1
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Allampalli SSP, Sivaprakasam S. Unveiling the potential of specific growth rate control in fed-batch fermentation: bridging the gap between product quantity and quality. World J Microbiol Biotechnol 2024; 40:196. [PMID: 38722368 DOI: 10.1007/s11274-024-03993-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024]
Abstract
During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing.
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Affiliation(s)
- Satya Sai Pavan Allampalli
- BioPAT Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam, 781039, India
| | - Senthilkumar Sivaprakasam
- BioPAT Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam, 781039, India.
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2
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Iglesias CF, Bolic M. How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models. SENSORS (BASEL, SWITZERLAND) 2024; 24:653. [PMID: 38276345 PMCID: PMC11154378 DOI: 10.3390/s24020653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/22/2023] [Accepted: 01/06/2024] [Indexed: 01/27/2024]
Abstract
The unstructured mechanistic model (UMM) allows for modeling the macro-scale of a phenomenon without known mechanisms. This is extremely useful in biomanufacturing because using the UMM for the joint estimation of states and parameters with an extended Kalman filter (JEKF) can enable the real-time monitoring of bioprocesses with unknown mechanisms. However, the UMM commonly used in biomanufacturing contains ordinary differential equations (ODEs) with unshared parameters, weak variables, and weak terms. When such a UMM is coupled with an initial state error covariance matrix P(t=0) and a process error covariance matrix Q with uncorrelated elements, along with just one measured state variable, the joint extended Kalman filter (JEKF) fails to estimate the unshared parameters and state simultaneously. This is because the Kalman gain corresponding to the unshared parameter remains constant and equal to zero. In this work, we formally describe this failure case, present the proof of JEKF failure, and propose an approach called SANTO to side-step this failure case. The SANTO approach consists of adding a quantity to the state error covariance between the measured state variable and unshared parameter in the initial P(t = 0) of the matrix Ricatti differential equation to compute the predicted error covariance matrix of the state and prevent the Kalman gain from being zero. Our empirical evaluations using synthetic and real datasets reveal significant improvements: SANTO achieved a reduction in root-mean-square percentage error (RMSPE) of up to approximately 17% compared to the classical JEKF, indicating a substantial enhancement in estimation accuracy.
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Affiliation(s)
- Cristovão Freitas Iglesias
- School of Electrical Engineering and Computer Science (EECS), University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Miodrag Bolic
- School of Electrical Engineering and Computer Science (EECS), University of Ottawa, Ottawa, ON K1N 6N5, Canada
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3
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Sharma S, Mahadevan J, Giri L, Mitra K. Identification of optimal flow rate for culture media, cell density, and oxygen toward maximization of virus production in a fed-batch baculovirus-insect cell system. Biotechnol Bioeng 2023; 120:3529-3542. [PMID: 37749905 DOI: 10.1002/bit.28558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023]
Abstract
In recent times, it has been realized that novel vaccines are required to combat emerging disease outbreaks, and faster optimization is required to respond to global vaccine demands. Although, fed-batch operations offer better productivity, experiment-based optimization of a new fed-batch process remains expensive and time-consuming. In this context, we propose a novel computational framework that can be used for process optimization and control of a fed-batch baculovirus-insect cell system. Since the baculovirus expression vector system (BEVS) is known to be widely used platforms for recombinant protein/vaccine production, we chose this system to demonstrate the identification of optimal profile. Toward this, first, we constructed a mathematical model that captures the time course of cell and virus growth in a baculovirus-insect cell system. Second, the proposed model was used for numerical analysis to determine the optimal operating profiles of control variables such as culture media, cell density, and oxygen based on a multiobjective optimal control formulation. Third, a detailed comparison between batch and fed-batch culture was perfromed along with a comparison between various alternatives of fed-batch operation. Finally, we demonstrate that a model-based quantification of controlled feed addition in fed-batch culture is capable of providing better productivity as compared to a batch culture. The proposed framework can be utilized for the estimation of optimal operating regions of different control variables to achieve maximum infected cell density and virus yield while minimizing the substrate/media, uninfected cell, and oxygen consumption.
