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Tavakoli N, Fong EJ, Coleman A, Huang YK, Bigger M, Doche ME, Kim S, Lenz HJ, Graham NA, Macklin P, Finley SD, Mumenthaler SM. Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.595756. [PMID: 38826317 PMCID: PMC11142224 DOI: 10.1101/2024.05.24.595756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. This approach reveals the network-wide effects of metabolic perturbations. Our results highlighted hexokinase (HK) as the crucial target, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF-conditioned media exhibited increased sensitivity to HK inhibition, confirming the model predictions. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk.
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Affiliation(s)
- Niki Tavakoli
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Emma J. Fong
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Abigail Coleman
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Yu-Kai Huang
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Mathias Bigger
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | | | - Seungil Kim
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
| | - Heinz-Josef Lenz
- Division of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
| | - Nicholas A. Graham
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 46202, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Shannon M. Mumenthaler
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Ellison Institute of Technology, Los Angeles, CA, 90064, USA
- Division of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
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Hari A, Doddapaneni TRKC, Kikas T. Common operational issues and possible solutions for sustainable biosurfactant production from lignocellulosic feedstock. ENVIRONMENTAL RESEARCH 2024; 251:118665. [PMID: 38493851 DOI: 10.1016/j.envres.2024.118665] [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: 10/13/2023] [Revised: 02/06/2024] [Accepted: 03/07/2024] [Indexed: 03/19/2024]
Abstract
Surfactants are compounds with high surface activity and emulsifying property. These compounds find application in food, medical, pharmaceutical, and petroleum industries, as well as in agriculture, bioremediation, cleaning, cosmetics, and personal care product formulations. Due to their widespread use and environmental persistence, ensuring biodegradability and sustainability is necessary so as not to harm the environment. Biosurfactants, i.e., surfactants of plant or microbial origin produced from lignocellulosic feedstock, perform better than their petrochemically derived counterparts on the scale of net-carbon-negativity. Although many biosurfactants are commercially available, their high cost of production justifies their application only in expensive pharmaceuticals and cosmetics. Besides, the annual number of new biosurfactant compounds reported is less, compared to that of chemical surfactants. Multiple operational issues persist in the biosurfactant value chain. In this review, we have categorized some of these issues based on their relative position in the value chain - hurdles occurring during planning, upstream processes, production stage, and downstream processes - alongside plausible solutions. Moreover, we have presented the available paths forward for this industry in terms of process development and integrated pretreatment, combining conventional tried-and-tested strategies, such as reactor designing and statistical optimization with cutting-edge technologies including metabolic modeling and artificial intelligence. The development of techno-economically feasible biosurfactant production processes would be instrumental in the complete substitution of petrochemical surfactants, rather than mere supplementation.
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Affiliation(s)
- Anjana Hari
- Chair of Biosystems Engineering, Institute of Forestry and Engineering, Estonian University of Life Sciences, Kreutzwaldi 56, Tartu, 51014, Estonia.
| | - Tharaka Rama Krishna C Doddapaneni
- Chair of Biosystems Engineering, Institute of Forestry and Engineering, Estonian University of Life Sciences, Kreutzwaldi 56, Tartu, 51014, Estonia
| | - Timo Kikas
- Chair of Biosystems Engineering, Institute of Forestry and Engineering, Estonian University of Life Sciences, Kreutzwaldi 56, Tartu, 51014, Estonia
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Wang R, He Z, Chen H, Guo S, Zhang S, Wang K, Wang M, Ho SH. Enhancing biomass conversion to bioenergy with machine learning: Gains and problems. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172310. [PMID: 38599406 DOI: 10.1016/j.scitotenv.2024.172310] [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: 01/18/2024] [Revised: 03/20/2024] [Accepted: 04/06/2024] [Indexed: 04/12/2024]
Abstract
The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.
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Affiliation(s)
- Rupeng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Zixiang He
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Honglin Chen
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Silin Guo
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shiyu Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Ke Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Meng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shih-Hsin Ho
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
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4
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Qu G, Liu Y, Ma Q, Li J, Du G, Liu L, Lv X. Progress and Prospects of Natural Glycoside Sweetener Biosynthesis: A Review. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:15926-15941. [PMID: 37856872 DOI: 10.1021/acs.jafc.3c05074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
To achieve an adequate sense of sweetness with a healthy low-sugar diet, it is necessary to explore and produce sugar alternatives. Recently, glycoside sweeteners and their biosynthetic approaches have attracted the attention of researchers. In this review, we first outlined the synthetic pathways of glycoside sweeteners, including the key enzymes and rate-limiting steps. Next, we reviewed the progress in engineered microorganisms producing glycoside sweeteners, including de novo synthesis, whole-cell catalysis synthesis, and in vitro synthesis. The applications of metabolic engineering strategies, such as cofactor engineering and enzyme modification, in the optimization of glycoside sweetener biosynthesis were summarized. Finally, the prospects of combining enzyme engineering and machine learning strategies to enhance the production of glycoside sweeteners were discussed. This review provides a perspective on synthesizing glycoside sweeteners in microbial cells, theoretically guiding the bioproduction of glycoside sweeteners.
