1
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Tentori EF, Fang S, Richardson RE. RNA Biomarker Trends across Type I and Type II Aerobic Methanotrophs in Response to Methane Oxidation Rates and Transcriptome Response to Short-Term Methane and Oxygen Limitation in Methylomicrobium album BG8. Microbiol Spectr 2022; 10:e0000322. [PMID: 35678574 PMCID: PMC9241951 DOI: 10.1128/spectrum.00003-22] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 05/21/2022] [Indexed: 11/26/2022] Open
Abstract
Methanotrophs, which help regulate atmospheric levels of methane, are active in diverse natural and man-made environments. This range of habitats and the feast-famine cycles seen by many environmental methanotrophs suggest that methanotrophs dynamically mediate rates of methane oxidation. Global methane budgets require ways to account for this variability in time and space. Functional gene biomarker transcripts are increasingly studied to inform the dynamics of diverse biogeochemical cycles. Previously, per-cell transcript levels of the methane oxidation biomarker pmoA were found to vary quantitatively with respect to methane oxidation rates in the model aerobic methanotroph Methylosinus trichosporium OB3b. In the present study, these trends were explored for two additional aerobic methanotroph pure cultures grown in membrane bioreactors, Methylocystis parvus OBBP and Methylomicrobium album BG8. At steady-state conditions, per-cell pmoA mRNA transcript levels strongly correlated with per-cell methane oxidation across the three methanotrophs across many orders of magnitude of activity (R2 = 0.91). The inclusion of both type I and type II aerobic methanotrophs suggests a universal trend between in situ activity level and pmoA RNA biomarker levels which can aid in improving estimates of both subsurface and atmospheric methane. Additionally, genome-wide expression data (obtained by transcriptome sequencing [RNA-seq]) were used to explore transcriptomic responses of steady-state M. album BG8 cultures to short-term CH4 and O2 limitation. These limitations induced regulation of genes involved in central carbon metabolism (including carbon storage), cell motility, and stress response. IMPORTANCE Methanotrophs are naturally occurring microorganisms capable of oxidizing methane, having an impact on global net methane emissions. Additionally, they have also gained interest for their biotechnological applications in single-cell protein production, biofuels, and bioplastics. Having better ways of measuring methanotroph activity and understanding how methanotrophs respond to changing conditions is imperative for both optimization in controlled-growth applications and understanding in situ methane oxidation rates. In this study, we explored the applicability of methane oxidation biomarkers as a universal indicator of methanotrophic activity and explored methanotroph transcriptomic response to short-term changes in substrate availability. Our results contribute to better understanding the activity of aerobic methanotrophs, their core metabolic pathways, and their stress responses.
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Affiliation(s)
- Egidio F. Tentori
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York, USA
| | - Shania Fang
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York, USA
| | - Ruth E. Richardson
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York, USA
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2
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Minden S, Aniolek M, Sarkizi Shams Hajian C, Teleki A, Zerrer T, Delvigne F, van Gulik W, Deshmukh A, Noorman H, Takors R. Monitoring Intracellular Metabolite Dynamics in Saccharomyces cerevisiae during Industrially Relevant Famine Stimuli. Metabolites 2022; 12:metabo12030263. [PMID: 35323706 PMCID: PMC8953226 DOI: 10.3390/metabo12030263] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/08/2022] [Accepted: 03/16/2022] [Indexed: 11/16/2022] Open
Abstract
Carbon limitation is a common feeding strategy in bioprocesses to enable an efficient microbiological conversion of a substrate to a product. However, industrial settings inherently promote mixing insufficiencies, creating zones of famine conditions. Cells frequently traveling through such regions repeatedly experience substrate shortages and respond individually but often with a deteriorated production performance. A priori knowledge of the expected strain performance would enable targeted strain, process, and bioreactor engineering for minimizing performance loss. Today, computational fluid dynamics (CFD) coupled to data-driven kinetic models are a promising route for the in silico investigation of the impact of the dynamic environment in the large-scale bioreactor on microbial performance. However, profound wet-lab datasets are needed to cover relevant perturbations on realistic time scales. As a pioneering study, we quantified intracellular metabolome dynamics of Saccharomyces cerevisiae following an industrially relevant famine perturbation. Stimulus-response experiments were operated as chemostats with an intermittent feed and high-frequency sampling. Our results reveal that even mild glucose gradients in the range of 100 µmol·L−1 impose significant perturbations in adapted and non-adapted yeast cells, altering energy and redox homeostasis. Apparently, yeast sacrifices catabolic reduction charges for the sake of anabolic persistence under acute carbon starvation conditions. After repeated exposure to famine conditions, adapted cells show 2.7% increased maintenance demands.