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Affiliation(s)
- Surbhi Sharma
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, Telangana, India
| | - Jagadeesh Mahadevan
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, Telangana, India
| | - Lopamudra Giri
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, Telangana, India
| | - Kishalay Mitra
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, Telangana, India
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4
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Sørensen HM, Cunningham D, Balakrishnan R, Maye S, MacLeod G, Brabazon D, Loscher C, Freeland B. Steps toward a digital twin for functional food production with increased health benefits. Curr Res Food Sci 2023; 7:100593. [PMID: 37790857 PMCID: PMC10543970 DOI: 10.1016/j.crfs.2023.100593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 10/05/2023] Open
Abstract
Lactobacillus rhamnosus (L. rhamnosus) is a commensal bacterium with health-promoting properties and with a wide range of applications within the food industry. To improve and optimize the control of L. rhamnosus biomass production in batch and fed-batch bioprocesses, this study proposes the application of artificial neural network (ANN) modelling to improve process control and monitoring, with potential future implementation as a basis for a digital twin. Three ANNs were developed using historical data from ten bioprocesses. These ANNs were designed to predict the biomass in batch bioprocesses with different media compositions, predict biomass in fed-batch bioprocesses, and predict the growth rate in fed-batch bioprocesses. The immunomodulatory effect of the L. rhamnosus samples was examined and found to elicit an anti-inflammatory response as evidenced by the inhibition of IL-6 and TNF-α secretion. Overall, the findings of this study reinforce the potential of ANN modelling for bioprocess optimization aimed at improved control for maximising the volumetric productivity of L. rhamnosus as an immunomodulatory agent with applications in the functional food industry.
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Affiliation(s)
- Helena Mylise Sørensen
- School of Biotechnology, Dublin City University, D9 Dublin, Ireland
- I-Form, Advanced Manufacturing Research Centre, Dublin City University, D9 Dublin, Ireland
| | - David Cunningham
- School of Biotechnology, Dublin City University, D9 Dublin, Ireland
| | | | - Susan Maye
- Dairygold Co-Operative Society Limited, Clonmel Road, Co. Cork, P67 DD36, Mitchelstown, Ireland
| | - George MacLeod
- Dairygold Co-Operative Society Limited, Clonmel Road, Co. Cork, P67 DD36, Mitchelstown, Ireland
| | - Dermot Brabazon
- I-Form, Advanced Manufacturing Research Centre, Dublin City University, D9 Dublin, Ireland
| | | | - Brian Freeland
- School of Biotechnology, Dublin City University, D9 Dublin, Ireland
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5
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Zhang W, Zhang C, Cao L, Liang F, Xie W, Tao L, Chen C, Yang M, Zhong L. Application of digital-intelligence technology in the processing of Chinese materia medica. Front Pharmacol 2023; 14:1208055. [PMID: 37693890 PMCID: PMC10484343 DOI: 10.3389/fphar.2023.1208055] [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: 04/18/2023] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
Processing of Chinese Materia Medica (PCMM) is the concentrated embodiment, which is the core of Chinese unique traditional pharmaceutical technology. The processing includes the preparation steps such as cleansing, cutting and stir-frying, to make certain impacts on the quality and efficacy of Chinese botanical drugs. The rapid development of new computer digital technologies, such as big data analysis, Internet of Things (IoT), blockchain and cloud computing artificial intelligence, has promoted the rapid development of traditional pharmaceutical manufacturing industry with digitalization and intellectualization. In this review, the application of digital intelligence technology in the PCMM was analyzed and discussed, which hopefully promoted the standardization of the process and secured the quality of botanical drugs decoction pieces. Through the intellectualization and the digitization of production, safety and effectiveness of clinical use of traditional Chinese medicine (TCM) decoction pieces were ensured. This review also provided a theoretical basis for further technical upgrading and high-quality development of TCM industry.
