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Peña Caballero V, López-Pérez PA, Oatna Georgina GS, Morales-Vargas AT. Experimental validation off-line a nonlinear controller for removal of chromium using non-living cells of Yarrowia lipolytica. Prep Biochem Biotechnol 2024:1-10. [PMID: 38533682 DOI: 10.1080/10826068.2024.2329277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
The removal of hexavalent chromium [Cr (VI)] using non-living cells of Yarrowia lipolytica was investigated. Batch and continuous studies on removal of Cr (VI) achieved 97% and 99% removal from aqueous phase, respectively. The specific uptake values at pH of 2 in batch process were 40.73 ± 1.3 mg/g and 30.09 ± 0.23 mg/g on non-living cells, when 100 and 200 mg/L of metal Cr (VI) concentrations were used. In order to investigate the regulation of Cr (VI) under continuous operation based on reaction volume numerically a new class of feedback controller from structure polynomial was designed. The proposed methodology was used to an experimentally kinetic model for a removal Cr (VI) from Yarrowia lipolytica biomass was showed satisfactory closed-loop performance the proposed controller. Starting from an off-line optimization performed in simulation, we present the controller implementation, focussing on the methodology required to could be suitable for implementation in real time. In our experimental results, we highlight some discrepancies between simulation and reality despite these differences, the controller managed to perform convergence to removal Cr (VI). Finally, the results validated with off-line samples suggest that the proposed control could be suitable for in application in potential scenarios for wastewater treatment.
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Affiliation(s)
| | - Pablo A López-Pérez
- Escuela Superior de Apan, Autonomous University of the State of Hidalgo, Carretera Apan-Calpulalpan, Hidalgo, México
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2
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Itto-Nakama K, Watanabe S, Ohnuki S, Kondo N, Kikuchi R, Nakamura T, Ogasawara W, Kasahara K, Ohya Y. Prediction of ethanol fermentation under stressed conditions using yeast morphological data. J Biosci Bioeng 2023; 135:210-216. [PMID: 36642617 DOI: 10.1016/j.jbiosc.2022.12.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/09/2022] [Accepted: 12/16/2022] [Indexed: 01/15/2023]
Abstract
A high sugar concentration is used as a starting condition in alcoholic fermentation by budding yeast, which shows changes in intracellular state and cell morphology under conditions of high-sugar stress. In this study, we developed artificial intelligence (AI) models to predict ethanol yields in yeast fermentation cultures under conditions of high-sugar stress using cell morphological data. Our method involves the extraction of high-dimensional morphological data from phase contrast images using image processing software, and predicting ethanol yields by supervised machine learning. The neural network algorithm produced the best performance, with a coefficient of determination (R2) of 0.95, and could predict ethanol yields well even 60 min in the future. Morphological data from cells cultured in low-glucose medium could not be used for accurate prediction under conditions of high-glucose stress. Cells cultured in high-concentration glucose medium were similar in terms of morphology to cells cultured under high osmotic pressure. Feeding experiments revealed that morphological changes differed depending on the fermentation phase. By monitoring the morphology of yeast under stress, it was possible to understand the intracellular physiological conditions, suggesting that analysis of cell morphology can aid the management and stable production of desired biocommodities.
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Affiliation(s)
- Kaori Itto-Nakama
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwano-ha, Kashiwa, Chiba 277-8562, Japan
| | - Shun Watanabe
- Chitose Laboratory Corp., Biotechnology Research Center, 2-13-3, Nogawahoncho, Miyamae-ku, Kawasaki, Kanagawa 216-0041, Japan
| | - Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwano-ha, Kashiwa, Chiba 277-8562, Japan
| | - Naoko Kondo
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwano-ha, Kashiwa, Chiba 277-8562, Japan
| | - Ryota Kikuchi
- Chitose Laboratory Corp., Biotechnology Research Center, 2-13-3, Nogawahoncho, Miyamae-ku, Kawasaki, Kanagawa 216-0041, Japan; Circular Bioeconomy Development, Office of Society Academia Collaboration for Innovation, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
| | - Toru Nakamura
- Chitose Laboratory Corp., Biotechnology Research Center, 2-13-3, Nogawahoncho, Miyamae-ku, Kawasaki, Kanagawa 216-0041, Japan
| | - Wataru Ogasawara
- Department of Bioengineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, Japan; Department of Science of Technology Innovation, Nagaoka University of Technology, 1603-1 Kamitomioka, Niigata 940-2188, Japan
| | - Ken Kasahara
- Chitose Laboratory Corp., Biotechnology Research Center, 2-13-3, Nogawahoncho, Miyamae-ku, Kawasaki, Kanagawa 216-0041, Japan
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwano-ha, Kashiwa, Chiba 277-8562, Japan; Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.
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3
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Dayakar B, Xavier KM, Ngasotter S, Layana P, Balange AK, Priyadarshini B, Nayak BB. Characterization of spray-dried carotenoprotein powder from Pacific white shrimp (Litopenaeus vannamei) shells and head waste extracted using papain: Antioxidant, spectroscopic, and microstructural properties. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113188] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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4
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Sá M, Ferrer-Ledo N, Gao F, Bertinetto CG, Jansen J, Crespo JG, Wijffels RH, Barbosa M, Galinha CF. Perspectives of fluorescence spectroscopy for online monitoring in microalgae industry. Microb Biotechnol 2022; 15:1824-1838. [PMID: 35175653 PMCID: PMC9151345 DOI: 10.1111/1751-7915.14013] [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: 08/27/2021] [Revised: 01/25/2022] [Accepted: 01/27/2022] [Indexed: 11/27/2022] Open
Abstract
Microalgae industrial production is viewed as a solution for alternative production of nutraceuticals, cosmetics, biofertilizers, and biopolymers. Throughout the years, several technological advances have been implemented, increasing the competitiveness of microalgae industry. However, online monitoring and real-time process control of a microalgae production factory still require further development. In this mini-review, non-destructive tools for online monitoring of cellular agriculture applications are described. Still, the focus is on the use of fluorescence spectroscopy to monitor several parameters (cell concentration, pigments, and lipids) in the microalgae industry. The development presented makes it the most promising solution for monitoring up-and downstream processes, different biological parameters simultaneously, and different microalgae species. The improvements needed for industrial application of this technology are also discussed.
