-scale bioreactor and its effect, a scale-down strategy for investigating this issue is addressed. Mathematical models and simulation methodology for integrating flow field in the reactor and microbial kinetics response are described. Finally, a comprehensive discussion on the advantages and challenges of the model-driven scale-up method is given at the end of this chapter.
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Affiliation(s)
- Jianye Xia
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Guan Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Jihan Lin
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Yonghong Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Siliang Zhang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China.
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