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Affiliation(s)
- Wanlong Zhang
- College of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
| | - Changhua Zhang
- College of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
- Nanchang Research Institute, Sun Yat-sen University, Nanchang, Jiangxi, China
| | - Lan Cao
- College of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
| | - Fang Liang
- College of Physical Culture, Yuzhang Normal University, Nanchang, Jiangxi, China
| | - Weihua Xie
- College of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
| | - Liang Tao
- Nanchang Research Institute, Sun Yat-sen University, Nanchang, Jiangxi, China
| | - Chen Chen
- School of Biomedical Sciences, University of Queensland, Brisbane, QLD, Australia
| | - Ming Yang
- Key Laboratory of Modern Chinese Medicine Preparation of Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
| | - Lingyun Zhong
- College of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
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6
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Zhang W, Zhang C, Cao L, Liang F, Xie W, Tao L, Chen C, Yang M, Zhong L. Application of digital-intelligence technology in the processing of Chinese materia medica. Front Pharmacol 2023; 14. [DOI: https:/doi.org/10.3389/fphar.2023.1208055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2024] Open
Abstract
Processing of Chinese Materia Medica (PCMM) is the concentrated embodiment, which is the core of Chinese unique traditional pharmaceutical technology. The processing includes the preparation steps such as cleansing, cutting and stir-frying, to make certain impacts on the quality and efficacy of Chinese botanical drugs. The rapid development of new computer digital technologies, such as big data analysis, Internet of Things (IoT), blockchain and cloud computing artificial intelligence, has promoted the rapid development of traditional pharmaceutical manufacturing industry with digitalization and intellectualization. In this review, the application of digital intelligence technology in the PCMM was analyzed and discussed, which hopefully promoted the standardization of the process and secured the quality of botanical drugs decoction pieces. Through the intellectualization and the digitization of production, safety and effectiveness of clinical use of traditional Chinese medicine (TCM) decoction pieces were ensured. This review also provided a theoretical basis for further technical upgrading and high-quality development of TCM industry.
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7
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Huang XY, Ao TJ, Zhang X, Li K, Zhao XQ, Champreda V, Runguphan W, Sakdaronnarong C, Liu CG, Bai FW. Developing high-dimensional machine learning models to improve generalization ability and overcome data insufficiency for mixed sugar fermentation simulation. BIORESOURCE TECHNOLOGY 2023:129375. [PMID: 37352987 DOI: 10.1016/j.biortech.2023.129375] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 06/25/2023]
Abstract
Biorefinery can be promoted by building accurate machine learning models. This work proposed a strategy to enhance model's generalization ability and overcome insufficient data conditions for mixed sugar fermentation simulation. Multiple inputs single output models, using initial glucose, initial xylose, and time together as inputs, have higher generalization ability than single input single output models with time as sole input in predicting glucose, xylose, ethanol, or biomass separately. Multiple inputs multiple outputs models, integrating outputs, enhanced model accuracy and resulted in an average R2 at 0.99. To overcome data insufficiency conditions, consensus yeast (CY) model, through consolidating data from 4 yeasts, obtained R2 at 0.90. By adjusting the pretrained CY model, the model can save more than 50% data and get R2 at 0.95 and 0.93 for yeast and bacterial fermentation simulation. The strategy can expand the application range and save costs of data curation for ANN models.
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Affiliation(s)
- Xiao-Yan Huang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Tian-Jie Ao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xue Zhang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Kai Li
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xin-Qing Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Verawat Champreda
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA) 113 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Weerawat Runguphan
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA) 113 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Chularat Sakdaronnarong
- Department of Chemical Engineering, Faculty of Engineering, Mahidol University, 25/25, Putthamonthon 4 Road, Salaya, Putthamonthon, Nakhon Pathom 73170 Thailand
| | - Chen-Guang Liu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Feng-Wu Bai
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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8
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Nikita S, Mishra S, Gupta K, Runkana V, Gomes J, Rathore AS. Advances in bioreactor control for production of biotherapeutic products. Biotechnol Bioeng 2023; 120:1189-1214. [PMID: 36760086 DOI: 10.1002/bit.28346] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/08/2023] [Accepted: 02/08/2023] [Indexed: 02/11/2023]
Abstract
Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics. Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.