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Affiliation(s)
- Marta Sá
- Bioprocess Engineering, Wageningen University and Research, Wageningen, 6708PB, The Netherlands.,Stichting imec Nederland - OnePlanet Research Center, Wageningen, 6708WH, The Netherlands
| | - Narcis Ferrer-Ledo
- Bioprocess Engineering, Wageningen University and Research, Wageningen, 6708PB, The Netherlands
| | - Fengzheng Gao
- Bioprocess Engineering, Wageningen University and Research, Wageningen, 6708PB, The Netherlands
| | - Carlo G Bertinetto
- Institute for Molecules and Materials (Analytical Chemistry), Radboud University, Nijmegen, The Netherlands
| | - Jeroen Jansen
- Institute for Molecules and Materials (Analytical Chemistry), Radboud University, Nijmegen, The Netherlands
| | - João G Crespo
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, FCT NOVA, Caparica, 2829-516, Portugal
| | - Rene H Wijffels
- Bioprocess Engineering, Wageningen University and Research, Wageningen, 6708PB, The Netherlands.,Faculty of Biosciences and Aquaculture, Nord University, Bodø, N-8049, Norway
| | - Maria Barbosa
- Bioprocess Engineering, Wageningen University and Research, Wageningen, 6708PB, The Netherlands
| | - Claudia F Galinha
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, FCT NOVA, Caparica, 2829-516, Portugal
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5
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Itto-Nakama K, Watanabe S, Kondo N, Ohnuki S, Kikuchi R, Nakamura T, Ogasawara W, Kasahara K, Ohya Y. AI-based forecasting of ethanol fermentation using yeast morphological data. Biosci Biotechnol Biochem 2021; 86:125-134. [PMID: 34751736 DOI: 10.1093/bbb/zbab188] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 10/25/2021] [Indexed: 11/12/2022]
Abstract
Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we tried to develop AI models to forecast ethanol yields in yeast fermentation cultures, using cell morphological data. Our platform involves the quick acquisition of yeast morphological images using a nonstaining protocol, extraction of high-dimensional morphological data using image processing software, and forecasting of ethanol yields via supervised machine learning. We found that the neural network algorithm produced the best performance, which had a coefficient of determination of >0.9 even at 30 and 60 min in the future. The model was validated using test data collected using the CalMorph-PC(10) system, which enables rapid image acquisition within 10 min. AI-based forecasting of product yields based on cell morphology will facilitate the management and stable production of desired biocommodities.
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Affiliation(s)
- Kaori Itto-Nakama
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Shun Watanabe
- Chitose Laboratory Corp., Biotechnology Research Center, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Naoko Kondo
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Ryota Kikuchi
- Chitose Laboratory Corp., Biotechnology Research Center, Miyamae-ku, Kawasaki, Kanagawa, Japan
- Circular Bioeconomy Development, Office of Society Academia Collaboration for Innovation, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto, Japan
| | - Toru Nakamura
- NRI System Techno Ltd., Hodogaya-ku, Yokohama, Kanagawa, Japan
| | - Wataru Ogasawara
- Department of Bioengineering, Nagaoka University of Technology, Nagaoka, Niigata, Japan
| | - Ken Kasahara
- Chitose Laboratory Corp., Biotechnology Research Center, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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6
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Zalai D, Kopp J, Kozma B, Küchler M, Herwig C, Kager J. Microbial technologies for biotherapeutics production: Key tools for advanced biopharmaceutical process development and control. DRUG DISCOVERY TODAY. TECHNOLOGIES 2021; 38:9-24. [PMID: 34895644 DOI: 10.1016/j.ddtec.2021.04.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 03/14/2021] [Accepted: 04/06/2021] [Indexed: 12/26/2022]
Abstract
Current trends in the biopharmaceutical market such as the diversification of therapies as well as the increasing time-to-market pressure will trigger the rethinking of bioprocess development and production approaches. Thereby, the importance of development time and manufacturing costs will increase, especially for microbial production. In the present review, we investigate three technological approaches which, to our opinion, will play a key role in the future of biopharmaceutical production. The first cornerstone of process development is the generation and effective utilization of platform knowledge. Building processes on well understood microbial and technological platforms allows to accelerate early-stage bioprocess development and to better condense this knowledge into multi-purpose technologies and applicable mathematical models. Second, the application of verified scale down systems and in silico models for process design and characterization will reduce the required number of large scale batches before dossier submission. Third, the broader availability of mathematical process models and the improvement of process analytical technologies will increase the applicability and acceptance of advanced control and process automation in the manufacturing scale. This will reduce process failure rates and subsequently cost of goods. Along these three aspects we give an overview of recently developed key tools and their potential integration into bioprocess development strategies.
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Affiliation(s)
- Denes Zalai
- Richter-Helm BioLogics GmbH & Co. KG, Suhrenkamp 59, 22335 Hamburg, Germany.
| | - Julian Kopp
- Research Division Biochemical Engineering, Institute of Chemical Environmental and Bioscience Engineering, Vienna University of Technology, Vienna, Austria
| | - Bence Kozma
- Research Division Biochemical Engineering, Institute of Chemical Environmental and Bioscience Engineering, Vienna University of Technology, Vienna, Austria
| | - Michael Küchler
- Richter-Helm BioLogics GmbH & Co. KG, Suhrenkamp 59, 22335 Hamburg, Germany
| | - Christoph Herwig
- Research Division Biochemical Engineering, Institute of Chemical Environmental and Bioscience Engineering, Vienna University of Technology, Vienna, Austria; Competence Center CHASE GmbH, Altenbergerstraße 69, 4040 Linz, Austria
| | - Julian Kager
- Research Division Biochemical Engineering, Institute of Chemical Environmental and Bioscience Engineering, Vienna University of Technology, Vienna, Austria
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7
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Abstract
Efficient monitoring is an open problem in the operation of anaerobic digestion processes, due to the lack of accurate, low-cost, and proper sensors for the on-line monitoring of key process variables. This paper presents two approaches for the indirect monitoring of the anaerobic digestion of cheese whey wastewater. First, the observability property is addressed using conventional and nonconventional techniques, including an observability index. Then, two model-based observer techniques, an extended Luenberger observer, a sliding mode observer, and a data-driven technique based on fractal analysis are formulated and discussed. The performance and capabilities of the proposed methodologies are illustrated on a validated model with experimental data of the anaerobic digestion of cheese whey. Experimental pH measurements are used for the data-driven approach based on fractal analysis. The experimental data sets correspond to experimental conditions (pH > 7.5 and temperature (T) = 40 °C) favoring volatile fatty acid (VFA) production (30 g/L) with simultaneously acceptable biogas production (3420 mL). Results also show that the proposed observers were able to predict satisfactory key process variables. On the other hand, the fractal analysis provides reliable qualitative trends of VFA production and chemical oxygen demand (COD) consumption.
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8
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Smiatek J, Jung A, Bluhmki E. Towards a Digital Bioprocess Replica: Computational Approaches in Biopharmaceutical Development and Manufacturing. Trends Biotechnol 2020; 38:1141-1153. [DOI: 10.1016/j.tibtech.2020.05.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/11/2020] [Accepted: 05/13/2020] [Indexed: 12/11/2022]
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9
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Brunner V, Siegl M, Geier D, Becker T. Biomass soft sensor for a Pichia pastoris fed-batch process based on phase detection and hybrid modeling. Biotechnol Bioeng 2020; 117:2749-2759. [PMID: 32510166 DOI: 10.1002/bit.27454] [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: 01/15/2020] [Revised: 05/05/2020] [Accepted: 06/05/2020] [Indexed: 12/16/2022]
Abstract
A common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data-driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO2 in the off-gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.
