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Canova CT, Inguva PK, Braatz RD. Mechanistic modeling of viral particle production. Biotechnol Bioeng 2023; 120:629-641. [PMID: 36461898 DOI: 10.1002/bit.28296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/05/2022]
Abstract
Viral systems such as wild-type viruses, viral vectors, and virus-like particles are essential components of modern biotechnology and medicine. Despite their importance, the commercial-scale production of viral systems remains highly inefficient for multiple reasons. Computational strategies are a promising avenue for improving process development, optimization, and control, but require a mathematical description of the system. This article reviews mechanistic modeling strategies for the production of viral particles, both at the cellular and bioreactor scales. In many cases, techniques and models from adjacent fields such as epidemiology and wild-type viral infection kinetics can be adapted to construct a suitable process model. These process models can then be employed for various purposes such as in-silico testing of novel process operating strategies and/or advanced process control.
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Affiliation(s)
- Christopher T Canova
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Pavan K Inguva
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Richard D Braatz
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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2
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Ramos JRC, Bissinger T, Genzel Y, Reichl U. Impact of Influenza A Virus Infection on Growth and Metabolism of Suspension MDCK Cells Using a Dynamic Model. Metabolites 2022; 12:metabo12030239. [PMID: 35323683 PMCID: PMC8950586 DOI: 10.3390/metabo12030239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/04/2022] [Accepted: 03/09/2022] [Indexed: 11/21/2022] Open
Abstract
Cell cultured-based influenza virus production is a viable option for vaccine manufacturing. In order to achieve a high concentration of viable cells, is requirement to have not only optimal process conditions, but also an active metabolism capable of intracellular synthesis of viral components. Experimental metabolic data collected in such processes are complex and difficult to interpret, for which mathematical models are an appropriate way to simulate and analyze the complex and dynamic interaction between the virus and its host cell. A dynamic model with 35 states was developed in this study to describe growth, metabolism, and influenza A virus production in shake flask cultivations of suspension Madin-Darby Canine Kidney (MDCK) cells. It considers cell growth (concentration of viable cells, mean cell diameters, volume of viable cells), concentrations of key metabolites both at the intracellular and extracellular level and virus titers. Using one set of parameters, the model accurately simulates the dynamics of mock-infected cells and correctly predicts the overall dynamics of virus-infected cells for up to 60 h post infection (hpi). The model clearly suggests that most changes observed after infection are related to cessation of cell growth and the subsequent transition to apoptosis and cell death. However, predictions do not cover late phases of infection, particularly for the extracellular concentrations of glutamate and ammonium after about 12 hpi. Results obtained from additional in silico studies performed indicated that amino acid degradation by extracellular enzymes resulting from cell lysis during late infection stages may contribute to this observed discrepancy.
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Affiliation(s)
- João Rodrigues Correia Ramos
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
- Correspondence:
| | - Thomas Bissinger
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
| | - Yvonne Genzel
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
| | - Udo Reichl
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
- Institute of Process Engineering, Faculty of Process & Systems Engineering, Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany
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Küchler J, Püttker S, Lahmann P, Genzel Y, Kupke S, Benndorf D, Reichl U. Absolute quantification of viral proteins during single-round replication of MDCK suspension cells. J Proteomics 2022; 259:104544. [PMID: 35240312 DOI: 10.1016/j.jprot.2022.104544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/15/2022] [Accepted: 02/20/2022] [Indexed: 11/17/2022]
Abstract
Madin-Darby canine kidney (MDCK) cells are widely used in basic research and for the propagation of influenza A viruses (IAV) for vaccine production. To identify targets for antiviral therapies and to optimize vaccine manufacturing, a detailed understanding of the viral life cycle is important. This includes the characterization of virus entry, the synthesis of the various viral RNAs and proteins, the transfer of viral compounds in the cell and virus budding. In case quantitative information is available, the analysis can be complemented by mathematical modelling approaches. While comprehensive studies focusing on IAV entry as well as viral mRNA, vRNA and cRNA accumulation in the nucleus of cells have been performed, quantitative data regarding IAV protein synthesis and accumulation was mostly lacking. In this study, we present a mass spectrometry (MS)-based method to evaluate whether an absolute quantification of viral proteins is possible for single-round replication in suspension MDCK cells. Using influenza A/PR/8/34 (H1N1, RKI) as a model strain at a multiplicity of infection of ten, defined amounts of isotopically labelled peptides of synthetic origin of four IAV proteins (hemagglutinin, neuraminidase, nucleoprotein, matrix protein 1) were added as an internal standard before tryptic digestion of samples for absolute quantification (AQUA). The first intracellular protein detected was NP at 1 h post infection (hpi). A maximum extracellular concentration of 7.7E+12 copies/mL was achieved. This was followed by hemagglutinin (3 hpi, maximum 4.1E+12 copies/mL at 13 hpi), matrix protein 1 (5 hpi, maximum 2.2E+12 copies/mL at 13 hpi) and neuraminidase (5 hpi, 6.0E+11 copies/mL at 13 hpi). In sum, for the first time absolute IAV protein copy numbers were quantified by a MS-based method for infected MDCK cells providing important insights into viral protein dynamics during single-round virus replication. SIGNIFICANCE: Influenza A virus is a significant human pathogen worldwide. To improve therapies against influenza and overcome bottlenecks in vaccine production in cell culture, it is critical to gain a detailed understanding of the viral life cycle. In addition to qPCR-based models, this study will examine the dynamics of influenza virus proteins during infection of producer cells to gain initial insights into changes in absolute copy numbers.