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Affiliation(s)
- Steven Minden
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Maria Aniolek
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Christopher Sarkizi Shams Hajian
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Attila Teleki
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Tobias Zerrer
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
| | - Frank Delvigne
- Microbial Processes and Interactions (MiPI), TERRA Research and Teaching Centre, Gembloux Agro Bio Tech, University of Liege, 5030 Gembloux, Belgium;
| | - Walter van Gulik
- Department of Biotechnology, Delft University of Technology, van der Maasweg 6, 2629 HZ Delft, The Netherlands;
| | - Amit Deshmukh
- Royal DSM, 2613 AX Delft, The Netherlands; (A.D.); (H.N.)
| | - Henk Noorman
- Royal DSM, 2613 AX Delft, The Netherlands; (A.D.); (H.N.)
- Department of Biotechnology, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (S.M.); (M.A.); (C.S.S.H.); (A.T.); (T.Z.)
- Correspondence:
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3
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Ziegler M, Zieringer J, Döring CL, Paul L, Schaal C, Takors R. Engineering of a robust Escherichia coli chassis and exploitation for large-scale production processes. Metab Eng 2021; 67:75-87. [PMID: 34098100 DOI: 10.1016/j.ymben.2021.05.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 05/30/2021] [Accepted: 05/31/2021] [Indexed: 11/28/2022]
Abstract
In large-scale bioprocesses microbes are exposed to heterogeneous substrate availability reducing the overall process performance. A series of deletion strains was constructed from E. coli MG1655 aiming for a robust phenotype in heterogeneous fermentations with transient starvation. Deletion targets were hand-picked based on a list of genes derived from previous large-scale simulation runs. Each gene deletion was conducted on the premise of strict neutrality towards growth parameters in glucose minimal medium. The final strain of the series, named E. coli RM214, was cultivated continuously in an STR-PFR (stirred tank reactor - plug flow reactor) scale-down reactor. The scale-down reactor system simulated repeated passages through a glucose starvation zone. When exposed to nutrient gradients, E. coli RM214 had a significantly lower maintenance coefficient than E. coli MG1655 (Δms = 0.038 gGlucose/gCDW/h, p < 0.05). In an exemplary protein production scenario E. coli RM214 remained significantly more productive than E. coli MG1655 reaching 44% higher eGFP yield after 28 h of STR-PFR cultivation. This study developed E. coli RM214 as a robust chassis strain and demonstrated the feasibility of engineering microbial hosts for large-scale applications.
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Affiliation(s)
- Martin Ziegler
- University of Stuttgart - Institute of Biochemical Engineering, Allmandring 31, 70569, Stuttgart, Germany.
| | - Julia Zieringer
- University of Stuttgart - Institute of Biochemical Engineering, Allmandring 31, 70569, Stuttgart, Germany.
| | - Clarissa-Laura Döring
- University of Stuttgart - Institute of Biochemical Engineering, Allmandring 31, 70569, Stuttgart, Germany.
| | - Liv Paul
- University of Stuttgart - Institute of Biochemical Engineering, Allmandring 31, 70569, Stuttgart, Germany.
| | - Christoph Schaal
- 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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4
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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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5
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Predicting By-Product Gradients of Baker’s Yeast Production at Industrial Scale: A Practical Simulation Approach. Processes (Basel) 2020. [DOI: 10.3390/pr8121554] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.