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Affiliation(s)
- Vincent Brunner
- Chair of Brewing and Beverage Technology, Technical University of Munich, Freising, Germany
| | - Manuel Siegl
- Chair of Brewing and Beverage Technology, Technical University of Munich, Freising, Germany
| | - Dominik Geier
- Chair of Brewing and Beverage Technology, Technical University of Munich, Freising, Germany
| | - Thomas Becker
- Chair of Brewing and Beverage Technology, Technical University of Munich, Freising, Germany
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10
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Single-Use Printed Biosensor for L-Lactate and Its Application in Bioprocess Monitoring. Processes (Basel) 2020. [DOI: 10.3390/pr8030321] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
There is a profound need in bioprocess manufacturing for low-cost single-use sensors that allow timely monitoring of critical product and production attributes. One such opportunity is screen-printed enzyme-based electrochemical sensors, which have the potential to enable low-cost online and/or off-line monitoring of specific parameters in bioprocesses. In this study, such a single-use electrochemical biosensor for lactate monitoring is designed and evaluated. Several aspects of its fabrication and use are addressed, including enzyme immobilization, stability, shelf-life and reproducibility. Applicability of the biosensor to off-line monitoring of bioprocesses was shown by testing in two common industrial bioprocesses in which lactate is a critical quality attribute (Corynebacterium fermentation and mammalian Chinese hamster ovary (CHO) cell cultivation). The specific response to lactate of the screen-printed biosensor was characterized by amperometric measurements. The usability of the sensor at typical industrial culture conditions was favorably evaluated and benchmarked with commonly used standard methods (HPLC and enzymatic kits). The single-use biosensor allowed fast and accurate detection of lactate in prediluted culture media used in industrial practice. The design and fabrication of the biosensor could most likely be adapted to several other critical bioprocess analytes using other specific enzymes. This makes this single-use screen-printed biosensor concept a potentially interesting and versatile tool for further applications in bioprocess monitoring.
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11
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Yousefi-Darani A, Paquet-Durand O, Hitzmann B. The Kalman Filter for the Supervision of Cultivation Processes. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2020; 177:95-125. [PMID: 33174065 DOI: 10.1007/10_2020_145] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
In the era of technology and digitalization, the process industries are undergoing a digital transformation. The available process models, advance sensor technologies, enhanced computational power and a broad set of data analytical techniques enable solid bases for digital transformation in the biopharmaceutical industry.Among various data analytical techniques, the Kalman filter and its non-linear extensions are powerful tools for prediction of reliable process information. The combination of the Kalman filter with a virtual representation of the bioprocess, called digital twin, can provide real-time available process information. Incorporation of such variables in process operation can provide improved control performance with enhanced productivity.In this chapter the linear discrete Kalman filter, the extended Kalman filter and the unscented Kalman filters are described and a brief overview of applications of the Kalman filter and its non-linear extensions to bioreactors are presented. Furthermore, in a case study an example of the digital twin of the backer's yeast batch cultivation process is presented. A digital twin of a bioreactor mirrors the processes of the real bioreactor. It contains the physical parts, the process model and prediction algorithm to predict the bioprocess variables. These values could be used for optimization and control of the process.
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Affiliation(s)
- Abdolrahim Yousefi-Darani
- Department of Process Analytics and Cereal Science, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany.
| | - Olivier Paquet-Durand
- Department of Process Analytics and Cereal Science, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany
| | - Bernd Hitzmann
- Department of Process Analytics and Cereal Science, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany
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Dahlmann K, Busse C, Aupert F, de Vries I, Marquard D, Solle D, Lammers F, Scheper T. Online monitoring of the cell-specific oxygen uptake rate with an in situ combi-sensor. Anal Bioanal Chem 2019; 412:2111-2121. [PMID: 31802179 DOI: 10.1007/s00216-019-02260-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 10/28/2019] [Accepted: 11/05/2019] [Indexed: 11/30/2022]
Abstract
In a biotechnological process, standard monitored process variables are pH, partial oxygen pressure (pO2), and temperature. These process variables are important, but they do not give any information about the metabolic activity of the cell. The ISICOM is an in situ combi-sensor that is measuring the cell-specific oxygen uptake rate (qOUR) online. This variable allows a qualitative judgement of metabolic cell activity. The measuring principle of the ISICOM is based on a volume element enclosed into a small measuring chamber. Inside the measuring chamber, the pO2 and the scattered light is measured. Within a defined measuring interval, the chamber closes, and the oxygen supply for the cells is interrupted. The decreasing oxygen concentration is recorded by the pO2 optode. This measuring principle, known as the dynamic method, determines the oxygen uptake rate (OUR). Together with the scattered light signal, the cell concentration is estimated and the qOUR is available online. The design of the ISICOM is focused on functionality, sterility, long-term stability, and response time behavior so the sensor can be used in bioprocesses. With the ISICOM, measurement of online and in situ measurement of the OUR is possible. The OUR and qOUR online measurement of an animal cell batch cultivation is demonstrated, with maximum values of OUR = 2.5 mmol L-1 h-1 and a qOUR = 9.5 pmol cell-1 day-1. Information about limitation of the primary and secondary substrate is derived by the monitoring of the metabolic cell activity of bacteria and yeast cultivation processes. This sensor contributes to a higher process understanding by offering an online view on to the cell behavior. In the sense of process analytical technology (PAT), this important information is needed for bioprocesses to realize a knowledge base process control.
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Affiliation(s)
- Katharina Dahlmann
- Institut für Technische Chemie, Gottfried Wilhelm Leibniz Universität Hannover, Callinstraße 5, 30167, Hannover, Germany
| | - Christoph Busse
- Institut für Technische Chemie, Gottfried Wilhelm Leibniz Universität Hannover, Callinstraße 5, 30167, Hannover, Germany
| | - Florian Aupert
- Institut für Technische Chemie, Gottfried Wilhelm Leibniz Universität Hannover, Callinstraße 5, 30167, Hannover, Germany
| | - Ingo de Vries
- Institut für Technische Chemie, Gottfried Wilhelm Leibniz Universität Hannover, Callinstraße 5, 30167, Hannover, Germany
| | - Daniel Marquard
- Institut für Technische Chemie, Gottfried Wilhelm Leibniz Universität Hannover, Callinstraße 5, 30167, Hannover, Germany
| | - Dörte Solle
- Institut für Technische Chemie, Gottfried Wilhelm Leibniz Universität Hannover, Callinstraße 5, 30167, Hannover, Germany.
| | - Frank Lammers
- Sanofi, Industriepark Höchst, 65926, Frankfurt am Main, Germany
| | - Thomas Scheper
- Institut für Technische Chemie, Gottfried Wilhelm Leibniz Universität Hannover, Callinstraße 5, 30167, Hannover, Germany
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Soft Sensor-Based Monitoring and Efficient Control Strategies of Biomass Concentration for Continuous Cultures of Haloferax mediterranei and Their Application to an Industrial Production Chain. Microorganisms 2019; 7:microorganisms7120648. [PMID: 31817128 PMCID: PMC6956367 DOI: 10.3390/microorganisms7120648] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 11/21/2019] [Accepted: 12/03/2019] [Indexed: 11/17/2022] Open
Abstract
Continuous bioprocessing using cell retention allows the achievement of high space-time yields for slow-growing organisms such as halophiles. However, the lack of efficient methods for monitoring and control limits the application of biotechnological processes in the industry. The aim of this study was to implement a control and online monitoring strategy for biomass in continuous cultures. For the first time, a feedforward cultivation strategy in a membrane-based cell retention system allowed to control the biomass concentration of the extreme halophilic Haloferax mediterranei at defined levels. Moreover, soft sensor-based biomass estimation allowed reliable monitoring of biomass online. Application of the combined monitoring and control strategy using industrial process water containing formate, phenol, aniline and 4,4′-methylenedianiline could for the first time demonstrate high throughput degradation in this extremophilic bioremediation process, obtaining degradation efficiencies of up to 100%. This process demonstrates the usefulness of continuous halophilic cultures in a circular economy application.
