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Abdel-Kareem MM, Zohri ANA, Rasmey AHM, Hawary H. Enhancing the biotransformation of progesterone to the anticancer compound testololactone by Penicillium chrysogenum Ras3009: kinetic modelling and efficiency maximization. BMC Biotechnol 2024; 24:73. [PMID: 39367307 PMCID: PMC11451084 DOI: 10.1186/s12896-024-00896-9] [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: 07/22/2024] [Accepted: 09/12/2024] [Indexed: 10/06/2024] Open
Abstract
BACKGROUND Biotransformation of steroid compounds into therapeutic products using microorganisms offers an eco-friendly and economically sustainable approach to the pharmaceutical industry rather than a chemical synthesis way. The biotransformation efficiency of progesterone into the anticancer compound testololactone using Penicillium chrysogenum Ras3009 has been investigated. Besides, maximization of testololactone formation was achieved by studying the kinetic modelling and impact of some fermentation conditions on the biotransformation process. RESULTS The fungal strain Ras3009 was selected among twelve fungal strains as the most runner for the transformation of 81.18% of progesterone into testololactone. Ras3009 was identified phenotypically and genotypically as Penicillium chrysogenum, its 18 S rRNA nucleotide sequence was deposited in the GenBank database by the accession number OR480104. Studying the impact of fermentation conditions on biotransformation efficiency indicated a positive correlation between substrate concentration and testololactone formation until reaching the maximum velocity vmax. Kinetic studies revealed that vmax was [Formula: see text] gL- 1hr- 1 with high accuracy, giving R2 of 0.977. The progesterone transformation efficiency generally increased with time, reaching a maximum of 100% at 42 h with testololactone yield (Ypt/s) 0.8700 mg/mg. Moreover, the study indicated that the enzymatic conversion by P. chrysogenum Ras3009 showed high affinity to the substrate, intracellularly expressed, and released during cell disruption, leading to higher efficiency when using whole microbial cell extract. CONCLUSIONS Fungi can be promising biocatalysts for steroid transformation into valuable chemicals and pharmaceutical compounds. The study revealed that the new fungal isolate P. chrysogenum Ras3009 possesses a great catalytic ability to convert progesterone into testololactone. Kinetic modelling analysis and optimization of the fermentation conditions lead to higher transformation efficiency and provide a better understanding of the transformation processes.
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Affiliation(s)
- Marwa M Abdel-Kareem
- Department of Botany and Microbiology, Faculty of Science, Sohag University, Sohag, EG-82524, Egypt
| | - Abdel-Nasser A Zohri
- Department of Botany and Microbiology, Faculty of Science, Assiut University, Assiut, EG-71516, Egypt
| | - Abdel-Hamied M Rasmey
- Department of Botany and Microbiology, Faculty of Science, Suez University, P.O.Box: 43221, Suez, Egypt.
| | - Heba Hawary
- Department of Botany and Microbiology, Faculty of Science, Suez University, P.O.Box: 43221, Suez, Egypt
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2
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Sahu N, Mahanty B, Haldar D. Challenges and opportunities in bioprocessing of gellan gum: A review. Int J Biol Macromol 2024; 276:133912. [PMID: 39025193 DOI: 10.1016/j.ijbiomac.2024.133912] [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: 03/27/2024] [Revised: 06/26/2024] [Accepted: 07/14/2024] [Indexed: 07/20/2024]
Abstract
Gellan gum (GG) - the microbial exopolysaccharide is increasingly being adopted into drug development, tissue engineering, and food and pharmaceutical products. In spite of the commercial importance and expanding application horizon of GG, little attention has been directed toward the exploration of novel microbial cultures, development of advanced screening protocols, strain engineering, and robust upstream or downstream processes. This comprehensive review not only attempts to summarize the existing knowledge pool on GG bioprocess but also critically assesses their inherent challenges. The process optimization design augmented with advanced machine learning modeling tools, widely adopted in other microbial bioprocesses, should be extended to GG. The unification of mechanistic insight into data-driven modeling would help to formulate optimal feeding and process control strategies.
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Affiliation(s)
- Nageswar Sahu
- Division of Biotechnology, School of Agricultural Sciences, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, India.
| | - Biswanath Mahanty
- Division of Biotechnology, School of Agricultural Sciences, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, India.
| | - Dibyajyoti Haldar
- Division of Biotechnology, School of Agricultural Sciences, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, India.
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3
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Xu S, Xu T, Yang Y, Chen X. Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network. mSystems 2024; 9:e0069724. [PMID: 39057922 PMCID: PMC11334518 DOI: 10.1128/msystems.00697-24] [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: 05/21/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
Modeling microbial metabolic dynamics is important for the rational optimization of both biosynthetic systems and industrial processes to facilitate green and efficient biomanufacturing. Classical approaches utilize explicit equation systems to represent metabolic networks, enabling the quantification of pathway fluxes to identify metabolic bottlenecks. However, these white-box models, despite their diverse applications, have limitations in simulating metabolic dynamics and are intrinsically inaccurate for industrial strains that lack information on network structures and kinetic parameters. On the other hand, black-box models do not rely on prior mechanistic knowledge of strains but are built upon observed time-series trajectories of biosynthetic systems in action. In practice, these observations are typically irregular, with discontinuously observed time points across multiple independent batches, each time point potentially containing missing measurements. Learning from such irregular data remains challenging for existing approaches. To address this issue, we present the Bidirectional Time-Series State Transfer Network (BTSTN) for modeling metabolic dynamics directly from irregular observations. Using evaluation data sets derived from both ideal dynamic systems and a real-world fermentation process, we demonstrate that BTSTN accurately reconstructs dynamic behaviors and predicts future trajectories. This approach exhibits enhanced robustness against missing measurements and noise, as compared to the state-of-the-art methods.IMPORTANCEIndustrial biosynthetic systems often involve strains with unclear genetic backgrounds, posing challenges in modeling their distinct metabolic dynamics. In such scenarios, white-box models, which commonly rely on inferred networks, are thereby of limited applicability and accuracy. In contrast, black-box models, such as statistical models and neural networks, are directly fitted or learned from observed time-series trajectories of biosynthetic systems in action. These methods typically assume regular observations without missing time points or measurements. If the observations are irregular, a pre-processing step becomes necessary to obtain a fully filled data set for subsequent model training, which, at the same time, inevitably introduces errors into the resulting models. BTSTN is a novel approach that natively learns from irregular observations. This distinctive feature makes it a unique addition to the current arsenal of technologies modeling metabolic dynamics.
