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Saha R, Chauhan A, Rastogi Verma S. Machine learning: an advancement in biochemical engineering. Biotechnol Lett 2024; 46:497-519. [PMID: 38902585 DOI: 10.1007/s10529-024-03499-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/24/2024] [Accepted: 05/18/2024] [Indexed: 06/22/2024]
Abstract
One of the most remarkable techniques recently introduced into the field of bioprocess engineering is machine learning. Bioprocess engineering has drawn much attention due to its vast application in different domains like biopharmaceuticals, fossil fuel alternatives, environmental remediation, and food and beverage industry, etc. However, due to their unpredictable mechanisms, they are very often challenging to optimize. Furthermore, biological systems are extremely complicated; hence, machine learning algorithms could potentially be utilized to improve and build new biotechnological processes. Gaining insight into the fundamental mathematical understanding of commonly used machine learning algorithms, including Support Vector Machine, Principal Component Analysis, Partial Least Squares and Reinforcement Learning, the present study aims to discuss various case studies related to the application of machine learning in bioprocess engineering. Recent advancements as well as challenges posed in this area along with their potential solutions are also presented.
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Affiliation(s)
- Ritika Saha
- Department of Biotechnology, Delhi Technological University, New Delhi, 110042, India
| | - Ashutosh Chauhan
- Department of Biotechnology, Delhi Technological University, New Delhi, 110042, India
| | - Smita Rastogi Verma
- Department of Biotechnology, Delhi Technological University, New Delhi, 110042, India.
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2
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Wang X, Mohsin A, Sun Y, Li C, Zhuang Y, Wang G. From Spatial-Temporal Multiscale Modeling to Application: Bridging the Valley of Death in Industrial Biotechnology. Bioengineering (Basel) 2023; 10:744. [PMID: 37370675 DOI: 10.3390/bioengineering10060744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.
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Affiliation(s)
- Xueting Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Ali Mohsin
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Yifei Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Chao Li
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Guan Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
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3
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Banner M, Alosert H, Spencer C, Cheeks M, Farid SS, Thomas M, Goldrick S. A decade in review: use of data analytics within the biopharmaceutical sector. Curr Opin Chem Eng 2021; 34:None. [PMID: 34926134 PMCID: PMC8665905 DOI: 10.1016/j.coche.2021.100758] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
There are large amounts of data generated within the biopharmaceutical sector. Traditionally, data analysis methods labelled as multivariate data analysis have been the standard statistical technique applied to interrogate these complex data sets. However, more recently there has been a surge in the utilisation of a broader set of machine learning algorithms to further exploit these data. In this article, the adoption of data analysis techniques within the biopharmaceutical sector is evaluated through a review of journal articles and patents published within the last ten years. The papers objectives are to identify the most dominant algorithms applied across different applications areas within the biopharmaceutical sector and to explore whether there is a trend between the size of the data set and the algorithm adopted.
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Affiliation(s)
- Matthew Banner
- Department of Biochemical Engineering, University College London, Gower Street, London WC1E 6BT, UK
| | - Haneen Alosert
- Department of Biochemical Engineering, University College London, Gower Street, London WC1E 6BT, UK
| | - Christopher Spencer
- Cell Culture Fermentation Sciences, Biopharmaceutical Development, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Matthew Cheeks
- Cell Culture Fermentation Sciences, Biopharmaceutical Development, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Suzanne S Farid
- Department of Biochemical Engineering, University College London, Gower Street, London WC1E 6BT, UK
| | - Michael Thomas
- Department of Biochemical Engineering, University College London, Gower Street, London WC1E 6BT, UK
- London Centre for Nanotechnology, University College London, Gordon Street, London WC1H 0AH, UK
| | - Stephen Goldrick
- Department of Biochemical Engineering, University College London, Gower Street, London WC1E 6BT, UK
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Esmaeili-Faraj SH, Vaferi B, Bolhasani A, Karamian S, Hosseini S, Rashedi R. Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants. Chem Eng Technol 2021. [DOI: 10.1002/ceat.202000442] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
| | - Behzad Vaferi
- Islamic Azad University Department of Advanced Calculations, Chemical, Petroleum, and Polymer Engineering Research Center, Shiraz Branch 7198774731 Shiraz Iran
| | - Akbar Bolhasani
- Jam Petrochemical Company Research and Development Center 1434853114 Bushehr Iran
| | - Soroush Karamian
- Jam Petrochemical Company Research and Development Center 1434853114 Bushehr Iran
| | - Shahin Hosseini
- Jam Petrochemical Company Research and Development Center 1434853114 Bushehr Iran
| | - Reza Rashedi
- Jam Petrochemical Company Research and Development Center 1434853114 Bushehr Iran
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Wang B, Yu M, Zhu X, Jiang Z. Soft-sensing method based on FDLS-SVM in marine alkaline protease fermentation process. Prep Biochem Biotechnol 2019; 49:783-789. [PMID: 31132010 DOI: 10.1080/10826068.2019.1615506] [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] [Indexed: 10/26/2022]
Abstract
To overcome the problem that soft-sensing model cannot be updated with the bioprocess changes, this article proposed a soft-sensing modeling method which combined fuzzy c-means clustering (FCM) algorithm with least squares support vector machine theory (LS-SVM). FCM is used for separating a whole training data set into several clusters with different centers, each subset is trained by LS-SVM and sub-models are developed to fit different hierarchical property of the process. The new sample data that bring new operation information is introduced in the model, and the fuzzy membership function of the sample to each clustering is first calculated by the FCM algorithm. Then, a corresponding LS-SVM sub-model of the clustering with the largest fuzzy membership function is used for performing dynamic learning so that the model can update online. The proposed method is applied to predict the key biological parameters in the marine alkaline protease MP process. The simulation result indicates that the soft-sensing modeling method increases the model's adaptive abilities in various operation conditions and can improve its generalization ability.