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14
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Biological and chemical diversity go hand in hand: Basidiomycota as source of new pharmaceuticals and agrochemicals. Biotechnol Adv 2019; 37:107344. [PMID: 30738916 DOI: 10.1016/j.biotechadv.2019.01.011] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 01/30/2019] [Accepted: 01/31/2019] [Indexed: 12/20/2022]
Abstract
The Basidiomycota constitutes the second largest higher taxonomic group of the Fungi after the Ascomycota and comprises over 30.000 species. Mycelial cultures of Basidiomycota have already been studied since the 1950s for production of antibiotics and other beneficial secondary metabolites. Despite the fact that unique and selective compounds like pleuromutilin were obtained early on, it took several decades more until they were subjected to a systematic screening for antimicrobial and anticancer activities. These efforts led to the discovery of the strobilurins and several hundreds of further compounds that mainly constitute terpenoids. In parallel the traditional medicinal mushrooms of Asia were also studied intensively for metabolite production, aimed at finding new therapeutic agents for treatment of various diseases including metabolic disorders and the central nervous system. While the evaluation of this organism group has in general been more tedious as compared to the Ascomycota, the chances to discover new metabolites and to develop them further to candidates for drugs, agrochemicals and other products for the Life Science industry have substantially increased over the past decade. This is owing to the revolutionary developments in -OMICS techniques, bioinformatics, analytical chemistry and biotechnological process technology, which are steadily being developed further. On the other hand, the new developments in polythetic fungal taxonomy now also allow a more concise selection of previously untapped organisms. The current review is dedicated to summarize the state of the art and to give an outlook to further developments.
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15
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Hlongwane GN, Dodoo-Arhin D, Wamwangi D, Daramola MO, Moothi K, Iyuke SE. DNA hybridisation sensors for product authentication and tracing: State of the art and challenges. SOUTH AFRICAN JOURNAL OF CHEMICAL ENGINEERING 2019. [DOI: 10.1016/j.sajce.2018.11.002] [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: 10/27/2022] Open
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16
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Martin AL, Satjaritanun P, Shimpalee S, Devivo BA, Weidner J, Greenway S, Henson JM, Turick CE. In-situ electrochemical analysis of microbial activity. AMB Express 2018; 8:162. [PMID: 30288622 PMCID: PMC6172163 DOI: 10.1186/s13568-018-0692-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 09/26/2018] [Indexed: 01/07/2023] Open
Abstract
Microbes have a wide range of metabolic capabilities available that makes them industrially useful organisms. Monitoring these metabolic processes is a crucial component in efficient industrial application. Unfortunately, monitoring these metabolic processes can often be invasive and time consuming and expensive, especially within an anaerobic environment. Electrochemical techniques, such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) offer a non-invasive approach to monitor microbial activity and growth. EIS and CV were used to monitor Clostridium phytofermentans, an anaerobic and endospore-forming bacterium. C. phytofermentans ferments a wide range of sugars into hydrogen, acetate, and ethanol as fermentation by-products. For this study, both traditional microbiological and electrochemical techniques were used to monitor the growth of C. phytofermentans and the formation of fermentation products. An irreversible reduction peak was observed using CV beginning at mid-logarithmic phase of growth. This peak was associated with C. phytofermentans and not the spent medium and was indicative of a decrease in carbon and energy sources to the cells. Additionally, EIS analysis during growth provided information related to increased charge transfer resistance of the culture also as a function of carbon and energy source depletion. Results demonstrate that CV and EIS are useful tools in the monitoring the physiological status of bioprocesses.
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17
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Dissolved Oxygen Control of Batch Bioreactor using Model Reference Adaptive Control scheme. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.ifacol.2018.06.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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18
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Kroll P, Hofer A, Ulonska S, Kager J, Herwig C. Model-Based Methods in the Biopharmaceutical Process Lifecycle. Pharm Res 2017; 34:2596-2613. [PMID: 29168076 PMCID: PMC5736780 DOI: 10.1007/s11095-017-2308-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 09/21/2017] [Indexed: 12/18/2022]
Abstract
Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.
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Affiliation(s)
- Paul Kroll
- Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, Vienna University of Technology, Gumpendorfer Straße 1a - 166/4, A-1060, Vienna, Austria
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, TU Wien, Vienna, Austria
| | - Alexandra Hofer
- Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, Vienna University of Technology, Gumpendorfer Straße 1a - 166/4, A-1060, Vienna, Austria
| | - Sophia Ulonska
- Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, Vienna University of Technology, Gumpendorfer Straße 1a - 166/4, A-1060, Vienna, Austria
| | - Julian Kager
- Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, Vienna University of Technology, Gumpendorfer Straße 1a - 166/4, A-1060, Vienna, Austria
| | - Christoph Herwig
- Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, Vienna University of Technology, Gumpendorfer Straße 1a - 166/4, A-1060, Vienna, Austria.
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, TU Wien, Vienna, Austria.
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19
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Biosensors based on β-galactosidase enzyme: Recent advances and perspectives. Anal Biochem 2017; 535:1-11. [DOI: 10.1016/j.ab.2017.07.019] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 07/08/2017] [Accepted: 07/18/2017] [Indexed: 11/19/2022]
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20
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Theuer L, Lehmann M, Junne S, Neubauer P, Birkholz M. Micro-Electromechanical Affinity Sensor for the Monitoring of Glucose in Bioprocess Media. Int J Mol Sci 2017; 18:E1235. [PMID: 28594350 PMCID: PMC5486058 DOI: 10.3390/ijms18061235] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Revised: 04/23/2017] [Accepted: 05/31/2017] [Indexed: 12/03/2022] Open
Abstract
An affinity-viscometry-based micro-sensor probe for continuous glucose monitoring was investigated with respect to its suitability for bioprocesses. The sensor operates with glucose and dextran competing as binding partner for concanavalin A, while the viscosity of the assay scales with glucose concentration. Changes in viscosity are determined with a micro-electromechanical system (MEMS) in the measurement cavity of the sensor probe. The study aimed to elucidate the interactions between the assay and a typical phosphate buffered bacterial cultivation medium. It turned out that contact with the medium resulted in a significant long-lasting drift of the assay's viscosity at zero glucose concentration. Adding glucose to the medium lowers the drift by a factor of eight. The cglc values measured off-line with the glucose sensor for monitoring of a bacterial cultivation were similar to the measurements with an enzymatic assay with a difference of less than ±0.15 g·L-1. We propose that lectin agglomeration, the electro-viscous effect, and constitutional changes of concanavalin A due to exchanges of the incorporated metal ions may account for the observed viscosity increase. The study has demonstrated the potential of the MEMS sensor to determine sensitive viscosity changes within very small sample volumes, which could be of interest for various biotechnological applications.