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Affiliation(s)
- Saxena Nikita
- Department of Chemical Engineering, DBT Centre of Excellence for Biopharmaceutical Technology, Indian Institute of Technology, Hauz Khas, Delhi, India
| | - Somesh Mishra
- Department of Chemical Engineering, DBT Centre of Excellence for Biopharmaceutical Technology, Indian Institute of Technology, Hauz Khas, Delhi, India
| | - Keshari Gupta
- TCS Research, Tata Consultancy Services Limited, Pune, India
| | | | - James Gomes
- Kusuma School of Biological Sciences, Indian Institute of Technology, Hauz Khas, Delhi, India
| | - Anurag S Rathore
- Department of Chemical Engineering, DBT Centre of Excellence for Biopharmaceutical Technology, Indian Institute of Technology, Hauz Khas, Delhi, India
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9
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Bayer B, Duerkop M, Pörtner R, Möller J. Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements, pitfalls and solution paths. Biotechnol J 2023; 18:e2200381. [PMID: 36382343 DOI: 10.1002/biot.202200381] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/17/2022]
Abstract
Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four-dimensional antibody-producing CHO fed-batch process are compared. Overall, 33 experiments are performed in the fractional factorial four-dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling.
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Affiliation(s)
| | | | - Ralf Pörtner
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany
| | - Johannes Möller
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany
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10
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Caño De Las Heras S, Gargalo CL, Caccavale F, Gernaey KV, Krühne U. NyctiDB: A non-relational bioprocesses modeling database supported by an ontology. FRONTIERS IN CHEMICAL ENGINEERING 2022. [DOI: 10.3389/fceng.2022.1036867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Strategies to exploit and enable the digitalization of industrial processes are on course to become game-changers in optimizing (bio)chemical facilities. To achieve this, these industries face an increasing need for process models and, as importantly, an efficient way to store the models and data/information. Therefore, this work proposes developing an online information storage system that can facilitate the reuse and expansion of process models and make them available to the digitalization cycle. This system is named NyctiDB, and it is a novel non-relational database coupled with a bioprocess ontology. The ontology supports the selection and classification of bioprocess models focused information, while the database is in charge of the online storage of said information. Through a series of online collections, NyctiDB contains essential knowledge for the design, monitoring, control, and optimization of a bioprocess based on its mathematical model. Once NyctiDB has been implemented, its applicability and usefulness are demonstrated through two applications. Application A shows how NyctiDB is integrated inside the software architecture of an online educational bioprocess simulator. This implies that NyctiDB provides the information for the visualization of different bioprocess behaviours and the modifications of the models in the software. Moreover, the information related to the parameters and conditions of each model is used to support the users’ understanding of the process. Additionally, application B illustrates that NyctiDB can be used as AI enabler to further the research in this field through open-source and reliable data. This can, in fact, be used as the information source for the AI frameworks when developing, for example, hybrid models or smart expert systems for bioprocesses. Henceforth, this work aims to provide a blueprint on how to collect bioprocess modeling information and connect it to facilitate and empower the Internet-of-Things paradigm and the digitalization of the biomanufacturing industries.
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11
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Opportunities and challenges for model utilization in the biopharmaceutical industry: current versus future state. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100813] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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12
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Shohan S, Zeng Y, Chen X, Jin R, Shirwaiker R. Investigating dielectric spectroscopy and soft sensing for nondestructive quality assessment of engineered tissues. Biosens Bioelectron 2022; 216:114286. [DOI: 10.1016/j.bios.2022.114286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/29/2022] [Accepted: 04/11/2022] [Indexed: 11/02/2022]
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13
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Siegl M, Brunner V, Geier D, Becker T. Ensemble‐based adaptive soft sensor for fault‐tolerant biomass monitoring. Eng Life Sci 2022; 22:229-241. [PMID: 35382536 PMCID: PMC8961066 DOI: 10.1002/elsc.202100091] [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: 07/21/2021] [Revised: 10/26/2021] [Accepted: 11/29/2021] [Indexed: 11/08/2022] Open
Abstract
The accuracy and precision of soft sensors depend strongly on the reliability of underlying model inputs. These inputs (particularly readings of hardware sensors) are frequently subject to faults. This study aims to develop an adaptive soft sensor capable of reliable and robust biomass concentration predictions in the presence of faulty model inputs for a Pichia pastoris bioprocess. Hence, three soft sensor submodels were developed based on three independent model inputs (base addition, CO2 production, and mid‐infrared spectrum). An ensemble‐based algorithm combined the submodels to form an ensemble model, that is, an adaptive soft sensor, to achieve fault‐tolerant prediction. The algorithm's basic steps are as follows: the initial determination of submodel reliability is followed by selecting appropriate submodels to generate a reliable prediction via variance‐based weighting of the submodels. The adaptive soft sensor demonstrated high robustness and accuracy in biomass prediction in the presence of multiple simulated sensor faults (RMSE = 0.43 g L−1) and multiple real sensor faults (RMSE = 0.70 g L−1).