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Affiliation(s)
- Shaohua Xu
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory for Microbial Biochemistry and Metabolic Engineering, Hangzhou, China
| | - Ting Xu
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuping Yang
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
| | - Xin Chen
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory for Microbial Biochemistry and Metabolic Engineering, Hangzhou, China
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4
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Xie L, Yu W, Gao J, Wang H, Zhou YJ. Ogataea polymorpha as a next-generation chassis for industrial biotechnology. Trends Biotechnol 2024:S0167-7799(24)00086-6. [PMID: 38622041 DOI: 10.1016/j.tibtech.2024.03.007] [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: 01/30/2024] [Revised: 03/15/2024] [Accepted: 03/18/2024] [Indexed: 04/17/2024]
Abstract
Ogataea (Hansenula) polymorpha is a nonconventional yeast with some unique characteristics, including fast growth, thermostability, and broad substrate spectrum. Other than common applications for protein production, O. polymorpha is attracting interest for chemical and protein production from methanol; a promising feedstock for the next-generation biomanufacturing due to its abundant sources and excellent characteristics. Benefiting from the development of synthetic biology, it has been engineered to produce value-added chemicals by extensively rewiring cellular metabolism. This Review discusses recently developed synthetic biology tools of O. polymorpha. The advances of chemicals production and systems biology were reviewed comprehensively. Finally, we look ahead to the developments of biomanufacturing in O. polymorpha to make an overall understanding of this chassis for academia and industry.
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Affiliation(s)
- Linfeng Xie
- Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Yu
- Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Dalian Key Laboratory of Energy Biotechnology, Dalian Institute of Chemical Physics, CAS, 457 Zhongshan Road, Dalian 116023, China
| | - Jiaoqi Gao
- Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Dalian Key Laboratory of Energy Biotechnology, Dalian Institute of Chemical Physics, CAS, 457 Zhongshan Road, Dalian 116023, China
| | - Haoyu Wang
- Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongjin J Zhou
- Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Dalian Key Laboratory of Energy Biotechnology, Dalian Institute of Chemical Physics, CAS, 457 Zhongshan Road, Dalian 116023, China.
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Hawary H, Marwa AKM, Rasmey AHM. Kinetic modeling and optimization of ethanol fermentation by the marine yeast Wickerhamomyces subpelliculosus ZE75. World J Microbiol Biotechnol 2024; 40:155. [PMID: 38581587 PMCID: PMC10998816 DOI: 10.1007/s11274-024-03942-y] [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: 10/13/2023] [Accepted: 02/25/2024] [Indexed: 04/08/2024]
Abstract
The study aims to enhance ethanol production by Wickerhamomyces subpelliculosus ZE75 isolated from marine sediment. In addition, analyzing the kinetic parameters of ethanol production and optimization of the fermentation conditions was performed. The marine yeast isolate ZE75 was selected as the front runner ethanol-producer, with an ethanol yield of 89.77 gL-1. ZE75 was identified relying on the phenotypic and genotypic characteristics of W. subpelliculosus. The genotypic characterization based on the Internal Transcribed Spacer (ITS) sequence was deposited in the GenBank database with the accession number OP715873. The maximum specific ethanol production rate (vmax) was 0.482 gg-1 h-1 at 175 gL-1 glucose concentration, with a high accuracy of R2 0.95. The maximum growth specific rates (μmax) were 0.141 h-1 obtained at 150 gL-1 glucose concentration with R2 0.91. Optimization of the fermentation parameters such as pH and salinity has been achieved. The highest ethanol yield 0.5637 gg-1 was achieved in a 100% natural seawater-based medium. The maximum ethanol production of 104.04 gL-1 was achieved at pH 4.5 with a specific ethanol rate of 0.1669 gg-1 h-1. The findings of the present study recommend the possibility of ethanol production from a seawater-based medium on a large scale using W. subpelliculosus ZE75.
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Affiliation(s)
- Heba Hawary
- Botany and Microbiology Department, Faculty of Science, Suez University, Suez, 43221, Egypt.
| | - Abdel-Kareem M Marwa
- Botany and Microbiology Department, Faculty of Science, Sohag University, Sohag, 82524, Egypt
| | - Abdel-Hamied M Rasmey
- Botany and Microbiology Department, Faculty of Science, Suez University, Suez, 43221, Egypt
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6
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Sigg A, Klimacek M, Nidetzky B. Pushing the boundaries of phosphorylase cascade reaction for cellobiose production I: Kinetic model development. Biotechnol Bioeng 2024; 121:580-592. [PMID: 37983971 DOI: 10.1002/bit.28602] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/31/2023] [Accepted: 11/04/2023] [Indexed: 11/22/2023]
Abstract
One-pot cascade reactions of coupled disaccharide phosphorylases enable an efficient transglycosylation via intermediary α-d-glucose 1-phosphate (G1P). Such transformations have promising applications in the production of carbohydrate commodities, including the disaccharide cellobiose for food and feed use. Several studies have shown sucrose and cellobiose phosphorylase for cellobiose synthesis from sucrose, but the boundaries on transformation efficiency that result from kinetic and thermodynamic characteristics of the individual enzyme reactions are not known. Here, we assessed in a step-by-step systematic fashion the practical requirements of a kinetic model to describe cellobiose production at industrially relevant substrate concentrations of up to 600 mM sucrose and glucose each. Mechanistic initial-rate models of the two-substrate reactions of sucrose phosphorylase (sucrose + phosphate → G1P + fructose) and cellobiose phosphorylase (G1P + glucose → cellobiose + phosphate) were needed and additionally required expansion by terms of glucose inhibition, in particular a distinctive two-site glucose substrate inhibition of the cellobiose phosphorylase (from Cellulumonas uda). Combined with mass action terms accounting for the approach to equilibrium, the kinetic model gave an excellent fit and a robust prediction of the full reaction time courses for a wide range of enzyme activities as well as substrate concentrations, including the variable substoichiometric concentration of phosphate. The model thus provides the essential engineering tool to disentangle the highly interrelated factors of conversion efficiency in the coupled enzyme reaction; and it establishes the necessary basis of window of operation calculations for targeted optimizations toward different process tasks.