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Affiliation(s)
- Lorenz Theuer
- Chair of Bioprocess Engineering, Department of Biotechnology, Technical University Berlin, ACK24, Ackerstr. 76, 13355 Berlin, Germany.
| | - Micha Lehmann
- Chair of Bioprocess Engineering, Department of Biotechnology, Technical University Berlin, ACK24, Ackerstr. 76, 13355 Berlin, Germany.
| | - Stefan Junne
- Chair of Bioprocess Engineering, Department of Biotechnology, Technical University Berlin, ACK24, Ackerstr. 76, 13355 Berlin, Germany.
| | - Peter Neubauer
- Chair of Bioprocess Engineering, Department of Biotechnology, Technical University Berlin, ACK24, Ackerstr. 76, 13355 Berlin, Germany.
| | - Mario Birkholz
- IHP, Im Technologiepark 25, 15236 Frankfurt (Oder), Germany.
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21
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Brüning S, Gerlach I, Pörtner R, Mandenius CF, Hass VC. Modeling Suspension Cultures of Microbial and Mammalian Cells with an Adaptable Six-Compartment Model. Chem Eng Technol 2017. [DOI: 10.1002/ceat.201600639] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Simone Brüning
- University of Applied Sciences Bremen; Institute for Environmental and Biotechnology; Neustadtswall 30 28199 Bremen Germany
| | - Inga Gerlach
- University of Applied Sciences Bremen; Institute for Environmental and Biotechnology; Neustadtswall 30 28199 Bremen Germany
- Linköping University; Division of Biotechnology/IFM; 58183 Linköping Sweden
| | - Ralf Pörtner
- Hamburg University of Technology; Institute for Bioprocess and Biosystems Engineering; Denickestraße 15 21073 Hamburg Germany
| | | | - Volker C. Hass
- Furtwangen University; Faculty of Medical and Life Sciences; Jakob-Kienzle-Straße 17 78054 Villingen-Schwenningen Germany
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22
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Nascimento RJAD, Macedo GRD, Santos ESD, Oliveira JAD. Real time and in situ Near-Infrared Spectroscopy (Nirs) for Quantitative Monitoring of Biomass, Glucose, Ethanol and Glycerine concentrations in an alcoholic fermentation. BRAZILIAN JOURNAL OF CHEMICAL ENGINEERING 2017. [DOI: 10.1590/0104-6632.20170342s20150347] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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23
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Bolivar JM, Eisl I, Nidetzky B. Advanced characterization of immobilized enzymes as heterogeneous biocatalysts. Catal Today 2016. [DOI: 10.1016/j.cattod.2015.05.004] [Citation(s) in RCA: 129] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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24
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Berry BN, Dobrowsky TM, Timson RC, Kshirsagar R, Ryll T, Wiltberger K. Quick generation of Raman spectroscopy based in-process glucose control to influence biopharmaceutical protein product quality during mammalian cell culture. Biotechnol Prog 2015; 32:224-34. [DOI: 10.1002/btpr.2205] [Citation(s) in RCA: 88] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Revised: 11/10/2015] [Indexed: 11/11/2022]
Affiliation(s)
- Brandon N. Berry
- Biogen Inc; 250 Binney Street 02142 Cambridge Massachusetts United States
| | | | - Rebecca C. Timson
- Biogen Inc; 250 Binney Street 02142 Cambridge Massachusetts United States
| | - Rashmi Kshirsagar
- Biogen Inc; 250 Binney Street 02142 Cambridge Massachusetts United States
| | - Thomas Ryll
- Biogen Inc; 250 Binney Street 02142 Cambridge Massachusetts United States
| | - Kelly Wiltberger
- Biogen Inc; 5000 Davis Drive 27709 Research Triangle Park North Carolina United States
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25
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Zhao L, Wang J, Yu T, Chen K, Liu T. Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements. Chin J Chem Eng 2015. [DOI: 10.1016/j.cjche.2015.09.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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26
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Cui L, Xu Y, Hu Z, Wang Z, Yuan J. Online Application-Oriented Optimal Scheduling for 2-keto-l-gulonic Acid Production. CAN J CHEM ENG 2015. [DOI: 10.1002/cjce.22206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Lei Cui
- College of Engineering; Shanghai Second Polytechnic University; 2360 Jinhai Road, Shanghai 201209 China
| | - Yuanyuan Xu
- Logistics Engineering College; Shanghai Maritime University; Shanghai 200135 China
| | - Zhihua Hu
- College of Engineering; Shanghai Second Polytechnic University; 2360 Jinhai Road, Shanghai 201209 China
| | - Zhifeng Wang
- College of Engineering; Shanghai Second Polytechnic University; 2360 Jinhai Road, Shanghai 201209 China
| | - Jingqi Yuan
- Department of Automation; Shanghai Jiao Tong University; Shanghai 200240 China
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27
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Simon LL, Pataki H, Marosi G, Meemken F, Hungerbühler K, Baiker A, Tummala S, Glennon B, Kuentz M, Steele G, Kramer HJM, Rydzak JW, Chen Z, Morris J, Kjell F, Singh R, Gani R, Gernaey KV, Louhi-Kultanen M, O’Reilly J, Sandler N, Antikainen O, Yliruusi J, Frohberg P, Ulrich J, Braatz RD, Leyssens T, von Stosch M, Oliveira R, Tan RBH, Wu H, Khan M, O’Grady D, Pandey A, Westra R, Delle-Case E, Pape D, Angelosante D, Maret Y, Steiger O, Lenner M, Abbou-Oucherif K, Nagy ZK, Litster JD, Kamaraju VK, Chiu MS. Assessment of Recent Process Analytical Technology (PAT) Trends: A Multiauthor Review. Org Process Res Dev 2015. [DOI: 10.1021/op500261y] [Citation(s) in RCA: 269] [Impact Index Per Article: 29.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
| | - Hajnalka Pataki
- Department
of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary
| | - György Marosi
- Department
of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary
| | - Fabian Meemken
- Department
of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg
1, 8093 Zürich, Switzerland
| | - Konrad Hungerbühler
- Department
of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg
1, 8093 Zürich, Switzerland
| | - Alfons Baiker
- Department
of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg
1, 8093 Zürich, Switzerland
| | - Srinivas Tummala
- Chemical
Development, Bristol-Myers Squibb Company, One Squibb Dr, New Brunswick, New Jersey 08903, United States
| | - Brian Glennon
- Synthesis
and Solid State Pharmaceutical Centre, School of Chemical and Bioprocess
Engineering, University College Dublin, Belfield, Dublin 4, Ireland
- APC Ltd, Belfield Innovation
Park, Dublin 4, Ireland
| | - Martin Kuentz
- School of Life
Sciences, Institute of Pharma Technology, University of Applied Sciences and Arts Northwestern Switzerland, Gründenstrasse 40, 4132 Muttenz, Switzerland
| | - Gerry Steele
- PharmaCryst Consulting
Ltd., Loughborough, Leicestershire LE11 3HN, U.K
| | - Herman J. M. Kramer
- Intensified Reaction & Separation Systems, Delft University of Technology, Leeghwaterstraat 39, 2628 CB Delft, The Netherlands