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Affiliation(s)
- Jan Küchler
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
| | - Sebastian Püttker
- Bioprocess Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Patrick Lahmann
- Bioprocess Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Yvonne Genzel
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Sascha Kupke
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Dirk Benndorf
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany; Bioprocess Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Udo Reichl
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany; Bioprocess Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
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McClelland RD, Culp TN, Marchant DJ. Imaging Flow Cytometry and Confocal Immunofluorescence Microscopy of Virus-Host Cell Interactions. Front Cell Infect Microbiol 2021; 11:749039. [PMID: 34712624 PMCID: PMC8546218 DOI: 10.3389/fcimb.2021.749039] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/27/2021] [Indexed: 11/25/2022] Open
Abstract
Viruses are diverse pathogens that use host factors to enter cells and cause disease. Imaging the entry and replication phases of viruses and their interactions with host factors is key to fully understanding viral infections. This review will discuss how confocal microscopy and imaging flow cytometry are used to investigate virus entry and replication mechanisms in fixed and live cells. Quantification of viral images and the use of cryo-electron microscopy to gather structural information of viruses is also explored. Using imaging to understand how viruses replicate and interact with host factors, we gain insight into cellular processes and identify novel targets to develop antiviral therapeutics and vaccines.
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Affiliation(s)
- Ryley D McClelland
- Department of Medical Microbiology and Immunology, Li Ka Shing Institute of Virology, Katz Center for Health Research, University of Alberta, Edmonton, AB, Canada
| | - Tyce N Culp
- Department of Medical Microbiology and Immunology, Li Ka Shing Institute of Virology, Katz Center for Health Research, University of Alberta, Edmonton, AB, Canada
| | - David J Marchant
- Department of Medical Microbiology and Immunology, Li Ka Shing Institute of Virology, Katz Center for Health Research, University of Alberta, Edmonton, AB, Canada
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Hooker KL, Ganusov VV. Impact of Oseltamivir Treatment on Influenza A and B Virus Dynamics in Human Volunteers. Front Microbiol 2021; 12:631211. [PMID: 33732224 PMCID: PMC7957053 DOI: 10.3389/fmicb.2021.631211] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 01/25/2021] [Indexed: 11/13/2022] Open
Abstract
Influenza viruses infect millions of humans every year causing an estimated 400,000 deaths globally. Due to continuous virus evolution current vaccines provide only limited protection against the flu. Several antiviral drugs are available to treat influenza infection, and one of the most commonly used drugs is oseltamivir (Tamiflu). While the mechanism of action of oseltamivir as a neuraminidase inhibitor is well-understood, the impact of oseltamivir on influenza virus dynamics in humans has been controversial. Many clinical trials with oseltamivir have been done by pharmaceutical companies such as Roche but the results of these trials until recently have been provided as summary reports or papers. Typically, such reports included median virus shedding curves for placebo and drug-treated influenza virus infected volunteers often indicating high efficacy of the early treatment. However, median shedding curves may be not accurately representing drug impact in individual volunteers. Importantly, due to public pressure clinical trials data testing oseltamivir efficacy has been recently released in the form of redacted PDF documents. We digitized and re-analyzed experimental data on influenza virus shedding in human volunteers from three previously published trials: on influenza A (1 trial) or B viruses (2 trials). Given that not all volunteers exposed to influenza viruses actually start virus shedding we found that impact of oseltamivir on the virus shedding dynamics was dependent on (i) selection of volunteers that were infected with the virus, and (ii) the detection limit in the measurement assay; both of these details were not well-articulated in the published studies. By assuming that any non-zero viral measurement is above the limit of detection we could match previously published data on median influenza A virus (flu A study) shedding but not on influenza B virus shedding (flu B study B) in human volunteers. Additional analyses confirmed that oseltamivir had an impact on the duration of shedding and overall shedding (defined as area under the curve) but this result varied by the trial. Interestingly, treatment had no impact on the rates at which shedding increased or declined with time in individual volunteers. Additional analyses showed that oseltamivir impacted the kinetics of the end of viral shedding, and in about 20-40% of volunteers that shed the virus treatment had no impact on viral shedding duration. Our results suggest an unusual impact of oseltamivir on influenza viruses shedding kinetics and caution about the use of published median data or data from a few individuals for inferences. Furthermore, we call for the need to publish raw data from critical clinical trials that can be independently analyzed.