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Affiliation(s)
- Alexander Sigg
- Institute of Biotechnology and Biochemical Engineering, Graz University of Technology, NAWI Graz, Graz, Austria
| | - Mario Klimacek
- Institute of Biotechnology and Biochemical Engineering, Graz University of Technology, NAWI Graz, Graz, Austria
| | - Bernd Nidetzky
- Institute of Biotechnology and Biochemical Engineering, Graz University of Technology, NAWI Graz, Graz, Austria
- Austrian Centre of Industrial Biotechnology (ACIB), Graz, Austria
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Sigg A, Klimacek M, Nidetzky B. Pushing the boundaries of phosphorylase cascade reaction for cellobiose production II: Model-based multiobjective optimization. Biotechnol Bioeng 2024; 121:566-579. [PMID: 37986649 DOI: 10.1002/bit.28601] [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: 09/15/2023] [Revised: 11/01/2023] [Accepted: 11/04/2023] [Indexed: 11/22/2023]
Abstract
The inherent complexity of coupled biocatalytic reactions presents a major challenge for process development with one-pot multienzyme cascade transformations. Kinetic models are powerful engineering tools to guide the optimization of cascade reactions towards a performance suitable for scale up to an actual production. Here, we report kinetic model-based window of operation analysis for cellobiose production (≥100 g/L) from sucrose and glucose by indirect transglycosylation via glucose 1-phosphate as intermediate. The two-step cascade transformation is catalyzed by sucrose and cellobiose phosphorylase in the presence of substoichiometric amounts of phosphate (≤27 mol% of substrate). Kinetic modeling was instrumental to uncover the hidden effect of bulk microviscosity due to high sugar concentrations on decreasing the rate of cellobiose phosphorylase specifically. The mechanistic-empirical hybrid model thus developed gives a comprehensive description of the cascade reaction at industrially relevant substrate conditions. Model simulations serve to unravel opposed relationships between efficient utilization of the enzymes and maximized concentration (or yield) of the product within a given process time, in dependence of the initial concentrations of substrate and phosphate used. Optimum balance of these competing key metrics of process performance is suggested from the model-calculated window of operation and is verified experimentally. The evidence shown highlights the important use of kinetic modeling for the characterization and optimization of cascade reactions in ways that appear to be inaccessible to purely data-driven approaches.
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Affiliation(s)
- Alexander Sigg
- Institute of Biotechnology and Biochemical Engineering, Graz University of Technology, NAWI Graz, Graz, Austria
| | - Mario Klimacek
- Institute of Biotechnology and Biochemical Engineering, Graz University of Technology, NAWI Graz, Graz, Austria
| | - Bernd Nidetzky
- Institute of Biotechnology and Biochemical Engineering, Graz University of Technology, NAWI Graz, Graz, Austria
- Austrian Centre of Industrial Biotechnology (acib), Graz, Austria
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8
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Narayanan B, Weilandt D, Masid M, Miskovic L, Hatzimanikatis V. Rational strain design with minimal phenotype perturbation. Nat Commun 2024; 15:723. [PMID: 38267425 PMCID: PMC10808392 DOI: 10.1038/s41467-024-44831-0] [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: 12/06/2022] [Accepted: 01/08/2024] [Indexed: 01/26/2024] Open
Abstract
Devising genetic interventions for desired cellular phenotypes remains challenging regarding time and resources. Kinetic models can accelerate this task by simulating metabolic responses to genetic perturbations. However, exhaustive design evaluations with kinetic models are computationally impractical, especially when targeting multiple enzymes. Here, we introduce a framework for efficiently scouting the design space while respecting cellular physiological requirements. The framework employs mixed-integer linear programming and nonlinear simulations with large-scale nonlinear kinetic models to devise genetic interventions while accounting for the network effects of these perturbations. Importantly, it ensures the engineered strain's robustness by maintaining its phenotype close to that of the reference strain. The framework, applied to improve the anthranilate production in E. coli, devises designs for experimental implementation, including eight previously experimentally validated targets. We expect this framework to play a crucial role in future design-build-test-learn cycles, significantly expediting the strain design compared to exhaustive design enumeration.
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Affiliation(s)
- Bharath Narayanan
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
- Department of Oncology, University of Cambridge, Cambridge, CB2 0XZ, UK
| | - Daniel Weilandt
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - Maria Masid
- Ludwig Institute for Cancer Research, Department of Oncology, University of Lausanne, and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Ljubisa Miskovic
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
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Basile A, Zampieri G, Kovalovszki A, Karkaria B, Treu L, Patil KR, Campanaro S. Modelling of microbial interactions in anaerobic digestion: from black to glass box. Curr Opin Microbiol 2023; 75:102363. [PMID: 37542746 DOI: 10.1016/j.mib.2023.102363] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/20/2023] [Accepted: 07/10/2023] [Indexed: 08/07/2023]
Abstract
Anaerobic and microaerophilic environments are pervasive in nature, providing essential contributions to the maintenance of human health, biogeochemical cycles and the Earth's climate. These ecological niches are characterised by low free oxygen and oxidants, or lack thereof. Under these conditions, interactions between species are essential for supporting the growth of syntrophic species and maintaining thermodynamic feasibility of anaerobic fermentation. Kinetic models provide a simplified view of complex metabolic networks, while genome-scale metabolic models and flux-balance analysis (FBA) aim to unravel these systems as a whole. The target of this review is to outline the main similarities, differences and challenges associated with kinetic and metabolic modelling, and describe state-of-the-art modelling practices for studying syntrophies in the anaerobic digestion (AD) case study.
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Affiliation(s)
- Arianna Basile
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
| | - Guido Zampieri
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy
| | - Adam Kovalovszki
- Department of Environmental and Resource Engineering, Technical University of Denmark, Building 115, Bygningstorvet, 2800 Kgs. Lyngby, Denmark
| | - Behzad Karkaria
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Laura Treu
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy.
| | - Kiran Raosaheb Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Stefano Campanaro
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padova, Italy
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Masson HO, Karottki KJLC, Tat J, Hefzi H, Lewis NE. From observational to actionable: rethinking omics in biologics production. Trends Biotechnol 2023; 41:1127-1138. [PMID: 37062598 PMCID: PMC10524802 DOI: 10.1016/j.tibtech.2023.03.009] [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: 01/11/2023] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 04/18/2023]
Abstract
As the era of omics continues to expand with increasing ubiquity and success in both academia and industry, omics-based experiments are becoming commonplace in industrial biotechnology, including efforts to develop novel solutions in bioprocess optimization and cell line development. Omic technologies provide particularly valuable 'observational' insights for discovery science, especially in academic research and industrial R&D; however, biomanufacturing requires a different paradigm to unlock 'actionable' insights from omics. Here, we argue the value of omic experiments in biotechnology can be maximized with deliberate selection of omic approaches and forethought about analysis techniques. We describe important considerations when designing and implementing omic-based experiments and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in mammalian-based biologics production.
