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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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Sun L, Li M, Liu B, Li R, Deng H, Zhu X, Zhu X, Tsang DCW. Machine learning for municipal sludge recycling by thermochemical conversion towards sustainability. BIORESOURCE TECHNOLOGY 2024; 394:130254. [PMID: 38151207 DOI: 10.1016/j.biortech.2023.130254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/09/2023] [Accepted: 12/23/2023] [Indexed: 12/29/2023]
Abstract
The sustainable disposal of high-moisture municipal sludge (MS) has received increasing attention. Thermochemical conversion technologies can be used to recycle MS into liquid/gas bio-fuel and value-added solid products. In this review, we compared energy recovery potential of common thermochemical technologies (i.e., incineration, pyrolysis, hydrothermal conversion) for MS disposal via statistical methods, which indicated that hydrothermal conversion had a great potential in achieving energy recovery from MS. The application of machine learning (ML) in MS recycling was discussed to decipher complex relationships among MS components, process parameters and physicochemical reactions. Comprehensive ML models should be developed considering successive reaction processes of thermochemical conversion in future studies. Furthermore, challenges and prospects were proposed to improve effectiveness of ML for energizing thermochemical conversion of MS regarding data collection and preprocessing, model optimization and interpretability. This review sheds light on mechanism exploration of MS thermochemical recycling by ML, and provide practical guidance for MS recycling.
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Affiliation(s)
- Lianpeng Sun
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
| | - Mingxuan Li
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Bingyou Liu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Ruohong Li
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
| | - Huanzhong Deng
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Xiefei Zhu
- School of Advanced Energy, Sun Yat-sen University, Shenzhen 518107, China
| | - Xinzhe Zhu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China.
| | - Daniel C W Tsang
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
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Sun M, Gao AX, Liu X, Yang Y, Ledesma-Amaro R, Bai Z. High-throughput process development from gene cloning to protein production. Microb Cell Fact 2023; 22:182. [PMID: 37715258 PMCID: PMC10503041 DOI: 10.1186/s12934-023-02184-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/19/2023] [Indexed: 09/17/2023] Open
Abstract
In the post-genomic era, the demand for faster and more efficient protein production has increased, both in public laboratories and industry. In addition, with the expansion of protein sequences in databases, the range of possible enzymes of interest for a given application is also increasing. Faced with peer competition, budgetary, and time constraints, companies and laboratories must find ways to develop a robust manufacturing process for recombinant protein production. In this review, we explore high-throughput technologies for recombinant protein expression and present a holistic high-throughput process development strategy that spans from genes to proteins. We discuss the challenges that come with this task, the limitations of previous studies, and future research directions.
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Affiliation(s)
- Manman Sun
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, 214112, China
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, UK
| | - Alex Xiong Gao
- Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Xiuxia Liu
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, 214112, China
- Key Laboratory of Industrial Biotechnology, School of Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China
- Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, 214122, China
| | - Yankun Yang
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, 214112, China
- Key Laboratory of Industrial Biotechnology, School of Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China
- Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, 214122, China
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, UK.
| | - Zhonghu Bai
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, 214112, China.
- Key Laboratory of Industrial Biotechnology, School of Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China.
- Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, 214122, China.
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