| | - James W. Rydzak
- GlaxoSmithKline Pharmaceuticals, 709 Swedeland Rd, King of
Prussia, Pennsylvania 19406, United States
| | - Zengping Chen
- State Key
Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry
and Chemical Engineering, Hunan University, Changsha, Hunan 410082, PR China
| | - Julian Morris
- Centre for Process Analytics & Control Technology, School of Chemical Engineering & Advanced Materials, Newcastle University, Newcastle upon Tyne, Tyne and Wear NE17RU, U.K
| | - Francois Kjell
- Siemens nv/sa,
Industry
Automation − SIPAT Industry Software, Marie Curie Square 30, 1070 Brussels, Belgium
| | - Ravendra Singh
- CAPEC-PROCESS,
Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), Building 229, DK-2800 Lyngby, Denmark
| | - Rafiqul Gani
- CAPEC-PROCESS,
Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), Building 229, DK-2800 Lyngby, Denmark
| | - Krist V. Gernaey
- CAPEC-PROCESS,
Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), Building 229, DK-2800 Lyngby, Denmark
| | - Marjatta Louhi-Kultanen
- Department
of Chemical Technology, Lappeenranta University of Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland
| | - John O’Reilly
- Roche Ireland
Limited, Clarecastle, Co. Clare, Ireland
| | - Niklas Sandler
- Pharmaceutical
Sciences Laboratory, Department of Biosciences, Abo Akademi University, Artillerigatan 6, 20520 Turku, Finland
| | - Osmo Antikainen
- Division
of Pharmaceutical Technology, Faculty of Pharmacy, University of Helsinki, Yliopistonkatu 4, 00100 Helsinki, Finland
| | - Jouko Yliruusi
- Division
of Pharmaceutical Technology, Faculty of Pharmacy, University of Helsinki, Yliopistonkatu 4, 00100 Helsinki, Finland
| | - Patrick Frohberg
- Center of
Engineering Science, Thermal Process Engineering, Martin Luther University Halle-Wittenberg, D-06099 Halle (Saale), Germany
| | - Joachim Ulrich
- Center of
Engineering Science, Thermal Process Engineering, Martin Luther University Halle-Wittenberg, D-06099 Halle (Saale), Germany
| | - Richard D. Braatz
- Massachusetts Institute
of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Tom Leyssens
- Institute
of Condensed Matter and Nanosciences, Université Catholique de Louvain, Place Louis Pasteur 1, 1348 Louvain-la-Neuve, Belgium
| | - Moritz von Stosch
- REQUIMTE
- Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 1099-085 Caparica, Portugal
- HybPAT, Caparica, Portugal
| | - Rui Oliveira
- REQUIMTE
- Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 1099-085 Caparica, Portugal
- HybPAT, Caparica, Portugal
| | - Reginald B. H. Tan
- Institute
of Chemical and Engineering Sciences, A*Star, 1 Pesek Road, Singapore 627833
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576
| | - Huiquan Wu
- Division
of Product Quality Research, Office of Testing and Research, Office
of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration (FDA), Silver Spring, Maryland 20993, United States
| | - Mansoor Khan
- Division
of Product Quality Research, Office of Testing and Research, Office
of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration (FDA), Silver Spring, Maryland 20993, United States
| | - Des O’Grady
- Mettler Toledo
AutoChem, 7075 Samuel Morse Drive, Columbia, Maryland 20146, United States
| | - Anjan Pandey
- Mettler Toledo
AutoChem, 7075 Samuel Morse Drive, Columbia, Maryland 20146, United States
| | - Remko Westra
- FMC Technologies B.V., Delta 101, 6825 MN Arnhem, The Netherlands
| | - Emmanuel Delle-Case
- University of Tulsa, 800 South Tucker
Drive, Tulsa, Oklahoma 74104, United States
| | - Detlef Pape
- ABB Corporate Research Center, Segelhofstrasse
1K, 5405, Dättwil, Baden, Switzerland
| | - Daniele Angelosante
- ABB Corporate Research Center, Segelhofstrasse
1K, 5405, Dättwil, Baden, Switzerland
| | - Yannick Maret
- ABB Corporate Research Center, Segelhofstrasse
1K, 5405, Dättwil, Baden, Switzerland
| | - Olivier Steiger
- ABB Corporate Research Center, Segelhofstrasse
1K, 5405, Dättwil, Baden, Switzerland
| | - Miklós Lenner
- ABB Corporate Research Center, Segelhofstrasse
1K, 5405, Dättwil, Baden, Switzerland
| | - Kaoutar Abbou-Oucherif
- School of
Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Zoltan K. Nagy
- School of
Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
- Chemical
Engineering Department, Loughborough University, Loughborough, LE11 3TU, U.K
| | - James D. Litster
- School of
Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Vamsi Krishna Kamaraju
- Synthesis
and Solid State Pharmaceutical Centre, School of Chemical and Bioprocess
Engineering, University College Dublin, Belfield, Dublin 4, Ireland
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576
| | - Min-Sen Chiu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576
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28
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Birle S, Hussein MA, Becker T. On-line yeast propagation process monitoring and control using an intelligent automatic control system. Eng Life Sci 2014. [DOI: 10.1002/elsc.201400058] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Stephan Birle
- Center of Life and Food Sciences Weihenstephan; BioPAT; Technische Universität München; Freising Germany
| | - Mohamed Ahmed Hussein
- Center of Life and Food Sciences Weihenstephan; BioPAT; Technische Universität München; Freising Germany
| | - Thomas Becker
- Center of Life and Food Sciences Weihenstephan; BioPAT; Technische Universität München; Freising Germany
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29
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Maschke RW, Geipel K, Bley T. Modeling of plant in vitro cultures: overview and estimation of biotechnological processes. Biotechnol Bioeng 2014; 112:1-12. [PMID: 25060508 DOI: 10.1002/bit.25346] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2014] [Revised: 06/05/2014] [Accepted: 07/15/2014] [Indexed: 01/09/2023]
Abstract
Plant cell and tissue cultivations are of growing interest for the production of structurally complex and expensive plant-derived products, especially in pharmaceutical production. Problems with up-scaling, low yields, and high-priced process conditions result in an increased demand for models to provide comprehension, simulation, and optimization of production processes. In the last 25 years, many models have evolved in plant biotechnology; the majority of them are specialized models for a few selected products or nutritional conditions. In this article we review, delineate, and discuss the concepts and characteristics of the most commonly used models. Therefore, the authors focus on models for plant suspension and submerged hairy root cultures. The article includes a short overview of modeling and mathematics and integrated parameters, as well as the application scope for each model. The review is meant to help researchers better understand and utilize the numerous models published for plant cultures, and to select the most suitable model for their purposes.