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6
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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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7
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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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8
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Verhagen KJA, van Gulik WM, Wahl SA. Dynamics in redox metabolism, from stoichiometry towards kinetics. Curr Opin Biotechnol 2020; 64:116-123. [DOI: 10.1016/j.copbio.2020.01.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/05/2020] [Accepted: 01/06/2020] [Indexed: 12/12/2022]
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9
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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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10
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Gorochowski TE, Hauert S, Kreft JU, Marucci L, Stillman NR, Tang TYD, Bandiera L, Bartoli V, Dixon DOR, Fedorec AJH, Fellermann H, Fletcher AG, Foster T, Giuggioli L, Matyjaszkiewicz A, McCormick S, Montes Olivas S, Naylor J, Rubio Denniss A, Ward D. Toward Engineering Biosystems With Emergent Collective Functions. Front Bioeng Biotechnol 2020; 8:705. [PMID: 32671054 PMCID: PMC7332988 DOI: 10.3389/fbioe.2020.00705] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 06/05/2020] [Indexed: 12/31/2022] Open
Abstract
Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.
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Affiliation(s)
| | - Sabine Hauert
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Jan-Ulrich Kreft
- School of Biosciences and Institute of Microbiology and Infection and Centre for Computational Biology, University of Birmingham, Birmingham, United Kingdom
| | - Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Namid R. Stillman
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - T.-Y. Dora Tang
- Max Plank Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- Physics of Life, Cluster of Excellence, Technische Universität Dresden, Dresden, Germany
| | - Lucia Bandiera
- School of Engineering, University of Edinburgh, Edinburgh, United Kingdom
| | - Vittorio Bartoli
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | | | - Alex J. H. Fedorec
- Division of Biosciences, University College London, London, United Kingdom
| | - Harold Fellermann
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Alexander G. Fletcher
- Bateson Centre and School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Tim Foster
- School of Biosciences and Institute of Microbiology and Infection and Centre for Computational Biology, University of Birmingham, Birmingham, United Kingdom
| | - Luca Giuggioli
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | | | - Scott McCormick
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Sandra Montes Olivas
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Jonathan Naylor
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Ana Rubio Denniss
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
| | - Daniel Ward
- School of Biological Sciences, University of Bristol, Bristol, United Kingdom
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11
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Paul K, Herwig C. Scale-down simulators for mammalian cell culture as tools to access the impact of inhomogeneities occurring in large-scale bioreactors. Eng Life Sci 2020; 20:197-204. [PMID: 32874183 PMCID: PMC7447876 DOI: 10.1002/elsc.201900162] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 12/23/2019] [Accepted: 12/27/2019] [Indexed: 12/19/2022] Open
Abstract
During the scale-up of a bioprocess, not all characteristics of the process can be kept constant throughout the different scales. This typically results in increased mixing times with increasing reactor volumes. The poor mixing leads in turn to the formation of concentration gradients throughout the reactor and exposes cells to varying external conditions based on their location in the bioreactor. This can affect process performance and complicate process scale-up. Scale-down simulators, which aim at replicating the large-scale environment, expose the cells to changing environmental conditions. This has the potential to reveal adaptation mechanisms, which cells are using to adjust to rapidly fluctuating environmental conditions and can identify possible root causes for difficulties maintaining similar process performance at different scales. This understanding is of utmost importance in process validation. Additionally, these simulators also have the potential to be used for selecting cells, which are most robust when encountering changing extracellular conditions. The aim of this review is to summarize recent work in this interesting and promising area with the focus on mammalian bioprocesses, since microbial processes have been extensively reviewed.