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Affiliation(s)
- Helen O Masson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | - Jasmine Tat
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA; Amgen Inc., Thousand Oaks, CA, USA
| | | | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
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Marín-Hernández Á, Saavedra E. Metabolic control analysis as a strategy to identify therapeutic targets, the case of cancer glycolysis. Biosystems 2023; 231:104986. [PMID: 37506818 DOI: 10.1016/j.biosystems.2023.104986] [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: 03/15/2023] [Revised: 07/23/2023] [Accepted: 07/25/2023] [Indexed: 07/30/2023]
Abstract
The use of kinetic modeling and metabolic control analysis (MCA) to identify possible therapeutic targets and to investigate the controlling and regulatory mechanisms in cancer glycolysis is here reviewed. The glycolytic pathway has been considered a target to decrease cancer cell growth; however, its occurrence in normal cells makes it difficult to design therapeutic strategies that target this pathway in pathological cells. Notwithstanding, the over-expression of all enzymes and transporters, as well as the expression of isoenzymes with different kinetic and regulatory properties in cancer cells, suggested a different distribution of the control of glycolytic flux than that observed in normal cells. Kinetic models of glycolysis are constructed with enzyme kinetics experimental data, validated with the steady-state metabolite concentrations and glycolytic fluxes; applying MCA, permitted us to identify the steps with the highest control of glycolysis in cancer cells, but low control in normal cells. The cancer glycolysis main controlling steps under several metabolic conditions were: glucose transport, hexokinase and hexose-6-phosphate isomerase (HPI); whereas in normal cells were: the first two and phosphofructokinase-1. HPI is the best therapeutic target because it exerts high control in cancer glycolytic flux, but not in normal cells. Furthermore, kinetic modeling also contributed to identifying new feed-back and feed-forward regulatory loops in cancer cells glycolysis, and to understanding the mode of metabolic action of glycolytic inhibitors. Thus, MCA and metabolic modeling allowed to propose new strategies for inhibiting glycolysis in cancer cells.
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Affiliation(s)
- Álvaro Marín-Hernández
- Departamento de Bioquímica, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, 14080, Mexico.
| | - Emma Saavedra
- Departamento de Bioquímica, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, 14080, Mexico
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Fakih I, Got J, Robles-Rodriguez CE, Siegel A, Forano E, Muñoz-Tamayo R. Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85. mSystems 2023; 8:e0102722. [PMID: 37289026 PMCID: PMC10308913 DOI: 10.1128/msystems.01027-22] [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: 10/24/2022] [Accepted: 03/14/2023] [Indexed: 06/09/2023] Open
Abstract
Fibrobacter succinogenes is a cellulolytic bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. It converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate, and formate. We developed dynamic models of F. succinogenes S85 metabolism on glucose, cellobiose, and cellulose on the basis of a network reconstruction done with the automatic reconstruction of metabolic model workspace. The reconstruction was based on genome annotation, five template-based orthology methods, gap filling, and manual curation. The metabolic network of F. succinogenes S85 comprises 1,565 reactions with 77% linked to 1,317 genes, 1,586 unique metabolites, and 931 pathways. The network was reduced using the NetRed algorithm and analyzed for the computation of elementary flux modes. A yield analysis was further performed to select a minimal set of macroscopic reactions for each substrate. The accuracy of the models was acceptable in simulating F. succinogenes carbohydrate metabolism with an average coefficient of variation of the root mean squared error of 19%. The resulting models are useful resources for investigating the metabolic capabilities of F. succinogenes S85, including the dynamics of metabolite production. Such an approach is a key step toward the integration of omics microbial information into predictive models of rumen metabolism. IMPORTANCE F. succinogenes S85 is a cellulose-degrading and succinate-producing bacterium. Such functions are central for the rumen ecosystem and are of special interest for several industrial applications. This work illustrates how information of the genome of F. succinogenes can be translated to develop predictive dynamic models of rumen fermentation processes. We expect this approach can be applied to other rumen microbes for producing a model of rumen microbiome that can be used for studying microbial manipulation strategies aimed at enhancing feed utilization and mitigating enteric emissions.
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Affiliation(s)
- Ibrahim Fakih
- Université Clermont Auvergne, INRAE, UMR454 Microbiologie Environnement Digestif et Santé, 63000 Clermont-Ferrand, France
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
| | - Jeanne Got
- Université Rennes, Inria, CNRS, IRISA, Dyliss team, 35042 Rennes, France
| | | | - Anne Siegel
- Université Rennes, Inria, CNRS, IRISA, Dyliss team, 35042 Rennes, France
| | - Evelyne Forano
- Université Clermont Auvergne, INRAE, UMR454 Microbiologie Environnement Digestif et Santé, 63000 Clermont-Ferrand, France
| | - Rafael Muñoz-Tamayo
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
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13
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Helleckes LM, Hemmerich J, Wiechert W, von Lieres E, Grünberger A. Machine learning in bioprocess development: from promise to practice. Trends Biotechnol 2023; 41:817-835. [PMID: 36456404 DOI: 10.1016/j.tibtech.2022.10.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 11/30/2022]
Abstract
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.
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Affiliation(s)
- Laura M Helleckes
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Johannes Hemmerich
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Wolfgang Wiechert
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Eric von Lieres
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Institute of Process Engineering in Life Sciences, Section III: Microsystems in Bioprocess Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany.
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14
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Sahin A, Weilandt DR, Hatzimanikatis V. Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations. Nat Commun 2023; 14:2618. [PMID: 37147292 PMCID: PMC10162984 DOI: 10.1038/s41467-023-38159-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/19/2023] [Indexed: 05/07/2023] Open
Abstract
Deciphering the metabolic functions of organisms requires understanding the dynamic responses of living cells upon genetic and environmental perturbations, which in turn can be inferred from enzymatic activity. In this work, we investigate the optimal modes of operation for enzymes in terms of the evolutionary pressure driving them toward increased catalytic efficiency. We develop a framework using a mixed-integer formulation to assess the distribution of thermodynamic forces and enzyme states, providing detailed insights into the enzymatic mode of operation. We use this framework to explore Michaelis-Menten and random-ordered multi-substrate mechanisms. We show that optimal enzyme utilization is achieved by unique or alternative operating modes dependent on reactant concentrations. We find that in a bimolecular enzyme reaction, the random mechanism is optimal over any other ordered mechanism under physiological conditions. Our framework can investigate the optimal catalytic properties of complex enzyme mechanisms. It can further guide the directed evolution of enzymes and fill in the knowledge gaps in enzyme kinetics.