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Affiliation(s)
- Rüdiger W Maschke
- Institute of Food Technology and Bioprocess Engineering, Technische Universität Dresden, Bergstraße 120, 01069, Dresden, Germany
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30
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Steinhoff RF, Ivarsson M, Habicher T, Villiger TK, Boertz J, Krismer J, Fagerer SR, Soos M, Morbidelli M, Pabst M, Zenobi R. High-throughput nucleoside phosphate monitoring in mammalian cell fed-batch cultivation using quantitative matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Biotechnol J 2014; 10:190-8. [PMID: 25139677 DOI: 10.1002/biot.201400292] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Revised: 06/19/2014] [Accepted: 08/18/2014] [Indexed: 01/07/2023]
Abstract
Current methods for monitoring multiple intracellular metabolite levels in parallel are limited in sample throughput capabilities and analyte selectivity. This article presents a novel high-throughput method based on matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry (TOF-MS) for monitoring intracellular metabolite levels in fed-batch processes. The MALDI-TOF-MS method presented here is based on a new microarray sample target and allows the detection of nucleoside phosphates and various other metabolites using stable isotope labeled internal standards. With short sample preparation steps and thus high sample throughput capabilities, the method is suitable for monitoring mammalian cell cultures, such as antibody producing hybridoma cell lines in industrial environments. The method is capable of reducing the runtime of standard LC-UV methods to approximately 1 min per sample (including 10 technical replicates). Its performance is exemplarily demonstrated in an 8-day monitoring experiment of independently controlled fed-batches, containing an antibody producing mouse hybridoma cell culture. The monitoring profiles clearly confirmed differences between cultivation conditions. Hypothermia and hyperosmolarity were studied in four bioreactors, where hypothermia was found to have a positive effect on the longevity of the cell culture, whereas hyperosmolarity lead to an arrest of cell proliferation. The results are in good agreement with HPLC-UV cross validation experiments. Subsequent principal component analysis (PCA) clearly separates the different bioreactor conditions based on the measured mass spectral profiles. This method is not limited to any cell line and can be applied as a process analytical tool in biotechnological processes.
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31
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Kruid J, Fogel R, Limson J. Voltammetric investigation of complex growth media at a bare glassy carbon electrode: A case study of oxytetracycline. Electrochim Acta 2014. [DOI: 10.1016/j.electacta.2013.08.188] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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32
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Pro-Optimizer: A Novel Web-Enabled Optimization Engine for Microbial Fermentations. BIOTECHNOL BIOTEC EQ 2014. [DOI: 10.2478/v10133-010-0092-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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33
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Abstract
This review covers key developments and trends in chemical process control during the past two decades. Control methodologies and related supporting technologies are covered, and recent trends in applications are also examined. After the widespread adoption of model-based techniques by industry, control interest has begun to move beyond the traditional realm of readily measured variables to include chemical compositions and particle features. However, the shift is being slowed by the shortage of accurate, reliable, and inexpensive sensing devices. Although the past two decades saw no new major theoretical or methodological advances, several important incremental improvements and extensions have been made to help the ripening of the technologies developed in the preceding two decades. Control is regaining its importance owing to society's renewed focus on energy and the maturation of various emerging technologies, but a community-wide consensus on what general problems should be solved is lacking.
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Affiliation(s)
- Jay H Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 305-701 Daejeon, Korea;
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34
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Abstract
Lab-scale stirred-tank bioreactors (0.2-20 l) are used for fundamental research on animal cells and in process development and troubleshooting for large-scale production. In this chapter, different configurations of bioreactor systems are shortly discussed and setting up these different configurations is described. In addition, online measurement and control of bioreactor parameters is described, with special attention to controller settings (PID) and online measurement of oxygen consumption and carbon dioxide production. Finally, methods for determining the oxygen transfer coefficient are described.
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Affiliation(s)
- Dirk E Martens
- Bioprocess Engineering, Wageningen University, Wageningen, The Netherlands,
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35
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Li B, Shanahan M, Calvet A, Leister KJ, Ryder AG. Comprehensive, quantitative bioprocess productivity monitoring using fluorescence EEM spectroscopy and chemometrics. Analyst 2014; 139:1661-71. [DOI: 10.1039/c4an00007b] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Using fluorescence excitation-emission matrix spectroscopy and chemometric methods we demonstrate an effective and rapid method for quantitative monitoring of a mammalian cell culture based manufacturing process.
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Affiliation(s)
- Boyan Li
- Nanoscale Biophotonics Laboratory
- School of Chemistry
- National University of Ireland
- Galway, Ireland
| | - Michael Shanahan
- Nanoscale Biophotonics Laboratory
- School of Chemistry
- National University of Ireland
- Galway, Ireland
| | - Amandine Calvet
- Nanoscale Biophotonics Laboratory
- School of Chemistry
- National University of Ireland
- Galway, Ireland
| | - Kirk J. Leister
- Bristol-Myers Squibb
- Process Analytical Sciences
- Syracuse, USA
| | - Alan G. Ryder
- Nanoscale Biophotonics Laboratory
- School of Chemistry
- National University of Ireland
- Galway, Ireland
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36
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Herold S, King R. Automatic identification of structured process models based on biological phenomena detected in (fed-)batch experiments. Bioprocess Biosyst Eng 2013; 37:1289-304. [PMID: 24317484 DOI: 10.1007/s00449-013-1100-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2013] [Accepted: 11/19/2013] [Indexed: 10/25/2022]
Abstract
In this paper, we present a set of methods to automatically propose structured process models from an automated analysis of (fed-)batch experiments. Therefore, the measurements are numerically compensated for the influence of feeding and sampling, and the qualitative behavior of the measurements is revealed. As measurements from fermentations are inherently noisy, we introduce a method that divides the compensated curves into several episodes in a probabilistic framework to better handle these shortcomings. The probability of biological phenomena that reveal crucial information about the underlying reaction network is calculated. Since the phenomena detection is measurement-driven, its reliability depends on the measurement situation, e.g., the number of samples taken and experiments considered, measurement noise, etc. We show a possible approach to test the uncertainty of the phenomena detection against these influences. Finally, model structures are proposed automatically based on the detected biological phenomena. An experimental validation of the approach is shown, using real fermentation data from fed-batch cultivations of Streptomyces tendae.
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Affiliation(s)
- Sebastian Herold
- Chair of Measurement and Control, Technische Universität Berlin, Secr. ER 2-1, Hardenbergstraße 36a, 10623, Berlin, Germany,
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37
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Masiero SS, Trierweiler JO, Farenzena M, Escobar M, Trierweiler LF, Ranzan C. Evaluation of wavelength selection methods for 2D fluorescence spectra applied to bioprocesses characterization. BRAZILIAN JOURNAL OF CHEMICAL ENGINEERING 2013. [DOI: 10.1590/s0104-66322013000200007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
| | | | - M. Farenzena
- Universidade Federal do Rio Grande do Sul, Brasil
| | - M. Escobar
- Universidade Federal do Rio Grande do Sul, Brasil
| | | | - C. Ranzan
- Universidade Federal do Rio Grande do Sul, Brasil
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38
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39
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Bolivar JM, Consolati T, Mayr T, Nidetzky B. Shine a light on immobilized enzymes: real-time sensing in solid supported biocatalysts. Trends Biotechnol 2013; 31:194-203. [PMID: 23384504 DOI: 10.1016/j.tibtech.2013.01.004] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2012] [Revised: 01/06/2013] [Accepted: 01/07/2013] [Indexed: 01/01/2023]
Abstract
Enzyme immobilization on solid supports has been key to biotransformation development. Although technologies for immobilization have largely reached maturity, the resulting biocatalysts are not well understood mechanistically. One limitation is that their internal environment is usually inferred from external data. Therefore, biological consequences of the immobilization remain masked by physical effects of mass transfer, obstructing further development. Work reviewed herein shows that opto-chemical sensing performed directly within the solid support enables the biocatalyst's internal environment to be uncovered quantitatively and in real time. Non-invasive methods of intraparticle pH and O2 determination are presented, and their use as process analytical tools for development of heterogeneous biocatalysts is described. Method diversification to other analytes remains a challenging task for the future.