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Affiliation(s)
- Manuel Siegl
- Chair of Brewing and Beverage Technology Technical University of Munich Freising Germany
| | - Vincent Brunner
- 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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14
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Espinel‐Ríos S, Bettenbrock K, Klamt S, Findeisen R. Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover. AIChE J 2022. [DOI: 10.1002/aic.17555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Sebastián Espinel‐Ríos
- Laboratory for Systems Theory and Automatic Control Otto von Guericke University Magdeburg Germany
- Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany
| | - Katja Bettenbrock
- Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany
| | - Steffen Klamt
- Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany
- Technische Universität Darmstadt Darmstadt Germany
| | - Rolf Findeisen
- Laboratory for Systems Theory and Automatic Control Otto von Guericke University Magdeburg Germany
- Control and Cyber‐Physical Systems Laboratory Technical University of Darmstadt Darmstadt Germany
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15
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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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16
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Kelani RO, Ahmad Z, Patle D. Mechanistic model-based control of biodiesel production processes: a review of needs and scopes. CHEM ENG COMMUN 2021. [DOI: 10.1080/00986445.2021.2012463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Rasheed O. Kelani
- School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia
| | - Zainal Ahmad
- School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia
| | - Dipesh Patle
- Chemical Engineering Department, MNNIT Allahabad, Uttar Pradesh, India
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17
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Bähner FD, Prado-Rubio OA, Huusom JK. Challenges in Optimization and Control of Biobased Process Systems: An Industrial-Academic Perspective. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c01792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Franz D. Bähner
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, 2800 Sealand, Denmark
| | - Oscar A. Prado-Rubio
- Facultad de Ingeniería y Arquitectura, Departamento de Ingeniería Química, Universidad Nacional de Colombia, Sede Manizales, 170001, Manizales, Colombia
| | - Jakob K. Huusom
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, 2800 Sealand, Denmark
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18
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Carranza-Saavedra D, Sánchez Henao CP, Zapata Montoya JE. Kinetic analysis and modeling of L-valine production in fermentation batch from E. coli using glucose, lactose and whey as carbon sources. BIOTECHNOLOGY REPORTS (AMSTERDAM, NETHERLANDS) 2021; 31:e00642. [PMID: 34150530 PMCID: PMC8193114 DOI: 10.1016/j.btre.2021.e00642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 06/02/2021] [Accepted: 06/03/2021] [Indexed: 11/18/2022]
Abstract
In this study the effect of the carbon source on L-valine production kinetics using genetically modified E. coli was researched. Glucose, lactose, Whey (W) and deproteinized whey (DW) were tested as carbon sources, keeping the carbon/nitrogen (C/N) ratio constant. Biomass generation and substrate consumption were modeled with Contois and Mass Conservation models, respectively, whereas Mass Conservation Balance and Luedeking-Piret models were used for product obtaining. Results showed that L-valine production is partially associated to growth, with values of 0.485 g L-valine/(g dry cell weight.h), and a product loss effect at a specific rate (β) of 0.019 g L-valine/(g dry cell weight.h) with W. The yield of this product increased 36 % using W concerning glucose or lactose as carbon sources. On the other hand, Mass Balance and Luedeking-Piret models adjust properly to experimental data (R2 >0.90). In conclusion whey is a promising substrate for obtaining L-valine using genetically-modified E. coli.
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Affiliation(s)
- Darwin Carranza-Saavedra
- Grupo Nutrición y Tecnología de Alimentos, Universidad de Antioquia, Medellín 050010, Colombia
- Departamento de Producción y Sanidad Vegetal, Facultad de Ingeniería Agronómica, Universidad Del Tolima, Ibagué 730006299, Colombia
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19
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Mowbray M, Savage T, Wu C, Song Z, Cho BA, Del Rio-Chanona EA, Zhang D. Machine learning for biochemical engineering: A review. Biochem Eng J 2021. [DOI: 10.1016/j.bej.2021.108054] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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20
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Rathore AS, Mishra S, Nikita S, Priyanka P. Bioprocess Control: Current Progress and Future Perspectives. Life (Basel) 2021; 11:life11060557. [PMID: 34199245 PMCID: PMC8231968 DOI: 10.3390/life11060557] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 02/07/2023] Open
Abstract
Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.