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Affiliation(s)
- Guanyi Qu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
- Shandong Jincheng Biological Pharmaceutical Company, Limited, Zibo 255000, P. R. China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
| | - Qinyuan Ma
- Shandong Jincheng Biological Pharmaceutical Company, Limited, Zibo 255000, P. R. China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
- Yixing Institute of Food Biotechnology Company, Limited, Yixing 214200, P. R. China
- Food Laboratory of Zhongyuan, Jiangnan University, Wuxi 214122, P. R. China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, P. R. China
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, P. R. China
- Yixing Institute of Food Biotechnology Company, Limited, Yixing 214200, P. R. China
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5
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Reddy JV, Raudenbush K, Papoutsakis ET, Ierapetritou M. Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation. Biotechnol Adv 2023; 67:108179. [PMID: 37257729 DOI: 10.1016/j.biotechadv.2023.108179] [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: 11/27/2022] [Revised: 05/18/2023] [Accepted: 05/21/2023] [Indexed: 06/02/2023]
Abstract
In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.
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Affiliation(s)
- Jayanth Venkatarama Reddy
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Katherine Raudenbush
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Eleftherios Terry Papoutsakis
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA; Delaware Biotechnology Institute, Department of Biological Sciences, University of Delaware, USA.
| | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA.
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6
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Karlsen ST, Rau MH, Sánchez BJ, Jensen K, Zeidan AA. From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry. FEMS Microbiol Rev 2023; 47:fuad030. [PMID: 37286882 PMCID: PMC10337747 DOI: 10.1093/femsre/fuad030] [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: 02/28/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/09/2023] Open
Abstract
When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.
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Affiliation(s)
- Signe T Karlsen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Martin H Rau
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Benjamín J Sánchez
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Kristian Jensen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Ahmad A Zeidan
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
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7
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Yang CT, Kristiani E, Leong YK, Chang JS. Big data and machine learning driven bioprocessing - Recent trends and critical analysis. BIORESOURCE TECHNOLOGY 2023; 372:128625. [PMID: 36642201 DOI: 10.1016/j.biortech.2023.128625] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 06/17/2023]
Abstract
Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.
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Affiliation(s)
- Chao-Tung Yang
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan
| | - Endah Kristiani
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Department of Informatics, Krida Wacana Christian University, Jakarta 11470, Indonesia
| | - Yoong Kit Leong
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407224, Taiwan
| | - Jo-Shu Chang
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407224, Taiwan; Department of Chemical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
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Gopalakrishnan S, Joshi CJ, Valderrama-Gómez MÁ, Icten E, Rolandi P, Johnson W, Kontoravdi C, Lewis NE. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. Metab Eng 2023; 75:181-191. [PMID: 36566974 PMCID: PMC10258867 DOI: 10.1016/j.ymben.2022.12.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/01/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.
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Affiliation(s)
| | - Chintan J Joshi
- Department of Pediatrics, University of California San Diego, United States
| | | | - Elcin Icten
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Pablo Rolandi
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - William Johnson
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, UK
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, United States; Department of Bioengineering, University of California San Diego, United States.
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Du YH, Wang MY, Yang LH, Tong LL, Guo DS, Ji XJ. Optimization and Scale-Up of Fermentation Processes Driven by Models. Bioengineering (Basel) 2022; 9:bioengineering9090473. [PMID: 36135019 PMCID: PMC9495923 DOI: 10.3390/bioengineering9090473] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.
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Affiliation(s)
- Yuan-Hang Du
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
| | - Min-Yu Wang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Lin-Hui Yang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
| | - Ling-Ling Tong
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
| | - Dong-Sheng Guo
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
- Correspondence: (D.-S.G.); (X.-J.J.)
| | - Xiao-Jun Ji
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China
- Correspondence: (D.-S.G.); (X.-J.J.)
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