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Affiliation(s)
- Asli Sahin
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Federale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Daniel R Weilandt
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Federale de Lausanne (EPFL), 1015, Lausanne, Switzerland
- Department of Chemistry and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Federale de Lausanne (EPFL), 1015, Lausanne, Switzerland.
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15
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Lao-Martil D, Schmitz JPJ, Teusink B, van Riel NAW. Elucidating yeast glycolytic dynamics at steady state growth and glucose pulses through kinetic metabolic modeling. Metab Eng 2023; 77:128-142. [PMID: 36963461 DOI: 10.1016/j.ymben.2023.03.005] [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: 09/27/2022] [Revised: 02/12/2023] [Accepted: 03/05/2023] [Indexed: 03/26/2023]
Abstract
Microbial cell factories face changing environments during industrial fermentations. Kinetic metabolic models enable the simulation of the dynamic metabolic response to these perturbations, but their development is challenging due to model complexity and experimental data requirements. An example of this is the well-established microbial cell factory Saccharomyces cerevisiae, for which no consensus kinetic model of central metabolism has been developed and implemented in industry. Here, we aim to bring the academic and industrial communities closer to this consensus model. We developed a physiology informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. To parametrize such a large model, a parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. Additionally, we show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Such a comprehensive model could not have been developed without using steady state and glucose perturbation data sets. The resulting metabolic reconstruction and parameter estimation pipeline can be applied in the future to study other industrially-relevant scenarios. We show this by generating a hybrid CFD-metabolic model to explore intracellular glycolytic dynamics for the first time. The model suggests that all intracellular metabolites oscillate within a physiological range, except carbon storage metabolism, which is sensitive to the extracellular environment.
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Affiliation(s)
- David Lao-Martil
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Noord-Brabant, 5612AE, the Netherlands
| | - Joep P J Schmitz
- DSM Biotechnology Center, Delft, Zuid-Holland, 2613AX, the Netherlands
| | - Bas Teusink
- Systems Biology Lab, Vrije Universiteit Amsterdam, Amsterdam, Noord-Holland, 1081HZ, the Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Noord-Brabant, 5612AE, the Netherlands; Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Noord-Holland, 1105AZ, the Netherlands.
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16
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Pandey AK, Park J, Ko J, Joo HH, Raj T, Singh LK, Singh N, Kim SH. Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications. BIORESOURCE TECHNOLOGY 2023; 370:128502. [PMID: 36535617 DOI: 10.1016/j.biortech.2022.128502] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/11/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.
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Affiliation(s)
- Ashutosh Kumar Pandey
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Jungsu Park
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Jeun Ko
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Hwan-Hong Joo
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Tirath Raj
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Lalit Kumar Singh
- Department of Biochemical Engineering, Harcourt Butler Technical University, Kanpur 208002, Uttar Pradesh (UP), India
| | - Noopur Singh
- Dr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh (UP), India
| | - Sang-Hyoun Kim
- Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.
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17
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Straathof AJ. Modelling of end-product inhibition in fermentation. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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18
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Janoska A, Buijs J, van Gulik WM. Predicting the influence of combined oxygen and glucose gradients based on scale-down and modelling approaches for the scale-up of penicillin fermentations. Process Biochem 2022. [DOI: 10.1016/j.procbio.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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19
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Comparative Analysis of a Family of Sliding Mode Observers under Real-Time Conditions for the Monitoring in the Bioethanol Production. FERMENTATION-BASEL 2022. [DOI: 10.3390/fermentation8090446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Online monitoring of fermentation processes is a necessary task to determine concentrations of key biochemical compounds, diagnose faults in process operations, and implement feedback controllers. However, obtaining the signals of all-important variables in a real process is a task that may be difficult and expensive due to the lack of adequate sensors, or simply because some variables cannot be directly measured. From the above, a model-based approach such as state observers may be a viable alternative to solve the estimation problem. This work shows a comparative analysis of the real-time performance of a family of sliding-mode observers for reconstructing key variables in a batch bioreactor for fermentative ethanol production. These observers were selected for their robust performance under model uncertainties and finite-time estimation convergence. The selected sliding-mode observers were the first-order sliding mode observer, the proportional sliding mode observer, and the high-order sliding mode observer. For estimation purposes, a power law kinetic model for ethanol production by Saccharomyces cerevisiae was performed. A hybrid methodology allows the kinetic parameters to be adjusted, and an approach based on inference diagrams allows the observability of the model to be determined. The experimental results reported here show that the observers under analysis were robust to modeling errors and measurement noise. Moreover, the proportional sliding-mode observer was the algorithm that exhibited the best performance.