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21
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Abstract
The monitoring of the main variables and parameters of biotechnological processes is of key importance for the research and control of the processes, especially in industrial installations, where there is a limited number of measurements. For this reason, many researchers are focusing their efforts on developing appropriate algorithms (software sensors (SS)) to provide reliable information on unmeasurable variables and parameters, based on the available on-line information. In the literature, a large number of developments related to this topic that concern data-based and model-based sensors are presented. Up-to-date reviews of data-driven SS for biotechnological processes have already been presented in the scientific literature. Hybrid software sensors as a combination between the abovementioned ones are under development. This gives a reason for the article to be focused on a review of model-based software sensors for biotechnological processes. The most applied model-based methods for monitoring the kinetics and state variables of these processes are analyzed and compared. The following software sensors are considered: Kalman filters, methods based on estimators and observers of a deterministic type, probability observers, high-gain observers, sliding mode observers, adaptive observers, etc. The comparison is made in terms of their stability and number of tuning parameters. Particular attention is paid to the approach of the general dynamic model. The main characteristics of the classic variant proposed by D. Dochain are summarized. Results related to the development of this approach are analyzed. A key point is the presentation of new formalizations of kinetics and the design of new algorithms for its estimation in cases of uncertainty. The efficiency and applicability of the considered software sensors are discussed.
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22
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Rathore AS, Nikita S, Thakur G, Deore N. Challenges in process control for continuous processing for production of monoclonal antibody products. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100671] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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23
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Digital Twin in biomanufacturing: challenges and opportunities towards its implementation. ACTA ACUST UNITED AC 2021. [DOI: 10.1007/s43393-021-00024-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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24
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Abstract
Process models, consisting of transferable and applicable knowledge, can be used for various tasks such as process development and optimization, and for predicting and controlling critical process variables. In this regard, mechanistic process models, describing the mechanism of a system with a distinct model structure and characteristic parameters, are very promising.The development of a reliable and applicable model is usually the critical step, before model simulation and application show beneficial effects. In this chapter, a workflow for the generation of mechanistic process models is presented and applied on a typical cell culture process. The workflow includes the definition of critical reactions and the identification of their kinetics. By an iterative approach different reactions and kinetics are tested and model quality is assessed, leading to a final, target-oriented model.
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25
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Kastenhofer J, Rajamanickam V, Libiseller-Egger J, Spadiut O. Monitoring and control of E. coli cell integrity. J Biotechnol 2021; 329:1-12. [PMID: 33485861 DOI: 10.1016/j.jbiotec.2021.01.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/06/2021] [Accepted: 01/08/2021] [Indexed: 12/15/2022]
Abstract
Soluble expression of recombinant proteins in E. coli is often done by translocation of the product across the inner membrane (IM) into the periplasm, where it is retained by the outer membrane (OM). While the integrity of the IM is strongly coupled to viability and impurity release, a decrease in OM integrity (corresponding to increased "leakiness") leads to accumulation of product in the extracellular space, strongly impacting the downstream process. Whether leakiness is desired or not, differential monitoring and control of IM and OM integrity are necessary for an efficient E. coli bioprocess in compliance with the guidelines of Quality by Design and Process Analytical Technology. In this review, we give an overview of relevant monitoring tools, summarize the research on factors affecting E. coli membrane integrity and provide a brief discussion on how the available monitoring technology can be implemented in real-time control of E. coli cultivations.
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Affiliation(s)
- Jens Kastenhofer
- TU Wien, Institute of Chemical, Environmental and Bioscience Engineering, Research Division Biochemical Engineering, Research Group Integrated Bioprocess Development, Gumpendorfer Strasse 1a, 1060, Vienna, Austria
| | - Vignesh Rajamanickam
- TU Wien, Institute of Chemical, Environmental and Bioscience Engineering, Research Division Biochemical Engineering, Research Group Integrated Bioprocess Development, Gumpendorfer Strasse 1a, 1060, Vienna, Austria
| | - Julian Libiseller-Egger
- TU Wien, Institute of Chemical, Environmental and Bioscience Engineering, Research Division Biochemical Engineering, Research Group Integrated Bioprocess Development, Gumpendorfer Strasse 1a, 1060, Vienna, Austria
| | - Oliver Spadiut
- TU Wien, Institute of Chemical, Environmental and Bioscience Engineering, Research Division Biochemical Engineering, Research Group Integrated Bioprocess Development, Gumpendorfer Strasse 1a, 1060, Vienna, Austria.