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Affiliation(s)
- Kyla L Hooker
- Genome Science and Technology, University of Tennessee, Knoxville, TN, United States
| | - Vitaly V Ganusov
- Genome Science and Technology, University of Tennessee, Knoxville, TN, United States.,Department of Microbiology, University of Tennessee, Knoxville, TN, United States.,Department of Mathematics, University of Tennessee, Knoxville, TN, United States
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Eosinophil Responses at the Airway Epithelial Barrier during the Early Phase of Influenza A Virus Infection in C57BL/6 Mice. Cells 2021; 10:cells10030509. [PMID: 33673645 PMCID: PMC7997358 DOI: 10.3390/cells10030509] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 02/09/2021] [Accepted: 02/12/2021] [Indexed: 02/07/2023] Open
Abstract
Eosinophils, previously considered terminally differentiated effector cells, have multifaceted functions in tissues. We previously found that allergic mice with eosinophil-rich inflammation were protected from severe influenza and discovered specialized antiviral effector functions for eosinophils including promoting cellular immunity during influenza. In this study, we hypothesized that eosinophil responses during the early phase of influenza contribute to host protection. Using in vitro and in vivo models, we found that eosinophils were rapidly and dynamically regulated upon influenza A virus (IAV) exposure to gain migratory capabilities to traffic to lymphoid organs after pulmonary infection. Eosinophils were capable of neutralizing virus upon contact and combinations of eosinophil granule proteins reduced virus infectivity through hemagglutinin inactivation. Bi-directional crosstalk between IAV-exposed epithelial cells and eosinophils occurred after IAV infection and cross-regulation promoted barrier responses to improve antiviral defenses in airway epithelial cells. Direct interactions between eosinophils and airway epithelial cells after IAV infection prevented virus-induced cytopathology in airway epithelial cells in vitro, and eosinophil recipient IAV-infected mice also maintained normal airway epithelial cell morphology. Our data suggest that eosinophils are important in the early phase of IAV infection providing immediate protection to the epithelial barrier until adaptive immune responses are deployed during influenza.
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Weston S, Baracco L, Keller C, Matthews K, McGrath ME, Logue J, Liang J, Dyall J, Holbrook MR, Hensley LE, Jahrling PB, Yu W, MacKerell AD, Frieman MB. The SKI complex is a broad-spectrum, host-directed antiviral drug target for coronaviruses, influenza, and filoviruses. Proc Natl Acad Sci U S A 2020; 117:30687-30698. [PMID: 33184176 PMCID: PMC7720140 DOI: 10.1073/pnas.2012939117] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The SARS-CoV-2 pandemic has made it clear that we have a desperate need for antivirals. We present work that the mammalian SKI complex is a broad-spectrum, host-directed, antiviral drug target. Yeast suppressor screening was utilized to find a functional genetic interaction between proteins from influenza A virus (IAV) and Middle East respiratory syndrome coronavirus (MERS-CoV) with eukaryotic proteins that may be potential host factors involved in replication. This screening identified the SKI complex as a potential host factor for both viruses. In mammalian systems siRNA-mediated knockdown of SKI genes inhibited replication of IAV and MERS-CoV. In silico modeling and database screening identified a binding pocket on the SKI complex and compounds predicted to bind. Experimental assays of those compounds identified three chemical structures that were antiviral against IAV and MERS-CoV along with the filoviruses Ebola and Marburg and two further coronaviruses, SARS-CoV and SARS-CoV-2. The mechanism of antiviral activity is through inhibition of viral RNA production. This work defines the mammalian SKI complex as a broad-spectrum antiviral drug target and identifies lead compounds for further development.