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Affiliation(s)
- Bo Wang
- a School of Electrical and Information Engineering, JiangSu University , Zhenjiang , China
| | - Meifang Yu
- a School of Electrical and Information Engineering, JiangSu University , Zhenjiang , China
| | - Xianglin Zhu
- a School of Electrical and Information Engineering, JiangSu University , Zhenjiang , China
| | - Zheyu Jiang
- b Wuxi Taihu Water Service Co., Ltd , Wuxi , China
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Voss JP, Mittelheuser NE, Lemke R, Luttmann R. Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods. Eng Life Sci 2017; 17:1281-1294. [PMID: 32624755 DOI: 10.1002/elsc.201600229] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 08/17/2017] [Accepted: 08/24/2017] [Indexed: 11/08/2022] Open
Abstract
This contribution includes an investigation of the applicability of Raman spectroscopy as a PAT analyzer in cyclic production processes of a potential Malaria vaccine with Pichia pastoris. In a feasibility study, Partial Least Squares Regression (PLSR) models were created off-line for cell density and concentrations of glycerol, methanol, ammonia and total secreted protein. Relative cross validation errors RMSEcvrel range from 2.87% (glycerol) to 11.0% (ammonia). In the following, on-line bioprocess monitoring was tested for cell density and glycerol concentration. By using the nonlinear Support Vector Regression (SVR) method instead of PLSR, the error RMSEPrel for cell density was reduced from 5.01 to 2.94%. The high potential of Raman spectroscopy in combination with multivariate calibration methods was demonstrated by the implementation of a closed loop control for glycerol concentration using PLSR. The strong nonlinear behavior of exponentially increasing control disturbances was met with a feed-forward control and adaptive correction of control parameters. In general the control procedure works very well for low cell densities. Unfortunately, PLSR models for glycerol concentration are strongly influenced by a correlation with the cell density. This leads to a failure in substrate prediction, which in turn prevents substrate control at cell densities above 16 g/L.
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Affiliation(s)
- Jan-Patrick Voss
- Research Center of Bioprocess Engineering and Analytical Techniques Department of Biotechnology Hamburg University of Applied Sciences Hamburg Germany
| | - Nina E Mittelheuser
- Research Center of Bioprocess Engineering and Analytical Techniques Department of Biotechnology Hamburg University of Applied Sciences Hamburg Germany
| | - Roman Lemke
- Research Center of Bioprocess Engineering and Analytical Techniques Department of Biotechnology Hamburg University of Applied Sciences Hamburg Germany
| | - Reiner Luttmann
- Research Center of Bioprocess Engineering and Analytical Techniques Department of Biotechnology Hamburg University of Applied Sciences Hamburg Germany
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Fatemi MH, Samghani K. Developing a Support Vector Machine Based QSPR Model for Prediction of Atmospheric Lifetime of Some Halocarbons. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2014. [DOI: 10.1246/bcsj.20140169] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
| | - Kobra Samghani
- Chemometrics Laboratory, Faculty of Chemistry, University of Mazandaran
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Wang T, Sun J, Zhang W, Yuan J. Prediction of product formation in 2-keto-l-gulonic acid fermentation through Bayesian combination of multiple neural networks. Process Biochem 2014. [DOI: 10.1016/j.procbio.2013.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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9
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Considerations in upstream bioprocess monitoring and statistical data analysis in the context of process analytical technology and quality by design. ACTA ACUST UNITED AC 2013. [DOI: 10.4155/pbp.13.16] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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10
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Liu Y, Gao Z, Li P, Wang H. Just-in-Time Kernel Learning with Adaptive Parameter Selection for Soft Sensor Modeling of Batch Processes. Ind Eng Chem Res 2012. [DOI: 10.1021/ie201650u] [Citation(s) in RCA: 84] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yi Liu
- Key Laboratory of Pharmaceutical
Engineering of Ministry of Education, Institute of Process Equipment
and Control Engineering, Zhejiang University of Technology, Hangzhou, 310032, People's Republic of China
| | - Zengliang Gao
- Key Laboratory of Pharmaceutical
Engineering of Ministry of Education, Institute of Process Equipment
and Control Engineering, Zhejiang University of Technology, Hangzhou, 310032, People's Republic of China
| | - Ping Li
- State Key Laboratory of Industrial
Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, 310027, People's Republic
of China
| | - Haiqing Wang
- College of Mechanical
and Electronic
Engineering, University of Petroleum (East China), West Changjiang Road, No. 66, Qingdao, 266555, People's Republic
of China
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Glassey J, Gernaey KV, Clemens C, Schulz TW, Oliveira R, Striedner G, Mandenius CF. Process analytical technology (PAT) for biopharmaceuticals. Biotechnol J 2011; 6:369-77. [DOI: 10.1002/biot.201000356] [Citation(s) in RCA: 151] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2010] [Revised: 02/07/2011] [Accepted: 02/14/2011] [Indexed: 01/04/2023]
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12
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Cui L, Xu Y, Jia Q, Wu H, Yuan J. Prediction of the Profit Function for Industrial 2-Keto-L-Gulonic Acid Cultivation. Chem Eng Technol 2011. [DOI: 10.1002/ceat.201000507] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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13
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Liu Y, Yang D, Wang H, Li P. Modeling of Fermentation Processes using Online Kernel Learning Algorithm. ACTA ACUST UNITED AC 2008. [DOI: 10.3182/20080706-5-kr-1001.01637] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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14
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LI Y, WANG Z, YUAN J. On-line Fault Detection Using SVM-based Dynamic MPLS for Batch Processes. Chin J Chem Eng 2006. [DOI: 10.1016/s1004-9541(07)60007-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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