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26
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Using Neural Networks to Obtain Indirect Information about the State Variables in an Alcoholic Fermentation Process. Processes (Basel) 2020. [DOI: 10.3390/pr9010074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg–Marquardt). The simulation results show that the feedback neural network outperformed the feed-forward neural network. The NN configuration is relatively flexible (with hidden layers and a number of nodes on each of them), but the number of input and output nodes depends on the fermentation process parameters. After laborious simulations, we determined that using pH and CO2 as inputs reduces the prediction errors of the NN. Thus, besides the most commonly used process parameters like fermentation temperature, time, the initial concentration of the substrate, the substrate concentration, and the biomass concentration, by adding pH and CO2, we obtained the optimum number of input nodes for the network. The optimal configuration in our case was obtained after 1500 iterations using a NN with one hidden layer and 12 neurons on it, seven neurons on the input layer, and one neuron as the output. If properly trained and validated, this model can be used in future research to accurately predict steady-state and dynamic alcoholic fermentation process behaviour and thereby improve process control performance.
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27
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Gargalo CL, de Las Heras SC, Jones MN, Udugama I, Mansouri SS, Krühne U, Gernaey KV. Towards the Development of Digital Twins for the Bio-manufacturing Industry. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2020; 176:1-34. [PMID: 33349908 DOI: 10.1007/10_2020_142] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The bio-manufacturing industry, along with other process industries, now has the opportunity to be engaged in the latest industrial revolution, also known as Industry 4.0. To successfully accomplish this, a physical-to-digital-to-physical information loop should be carefully developed. One way to achieve this is, for example, through the implementation of digital twins (DTs), which are virtual copies of the processes. Therefore, in this paper, the focus is on understanding the needs and challenges faced by the bio-manufacturing industry when dealing with this digitalized paradigm. To do so, two major building blocks of a DT, data and models, are highlighted and discussed. Hence, firstly, data and their characteristics and collection strategies are examined as well as new methods and tools for data processing. Secondly, modelling approaches and their potential of being used in DTs are reviewed. Finally, we share our vision with regard to the use of DTs in the bio-manufacturing industry aiming at bringing the DT a step closer to its full potential and realization.
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Affiliation(s)
- Carina L Gargalo
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
| | | | - Mark Nicholas Jones
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark.,Molecular Quantum Solutions ApS, Copenhagen, Denmark
| | - Isuru Udugama
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Seyed Soheil Mansouri
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Ulrich Krühne
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Krist V Gernaey
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark.
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28
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Swaminathan N, Priyanka P, Rathore AS, Sivaprakasam S, Subbiah S. Multiobjective Optimization for Enhanced Production of Therapeutic Proteins in Escherichia coli: Application of Real-Time Dielectric Spectroscopy. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c04010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Nivedhitha Swaminathan
- Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati, Assam 781039, India
| | - Priyanka Priyanka
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Anurag S. Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Senthilkumar Sivaprakasam
- Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati, Assam 781039, India
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, Assam 781039, India
| | - Senthilmurugan Subbiah
- Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati, Assam 781039, India
- Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam 781039, India
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29
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Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes. J Ind Microbiol Biotechnol 2020; 47:947-964. [PMID: 32895764 PMCID: PMC7695667 DOI: 10.1007/s10295-020-02308-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 08/31/2020] [Indexed: 12/22/2022]
Abstract
The biomanufacturing industry has now the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Technology creates capabilities that enable smart manufacturing while still complying with unfolding regulations. However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data. Therefore, in this work, we both summarize the advances that have been made to date with regards to the monitoring and control of bioprocesses, and highlight some of the key technologies that have the potential to contribute to gathering big data. Empowering the current biomanufacturing industry to transition to Industry 4.0 operations allows for improved productivity through information-driven automation, not only by developing infrastructure, but also by introducing more advanced monitoring and control strategies.