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20
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Li H, Zhang S, Xu S, Yang J, Yuan Y, Gao X, Li H, Yu J. Enzymatic hydrolysis of milk protein by complex enzyme mixture of alcalase and neutrase: Kinetic model and hydrolysis control. INT J DAIRY TECHNOL 2022. [DOI: 10.1111/1471-0307.12907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Hongbo Li
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering Tianjin University of Science and Technology No. 29, No. 13 Avenue, Economic‐Technological Development Area (TEDA) Tianjin 300457 China
| | - Shuhua Zhang
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering Tianjin University of Science and Technology No. 29, No. 13 Avenue, Economic‐Technological Development Area (TEDA) Tianjin 300457 China
| | - Siyuan Xu
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering Tianjin University of Science and Technology No. 29, No. 13 Avenue, Economic‐Technological Development Area (TEDA) Tianjin 300457 China
| | - Jingjing Yang
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering Tianjin University of Science and Technology No. 29, No. 13 Avenue, Economic‐Technological Development Area (TEDA) Tianjin 300457 China
| | - Yujing Yuan
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering Tianjin University of Science and Technology No. 29, No. 13 Avenue, Economic‐Technological Development Area (TEDA) Tianjin 300457 China
| | - Xianjun Gao
- Tianjin Tianyi‐U Biotechnology Co., Ltd. No. 88, North Central Road, Hangzhou Street, Economic‐Technological Development Area (TEDA) Tianjin 300457 China
| | - Hongjuan Li
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering Tianjin University of Science and Technology No. 29, No. 13 Avenue, Economic‐Technological Development Area (TEDA) Tianjin 300457 China
| | - Jinghua Yu
- State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Food Nutrition and Safety, Ministry of Education, College of Food Science and Engineering Tianjin University of Science and Technology No. 29, No. 13 Avenue, Economic‐Technological Development Area (TEDA) Tianjin 300457 China
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21
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Dev MJ, Warke RG, Warke GM, Mahajan GB, Patil TA, Singhal RS. Advances in fermentative production, purification, characterization and applications of gellan gum. BIORESOURCE TECHNOLOGY 2022; 359:127498. [PMID: 35724911 DOI: 10.1016/j.biortech.2022.127498] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/11/2022] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Multiple microbial exopolysaccharides have been reported in recent decade with their structural and functional features. Gellan gum (GG) is among these emerging biopolymers with versatile properties. Low production yield, high downstream cost, and abundant market demand have made GG a high cost material. Hence, an understanding on the various possibilities to develop cost-effective gellan gum bioprocess is desirable. This review focuses on details of upstream and downstream process of GG from an industrial perspective. It emphasizes on GG producing Sphingomonas spp., updates on biosynthesis, strain and media engineering, kinetic modeling, bioreactor design and scale-up considerations. Details of the downstream operations with possible modifications to make it cost-effective and environmentally sustainable have been discussed. The updated regulatory criteria for GG as a food ingredient and analytical tools required to validate the same have been briefly discussed. Derivatives of GG and their applications in various industrial segments have also been highlighted.
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Affiliation(s)
- Manoj J Dev
- Food Engineering and Technology Department, Institute of Chemical Technology, Mumbai, India
| | - Rahul G Warke
- Microbiology Division, Hi-Media Laboratories Pvt. Ltd., Mumbai, India
| | - Gangadhar M Warke
- Microbiology Division, Hi-Media Laboratories Pvt. Ltd., Mumbai, India
| | - Girish B Mahajan
- Microbiology Division, Hi-Media Laboratories Pvt. Ltd., Mumbai, India
| | - Tanuja A Patil
- Microbiology Division, Hi-Media Laboratories Pvt. Ltd., Mumbai, India
| | - Rekha S Singhal
- Food Engineering and Technology Department, Institute of Chemical Technology, Mumbai, India.
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22
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Choudhury S, Moret M, Salvy P, Weilandt D, Hatzimanikatis V, Miskovic L. Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks. NAT MACH INTELL 2022; 4:710-719. [PMID: 37790987 PMCID: PMC10543203 DOI: 10.1038/s42256-022-00519-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 07/11/2022] [Indexed: 11/09/2022]
Abstract
Kinetic models of metabolism relate metabolic fluxes, metabolite concentrations and enzyme levels through mechanistic relations, rendering them essential for understanding, predicting and optimizing the behaviour of living organisms. However, due to the lack of kinetic data, traditional kinetic modelling often yields only a few or no kinetic models with desirable dynamical properties, making the analysis unreliable and computationally inefficient. We present REKINDLE (Reconstruction of Kinetic Models using Deep Learning), a deep-learning-based framework for efficiently generating kinetic models with dynamic properties matching the ones observed in cells. We showcase REKINDLE's capabilities to navigate through the physiological states of metabolism using small numbers of data with significantly lower computational requirements. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in biotechnology and health.
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Affiliation(s)
- Subham Choudhury
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael Moret
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pierre Salvy
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Present Address: Cambrium GmBH, Berlin, Germany
| | - Daniel Weilandt
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Present Address: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ USA
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ljubisa Miskovic
- Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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23
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Briki A, Olmos E, Delaunay S, Fournier F. Generalized modelling of effect of oxygenation and glucose concentration on Corynebacterium glutamicum growth and production kinetics. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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24
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Modeling the Succinic Acid Bioprocess: A Review. FERMENTATION 2022. [DOI: 10.3390/fermentation8080368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Succinic acid has attracted much interest as a key platform chemical that can be obtained in high titers from biomass through sustainable fermentation processes, thus boosting the bioeconomy as a critical production strategy for the future. After several years of development of the production of succinic acid, many studies on lab or pilot scale production have been reported. The relevant experimental data reveal underlying physical and chemical dynamic phenomena. To take advantage of this vast, but disperse, kinetic information, a number of mathematical kinetic models of the unstructured non-segregated type have been proposed in the first place. These relatively simple models feature critical aspects of interest for the design, control, optimization and operation of this key bioprocess. This review includes a detailed description of the phenomena involved in the bioprocesses and how they reflect on the most important and recent models based on macroscopic and metabolic chemical kinetics, and in some cases even coupling mass transport.
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25
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Lapin A, Perfahl H, Jain HV, Reuss M. Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth. Sci Rep 2022; 12:12373. [PMID: 35858953 PMCID: PMC9300625 DOI: 10.1038/s41598-022-15767-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/29/2022] [Indexed: 11/09/2022] Open
Abstract
We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.
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Affiliation(s)
- Alexey Lapin
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany
- Institute of Chemical Process Engineering, University Stuttgart, Stuttgart, Germany
| | - Holger Perfahl
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany
| | - Harsh Vardhan Jain
- Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, MN, USA
| | - Matthias Reuss
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany.