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Affiliation(s)
- Juan M Bolivar
- Institute of Biotechnology and Biochemical Engineering, Graz University of Technology, Petersgasse 12, A-8010 Graz, Austria
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40
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On-line multiple component analysis for efficient quantitative bioprocess development. J Biotechnol 2013; 163:362-70. [DOI: 10.1016/j.jbiotec.2012.03.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2011] [Revised: 03/08/2012] [Accepted: 03/13/2012] [Indexed: 11/22/2022]
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41
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An advanced monitoring platform for rational design of recombinant processes. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2012. [PMID: 23207722 DOI: 10.1007/10_2012_169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Bioprocess engineering is an application-oriented science in an interdisciplinary environment, and a meaningful combination of different scientific disciplines is the only way to meet the challenges of bioprocess complexity. Setting up a reasoned process monitoring platform is the first step in an iterative procedure aiming at process and systems understanding, being the key to rational and innovative bioprocess design. This chapter describes a comprehensive process monitoring platform and how the resulting knowledge is translated into new strategies in process and/or host cell design.
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42
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Galvanauskas V, Grigs O, Vanags J, Dubencovs K, Stepanova V. Model-based optimization and pO2control of fed-batchEscherichia coliandSaccharomyces cerevisiaecultivation processes. Eng Life Sci 2012. [DOI: 10.1002/elsc.201200012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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43
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Spadiut O, Dietzsch C, Posch A, Herwig C. Evaluating online sampling probes for substrate concentration and protein production by a Design of Experiments screening approach. Eng Life Sci 2012. [DOI: 10.1002/elsc.201100228] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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44
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Bioreactor monitoring with spectroscopy and chemometrics: a review. Anal Bioanal Chem 2012; 404:1211-37. [DOI: 10.1007/s00216-012-6073-9] [Citation(s) in RCA: 185] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2012] [Accepted: 04/21/2012] [Indexed: 11/26/2022]
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45
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Feng J, Zhan XB, Wang D, Zhang LM, Lin CC. An unstructured kinetic model to study NaCl effect on volatile ester fermentation by Candida etchellsii for soy sauce production. BIOTECHNOL BIOPROC E 2012. [DOI: 10.1007/s12257-011-0416-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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46
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Zhao Z, Shao X, Huang B, Liu F. On-line estimation of glucose and biomass concentration in batch fermentation process using particle filter with constraint. ASIA-PAC J CHEM ENG 2012. [DOI: 10.1002/apj.1626] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Zhonggai Zhao
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education); Jiangnan University; Wuxi; China; 214122
| | - Xinguang Shao
- Department of Chemical and Materials Engineering; University of Alberta; Edmonton; AB; Canada; T6G 2 G6
| | - Biao Huang
- Department of Chemical and Materials Engineering; University of Alberta; Edmonton; AB; Canada; T6G 2 G6
| | - Fei Liu
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education); Jiangnan University; Wuxi; China; 214122
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47
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Enzymatic synthesis of ampicillin: nonlinear modeling, kinetics estimation, and adaptive control. J Biomed Biotechnol 2012; 2012:512691. [PMID: 22523470 PMCID: PMC3306788 DOI: 10.1155/2012/512691] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2011] [Accepted: 12/05/2011] [Indexed: 11/17/2022] Open
Abstract
Nowadays, the use of advanced control strategies in biotechnology is quite low. A main reason is the lack of quality of the data, and the fact that more sophisticated control strategies must be based on a model of the dynamics of bioprocesses. The nonlinearity of the bioprocesses and the absence of cheap and reliable instrumentation require an enhanced modeling effort and identification strategies for the kinetics. The present work approaches modeling and control strategies for the enzymatic synthesis of ampicillin that is carried out inside a fed-batch bioreactor. First, a nonlinear dynamical model of this bioprocess is obtained by using a novel modeling procedure for biotechnology: the bond graph methodology. Second, a high gain observer is designed for the estimation of the imprecisely known kinetics of the synthesis process. Third, by combining an exact linearizing control law with the on-line estimation kinetics algorithm, a nonlinear adaptive control law is designed. The case study discussed shows that a nonlinear feedback control strategy applied to the ampicillin synthesis bioprocess can cope with disturbances, noisy measurements, and parametric uncertainties. Numerical simulations performed with MATLAB environment are included in order to test the behavior and the performances of the proposed estimation and control strategies.
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48
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Sonnleitner B. Automated measurement and monitoring of bioprocesses: key elements of the M(3)C strategy. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2012. [PMID: 23179291 DOI: 10.1007/10_2012_173] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
The state-of-routine monitoring items established in the bioprocess industry as well as some important state-of-the-art methods are briefly described and the potential pitfalls discussed. Among those are physical and chemical variables such as temperature, pressure, weight, volume, mass and volumetric flow rates, pH, redox potential, gas partial pressures in the liquid and molar fractions in the gas phase, infrared spectral analysis of the liquid phase, and calorimetry over an entire reactor. Classical as well as new optical versions are addressed. Biomass and bio-activity monitoring (as opposed to "measurement") via turbidity, permittivity, in situ microscopy, and fluorescence are critically analyzed. Some new(er) instrumental analytical tools, interfaced to bioprocesses, are explained. Among those are chromatographic methods, mass spectrometry, flow and sequential injection analyses, field flow fractionation, capillary electrophoresis, and flow cytometry. This chapter surveys the principles of monitoring rather than compiling instruments.
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Affiliation(s)
- Bernhard Sonnleitner
- Institute for Chemistry and Biological Chemistry (ICBC), Zurich University of Applied Sciences (ZHAW), Einsiedlerstrasse 29, CH-8820, Waedenswil, Switzerland,
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49
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Palmer SM, Kunji ERS. Online analysis and process control in recombinant protein production (review). Methods Mol Biol 2012; 866:129-155. [PMID: 22454120 DOI: 10.1007/978-1-61779-770-5_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Online analysis and control is essential for efficient and reproducible bioprocesses. A key factor in real-time control is the ability to measure critical variables rapidly. Online in situ measurements are the preferred option and minimize the potential loss of sterility. The challenge is to provide sensors with a good lifespan that withstand harsh bioprocess conditions, remain stable for the duration of a process without the need for recalibration, and offer a suitable working range. In recent decades, many new techniques that promise to extend the possibilities of analysis and control, not only by providing new parameters for analysis, but also through the improvement of accepted, well practiced, measurements have arisen.
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Affiliation(s)
- Shane M Palmer
- Mitochondrial Biology Unit, The Medical Research Council, Cambridge, UK
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50
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Behr L, Joeris K, Burnett M, Scheper T. A novel in situ probe for oxygen uptake rate measurement in mammalian cell cultures. Biotechnol Prog 2011; 28:581-6. [DOI: 10.1002/btpr.741] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2011] [Revised: 08/29/2011] [Indexed: 11/10/2022]
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