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30
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Bayer B, Striedner G, Duerkop M. Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization. Biotechnol J 2020; 15:e2000121. [DOI: 10.1002/biot.202000121] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/11/2020] [Indexed: 02/06/2023]
Affiliation(s)
- Benjamin Bayer
- Department of Biotechnology University of Natural Resources and Life Sciences Vienna 1190 Austria
| | - Gerald Striedner
- Department of Biotechnology University of Natural Resources and Life Sciences Vienna 1190 Austria
| | - Mark Duerkop
- Department of Biotechnology University of Natural Resources and Life Sciences Vienna 1190 Austria
- Novasign GmbH Vienna 1190 Austria
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31
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Duan Z, Wilms T, Neubauer P, Kravaris C, Cruz Bournazou MN. Model reduction of aerobic bioprocess models for efficient simulation. Chem Eng Sci 2020. [DOI: 10.1016/j.ces.2020.115512] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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32
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Kager J, Tuveri A, Ulonska S, Kroll P, Herwig C. Experimental verification and comparison of model predictive, PID and model inversion control in a Penicillium chrysogenum fed-batch process. Process Biochem 2020. [DOI: 10.1016/j.procbio.2019.11.023] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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33
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Wang G, Haringa C, Tang W, Noorman H, Chu J, Zhuang Y, Zhang S. Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses. Biotechnol Bioeng 2019; 117:844-867. [PMID: 31814101 DOI: 10.1002/bit.27243] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 11/28/2019] [Accepted: 11/30/2019] [Indexed: 12/13/2022]
Abstract
Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.
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Affiliation(s)
- Guan Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Cees Haringa
- Transport Phenomena, Chemical Engineering Department, Delft University of Technology, Delft, The Netherlands.,DSM Biotechnology Center, Delft, The Netherlands
| | - Wenjun Tang
- DSM Biotechnology Center, Delft, The Netherlands
| | - Henk Noorman
- DSM Biotechnology Center, Delft, The Netherlands.,Bioprocess Engineering, Department of Biotechnology, Delft University of Technology, Delft, The Netherlands
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Siliang Zhang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
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34
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Spann R, Gernaey KV, Sin G. A compartment model for risk-based monitoring of lactic acid bacteria cultivations. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.107293] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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35
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Output uncertainty of dynamic growth models: Effect of uncertain parameter estimates on model reliability. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.107247] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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36
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Steinwandter V, Borchert D, Herwig C. Data science tools and applications on the way to Pharma 4.0. Drug Discov Today 2019; 24:1795-1805. [DOI: 10.1016/j.drudis.2019.06.005] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 04/23/2019] [Accepted: 06/11/2019] [Indexed: 01/02/2023]
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37
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Marine psychrophile-derived cold-active polygalacturonase: Enhancement of productivity in Thalassospira frigidphilosprofundus S3BA12 by whole cell immobilization. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.01.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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38
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Guerra A, von Stosch M, Glassey J. Toward biotherapeutic product real-time quality monitoring. Crit Rev Biotechnol 2019; 39:289-305. [DOI: 10.1080/07388551.2018.1524362] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- André Guerra
- School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Moritz von Stosch
- School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jarka Glassey
- School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, United Kingdom
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39
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Sha S, Huang Z, Wang Z, Yoon S. Mechanistic modeling and applications for CHO cell culture development and production. Curr Opin Chem Eng 2018. [DOI: 10.1016/j.coche.2018.08.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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40
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Luna MF, Martínez EC. Optimal design of dynamic experiments in the development of cybernetic models for bioreactors. Chem Eng Res Des 2018. [DOI: 10.1016/j.cherd.2018.05.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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41
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42
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Montes FCC, Gernaey K, Sin G. Dynamic Plantwide Modeling, Uncertainty, and Sensitivity Analysis of a Pharmaceutical Upstream Synthesis: Ibuprofen Case Study. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b00465] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Frederico C. C. Montes
- PROSYS, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800 Kgs. Lyngby, Denmark
| | - Krist Gernaey
- PROSYS, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800 Kgs. Lyngby, Denmark
| | - Gürkan Sin
- PROSYS, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800 Kgs. Lyngby, Denmark
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