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Affiliation(s)
- Katrin Paul
- Institute of Chemical, Environmental and Bioscience EngineeringViennaAustria
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved BioprocessesViennaAustria
| | - Christoph Herwig
- Institute of Chemical, Environmental and Bioscience EngineeringViennaAustria
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved BioprocessesViennaAustria
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12
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Maluta F, Pigou M, Montante G, Morchain J. Modeling the effects of substrate fluctuations on the maintenance rate in bioreactors with a probabilistic approach. Biochem Eng J 2020. [DOI: 10.1016/j.bej.2020.107536] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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13
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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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14
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Grand Research Challenges for Sustainable Industrial Biotechnology. Trends Biotechnol 2019; 37:1042-1050. [DOI: 10.1016/j.tibtech.2019.04.002] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 04/02/2019] [Accepted: 04/03/2019] [Indexed: 01/23/2023]
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15
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Wang G, Zhao J, Wang X, Wang T, Zhuang Y, Chu J, Zhang S, Noorman HJ. Quantitative metabolomics and metabolic flux analysis reveal impact of altered trehalose metabolism on metabolic phenotypes of Penicillium chrysogenum in aerobic glucose-limited chemostats. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.03.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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16
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Wehrs M, Tanjore D, Eng T, Lievense J, Pray TR, Mukhopadhyay A. Engineering Robust Production Microbes for Large-Scale Cultivation. Trends Microbiol 2019; 27:524-537. [PMID: 30819548 DOI: 10.1016/j.tim.2019.01.006] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/11/2019] [Accepted: 01/23/2019] [Indexed: 11/27/2022]
Abstract
Systems biology and synthetic biology are increasingly used to examine and modulate complex biological systems. As such, many issues arising during scaling-up microbial production processes can be addressed using these approaches. We review differences between laboratory-scale cultures and larger-scale processes to provide a perspective on those strain characteristics that are especially important during scaling. Systems biology has been used to examine a range of microbial systems for their response in bioreactors to fluctuations in nutrients, dissolved gases, and other stresses. Synthetic biology has been used both to assess and modulate strain response, and to engineer strains to improve production. We discuss these approaches and tools in the context of their use in engineering robust microbes for applications in large-scale production.
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Affiliation(s)
- Maren Wehrs
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Institut für Genetik, Technische Universität Braunschweig, Braunschweig, Germany; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA
| | - Deepti Tanjore
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Thomas Eng
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA
| | | | - Todd R Pray
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Aindrila Mukhopadhyay
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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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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18
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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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19
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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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Delvigne F, Takors R, Mudde R, van Gulik W, Noorman H. Bioprocess scale-up/down as integrative enabling technology: from fluid mechanics to systems biology and beyond. Microb Biotechnol 2017; 10:1267-1274. [PMID: 28805306 PMCID: PMC5609235 DOI: 10.1111/1751-7915.12803] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 07/12/2017] [Indexed: 11/28/2022] Open
Abstract
Efficient optimization of microbial processes is a critical issue for achieving a number of sustainable development goals, considering the impact of microbial biotechnology in agrofood, environment, biopharmaceutical and chemical industries. Many of these applications require scale-up after proof of concept. However, the behaviour of microbial systems remains unpredictable (at least partially) when shifting from laboratory-scale to industrial conditions. The need for robust microbial systems is thus highly needed in this context, as well as a better understanding of the interactions between fluid mechanics and cell physiology. For that purpose, a full scale-up/down computational framework is already available. This framework links computational fluid dynamics (CFD), metabolic flux analysis and agent-based modelling (ABM) for a better understanding of the cell lifelines in a heterogeneous environment. Ultimately, this framework can be used for the design of scale-down simulators and/or metabolically engineered cells able to cope with environmental fluctuations typically found in large-scale bioreactors. However, this framework still needs some refinements, such as a better integration of gas-liquid flows in CFD, and taking into account intrinsic biological noise in ABM.
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Affiliation(s)
- Frank Delvigne
- TERRA Research CenterMicrobial Processes and Interactions (MiPI)University of LiègeLiègeBelgium
| | - Ralf Takors
- Institute of Biochemical EngineeringUniversity of StuttgartStuttgartGermany
| | - Rob Mudde
- Transport Phenomena SectionDepartment of Chemical EngineeringDelft University of TechnologyDelftThe Netherlands
| | - Walter van Gulik
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
| | - Henk Noorman
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
- DSM Biotechnology CenterDelftThe Netherlands
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