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26
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Chaleewong T, Khunnonkwao P, Puchongkawarin C, Jantama K. Kinetic Modeling of Succinate Production from Glucose and Xylose by Metabolically Engineered Escherichia coli KJ12201. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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27
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Ramos JRC, Bissinger T, Genzel Y, Reichl U. Impact of Influenza A Virus Infection on Growth and Metabolism of Suspension MDCK Cells Using a Dynamic Model. Metabolites 2022; 12:metabo12030239. [PMID: 35323683 PMCID: PMC8950586 DOI: 10.3390/metabo12030239] [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: 02/16/2022] [Revised: 03/04/2022] [Accepted: 03/09/2022] [Indexed: 11/21/2022] Open
Abstract
Cell cultured-based influenza virus production is a viable option for vaccine manufacturing. In order to achieve a high concentration of viable cells, is requirement to have not only optimal process conditions, but also an active metabolism capable of intracellular synthesis of viral components. Experimental metabolic data collected in such processes are complex and difficult to interpret, for which mathematical models are an appropriate way to simulate and analyze the complex and dynamic interaction between the virus and its host cell. A dynamic model with 35 states was developed in this study to describe growth, metabolism, and influenza A virus production in shake flask cultivations of suspension Madin-Darby Canine Kidney (MDCK) cells. It considers cell growth (concentration of viable cells, mean cell diameters, volume of viable cells), concentrations of key metabolites both at the intracellular and extracellular level and virus titers. Using one set of parameters, the model accurately simulates the dynamics of mock-infected cells and correctly predicts the overall dynamics of virus-infected cells for up to 60 h post infection (hpi). The model clearly suggests that most changes observed after infection are related to cessation of cell growth and the subsequent transition to apoptosis and cell death. However, predictions do not cover late phases of infection, particularly for the extracellular concentrations of glutamate and ammonium after about 12 hpi. Results obtained from additional in silico studies performed indicated that amino acid degradation by extracellular enzymes resulting from cell lysis during late infection stages may contribute to this observed discrepancy.
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Affiliation(s)
- João Rodrigues Correia Ramos
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
- Correspondence:
| | - Thomas Bissinger
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
| | - Yvonne Genzel
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
| | - Udo Reichl
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
- Institute of Process Engineering, Faculty of Process & Systems Engineering, Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany
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28
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Liang E, Zhang S, Liu B, Qi B, Nie Y, Yuan Z. Mathematical Modeling for the Industrial 2-Mercaptobenzothiazole Batch Production Process. ACS OMEGA 2022; 7:6963-6977. [PMID: 35252688 PMCID: PMC8892915 DOI: 10.1021/acsomega.1c06646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
As an important chemical intermediate, 2-mercaptobenzothiazole (MBT) is widely used in various processes, especially in the rubber industry. However, there is no first-principles model that describes the synthetic process of MBT. This paper focuses on the formulation of a reliable mathematical model represented by a series of differential and algebraic equations for the industrial batch MBT production process. It is difficult to estimate all of the unknown parameters in the model because of the lack of sufficient industrial/experimental data. Thus, a reduced estimable parameter set is derived by performing estimability analysis on the original estimation problem. A multiple-starting-point strategy is then applied to numerically solve the non-convex parameter estimation problem with the weighted least-squares approach. Subsequently, a cross-validation technique is employed to evaluate the generalizability of the proposed model. Finally, it is confirmed that the proposed model produces encouraging prediction performance with regard to independent test data.
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Affiliation(s)
- Enzhi Liang
- State
Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Song Zhang
- China
Sunsine Chemical Holdings Ltd., Shanxian City, Shandong 274300, China
| | - Bin Liu
- China
Sunsine Chemical Holdings Ltd., Shanxian City, Shandong 274300, China
| | - Bujin Qi
- China
Sunsine Chemical Holdings Ltd., Shanxian City, Shandong 274300, China
| | - Yanpei Nie
- China
Sunsine Chemical Holdings Ltd., Shanxian City, Shandong 274300, China
| | - Zhihong Yuan
- State
Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
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29
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Kaya U, Gopireddy S, Urbanetz N, Nopens I, Verwaeren J. Predicting the Hydrodynamic Properties of a Bioreactor: Conditional Density Estimation as a Surrogate Model for CFD Simulations. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2022.03.042] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Berzins K, Muiznieks R, Baumanis MR, Strazdina I, Shvirksts K, Prikule S, Galvanauskas V, Pleissner D, Pentjuss A, Grube M, Kalnenieks U, Stalidzans E. Kinetic and Stoichiometric Modeling-Based Analysis of Docosahexaenoic Acid (DHA) Production Potential by C. cohnii from Glycerol, Glucose and Ethanol. Mar Drugs 2022; 20:md20020115. [PMID: 35200644 PMCID: PMC8879253 DOI: 10.3390/md20020115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 11/16/2022] Open
Abstract
Docosahexaenoic acid (DHA) is one of the most important long-chain polyunsaturated fatty acids (LC-PUFAs), with numerous health benefits. Crypthecodinium cohnii, a marine heterotrophic dinoflagellate, is successfully used for the industrial production of DHA because it can accumulate DHA at high concentrations within the cells. Glycerol is an interesting renewable substrate for DHA production since it is a by-product of biodiesel production and other industries, and is globally generated in large quantities. The DHA production potential from glycerol, ethanol and glucose is compared by combining fermentation experiments with the pathway-scale kinetic modeling and constraint-based stoichiometric modeling of C. cohnii metabolism. Glycerol has the slowest biomass growth rate among the tested substrates. This is partially compensated by the highest PUFAs fraction, where DHA is dominant. Mathematical modeling reveals that glycerol has the best experimentally observed carbon transformation rate into biomass, reaching the closest values to the theoretical upper limit. In addition to our observations, the published experimental evidence indicates that crude glycerol is readily consumed by C. cohnii, making glycerol an attractive substrate for DHA production.
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Affiliation(s)
- Kristaps Berzins
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (K.B.); (R.M.); (M.R.B.); (I.S.); (K.S.); (S.P.); (A.P.); (M.G.); (U.K.)
| | - Reinis Muiznieks
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (K.B.); (R.M.); (M.R.B.); (I.S.); (K.S.); (S.P.); (A.P.); (M.G.); (U.K.)
| | - Matiss R. Baumanis
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (K.B.); (R.M.); (M.R.B.); (I.S.); (K.S.); (S.P.); (A.P.); (M.G.); (U.K.)
| | - Inese Strazdina
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (K.B.); (R.M.); (M.R.B.); (I.S.); (K.S.); (S.P.); (A.P.); (M.G.); (U.K.)
| | - Karlis Shvirksts
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (K.B.); (R.M.); (M.R.B.); (I.S.); (K.S.); (S.P.); (A.P.); (M.G.); (U.K.)
| | - Santa Prikule
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (K.B.); (R.M.); (M.R.B.); (I.S.); (K.S.); (S.P.); (A.P.); (M.G.); (U.K.)