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Affiliation(s)
- Stuart Weston
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Lauren Baracco
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Chloe Keller
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Krystal Matthews
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Marisa E McGrath
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - James Logue
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Janie Liang
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
| | - Julie Dyall
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
| | - Michael R Holbrook
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
| | - Lisa E Hensley
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
| | - Peter B Jahrling
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
- Emerging Viral Pathogens Section, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
| | - Wenbo Yu
- Center for Biomolecular Therapeutics, University of Maryland School of Medicine, Baltimore, MD 21201
- University of Maryland Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
| | - Alexander D MacKerell
- Center for Biomolecular Therapeutics, University of Maryland School of Medicine, Baltimore, MD 21201
- University of Maryland Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
| | - Matthew B Frieman
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201;
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Duvigneau S, Dürr R, Laske T, Bachmann M, Dostert M, Kienle A. Model-based approach for predicting the impact of genetic modifications on product yield in biopharmaceutical manufacturing-Application to influenza vaccine production. PLoS Comput Biol 2020; 16:e1007810. [PMID: 32598363 PMCID: PMC7323952 DOI: 10.1371/journal.pcbi.1007810] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 03/22/2020] [Indexed: 12/23/2022] Open
Abstract
A large group of biopharmaceuticals is produced in cell lines. The yield of such products can be increased by genetic engineering of the corresponding cell lines. The prediction of promising genetic modifications by mathematical modeling is a valuable tool to facilitate experimental screening. Besides information on the intracellular kinetics and genetic modifications the mathematical model has to account for ubiquitous cell-to-cell variability. In this contribution, we establish a novel model-based methodology for influenza vaccine production in cell lines with overexpressed genes. The manipulation of the expression level of genes coding for host cell factors relevant for virus replication is achieved by lentiviral transduction. Since lentiviral transduction causes increased cell-to-cell variability due to different copy numbers and integration sites of the gene constructs we use a population balance modeling approach to account for this heterogeneity in terms of intracellular viral components and distributed kinetic parameters. The latter are estimated from experimental data of intracellular viral RNA levels and virus titers of infection experiments using cells overexpressing a single host cell gene. For experiments with cells overexpressing multiple host cell genes, only final virus titers were measured and thus, no direct estimation of the parameter distributions was possible. Instead, we evaluate four different computational strategies to infer these from single gene parameter sets. Finally, the best computational strategy is used to predict the most promising candidates for future modifications that show the highest potential for an increased virus yield in a combinatorial study. As expected, there is a trend to higher yields the more modifications are included. In the present work, we use a sophisticated simulation-based methodology to account for the impact of genetic modifications in producer cell lines on the yield of biomanufacturing processes. Furthermore, our approach opens the possibility to predict the most promising genetic modifications instead of identifying them in costly and time-consuming screening experiments. As an example, we apply our methodology to cell culture-based influenza vaccine production, a process that is of tremendous importance for the maintenance of public health. Here, we consider cell lines in which genes coding for one or more cellular factors are up-regulated by genetic engineering to increase the virus yield. However, the gene editing procedure increases the heterogeneity in the producer cell population because genetic modifications do not occur equally in each cell. This cell-to-cell variability is taken into account in a population balance modeling framework, thus providing a more accurate prediction of the virus yield in the heterogeneous population. Finally, we use our approach and a concise experimental data set from cell lines with one gene modification to predict the virus yield of cell lines with multiple genetic modifications. Thereby, we facilitate the experimental screening of potential candidates. We suggest that this methodology is transferable to a wide range of biomanufacturing processes and constitutes a valuable contribution to experimental design.
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Affiliation(s)
- Stefanie Duvigneau
- Institute for Automation Engineering, Otto von Guericke University, Magdeburg, Saxony-Anhalt, Germany
| | - Robert Dürr
- Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Saxony-Anhalt, Germany
- * E-mail:
| | - Tanja Laske
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Saxony-Anhalt, Germany
| | - Mandy Bachmann
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Saxony-Anhalt, Germany
| | - Melanie Dostert
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Saxony-Anhalt, Germany
| | - Achim Kienle
- Institute for Automation Engineering, Otto von Guericke University, Magdeburg, Saxony-Anhalt, Germany
- Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Saxony-Anhalt, Germany
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