| | - Vytautas Galvanauskas
- Biotehniskais Centrs AS, Dzerbenes Street 27, LV-1006 Riga, Latvia;
- Department of Automation, Kaunas University of Technology, LT-51367 Kaunas, Lithuania
| | - Daniel Pleissner
- Sustainable Chemistry (Resource Efciency), Institute of Sustainable and Environmental Chemistry, Leuphana University of Lüneburg, Universitätsallee 1, C13.203, 21335 Luneburg, Germany;
- Institute for Food and Environmental Research (ILU), Papendorfer Weg 3, 14806 Bad Belzig, Germany
| | - Agris Pentjuss
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (K.B.); (R.M.); (M.R.B.); (I.S.); (K.S.); (S.P.); (A.P.); (M.G.); (U.K.)
| | - Mara Grube
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (K.B.); (R.M.); (M.R.B.); (I.S.); (K.S.); (S.P.); (A.P.); (M.G.); (U.K.)
| | - Uldis Kalnenieks
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (K.B.); (R.M.); (M.R.B.); (I.S.); (K.S.); (S.P.); (A.P.); (M.G.); (U.K.)
| | - Egils Stalidzans
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia; (K.B.); (R.M.); (M.R.B.); (I.S.); (K.S.); (S.P.); (A.P.); (M.G.); (U.K.)
- Biotehniskais Centrs AS, Dzerbenes Street 27, LV-1006 Riga, Latvia;
- Correspondence: ; Tel.: +371-29575510
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Villaverde AF, Pathirana D, Fröhlich F, Hasenauer J, Banga JR. A protocol for dynamic model calibration. Brief Bioinform 2022; 23:bbab387. [PMID: 34619769 PMCID: PMC8769694 DOI: 10.1093/bib/bbab387] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/06/2021] [Accepted: 08/29/2021] [Indexed: 12/23/2022] Open
Abstract
Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.
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Affiliation(s)
- Alejandro F Villaverde
- Universidade de Vigo, Department of Systems Engineering & Control, Vigo 36310, Galicia, Spain
| | - Dilan Pathirana
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn 53115, Germany
| | - Fabian Fröhlich
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany
| | - Jan Hasenauer
- Center for Mathematics, Technische Universität München, Garching 85748, Germany
- Harvard Medical School, Cambridge, MA 02115, USA
| | - Julio R Banga
- Bioprocess Engineering Group, IIM-CSIC, Vigo 36208, Galicia, Spain
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Lao-Martil D, Verhagen KJA, Schmitz JPJ, Teusink B, Wahl SA, van Riel NAW. Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities. Metabolites 2022; 12:74. [PMID: 35050196 PMCID: PMC8779790 DOI: 10.3390/metabo12010074] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 11/23/2022] Open
Abstract
Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.
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Affiliation(s)
- David Lao-Martil
- Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 5, 5612 AE Eindhoven, The Netherlands;
| | - Koen J. A. Verhagen
- Lehrstuhl für Bioverfahrenstechnik, FAU Erlangen-Nürnberg, 91052 Erlangen, Germany; (K.J.A.V.); (S.A.W.)
| | - Joep P. J. Schmitz
- DSM Biotechnology Center, Alexander Fleminglaan 1, 2613 AX Delft, The Netherlands;
| | - Bas Teusink
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands;
| | - S. Aljoscha Wahl
- Lehrstuhl für Bioverfahrenstechnik, FAU Erlangen-Nürnberg, 91052 Erlangen, Germany; (K.J.A.V.); (S.A.W.)
| | - Natal A. W. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 5, 5612 AE Eindhoven, The Netherlands;
- Amsterdam University Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
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Modeling fixed bed bioreactors for isopropanol and butanol production using Clostridium beijerinckii DSM 6423 immobilized on polyurethane foams. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Hong Y, Zeng AP. Biosynthesis Based on One-Carbon Mixotrophy. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2022; 180:351-371. [DOI: 10.1007/10_2021_198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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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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Chen Y, Liu X, Anderson JYL, Naik HM, Dhara VG, Chen X, Harris GA, Betenbaugh MJ. A genome-scale nutrient minimization forecast algorithm for controlling essential amino acid levels in CHO cell cultures. Biotechnol Bioeng 2021; 119:435-451. [PMID: 34811743 DOI: 10.1002/bit.27994] [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: 08/18/2021] [Revised: 10/26/2021] [Accepted: 11/13/2021] [Indexed: 11/09/2022]
Abstract
Mammalian cell culture processes rely heavily on empirical knowledge in which process control remains a challenge due to the limited characterization/understanding of cell metabolism and inability to predict the cell behaviors. This study facilitates control of Chinese hamster ovary (CHO) processes through a forecast-based feeding approach that predicts multiple essential amino acids levels in the culture from easily acquired viable cell density data. Multiple cell growth behavior forecast extrapolation approaches are considered with logistic curve fitting found to be the most effective. Next, the nutrient-minimized CHO genome-scale model is combined with the growth forecast model to generate essential amino acid forecast profiles of multiple CHO batch cultures. Comparison of the forecast with the measurements suggests that this algorithm can accurately predict the concentration of most essential amino acids from cell density measurement with error mitigated by incorporating off-line amino acids concentration measurements. Finally, the forecast algorithm is applied to CHO fed-batch cultures to support amino acid feeding control to control the concentration of essential amino acids below 1-2 mM for lysine, leucine, and valine as a model over a 9-day fed batch culture while maintaining comparable growth behavior to an empirical-based culture. In turn, glycine production was elevated, alanine reduced and lactate production slightly lower in control cultures due to metabolic shifts in branched-chain amino acid degradation. With the advantage of requiring minimal measurement inputs while providing valuable and in-advance information of the system based on growth measurements, this genome model-based amino acid forecast algorithm represent a powerful and cost-effective tool to facilitate enhanced control over CHO and other mammalian cell-based bioprocesses.
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Affiliation(s)
- Yiqun Chen
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Xiao Liu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Harnish Mukesh Naik
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Venkata Gayatri Dhara
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Xiaolu Chen
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Glenn A Harris
- Research and Development, 908 Devices Inc., Boston, Massachusetts, USA
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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Marzo C, Díaz AB, Caro I, Blandino A. Valorisation of fungal hydrolysates of exhausted sugar beet pulp for lactic acid production. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:4108-4117. [PMID: 33368320 DOI: 10.1002/jsfa.11046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/24/2020] [Accepted: 12/26/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND Exhausted sugar beet pulp pellets (ESBPP) were used as raw material for lactic acid (LA) fermentation. The enzymatic hydrolysis of ESBPP was performed with the solid obtained after the fungal solid-state fermentation of ESBPP as a source of hydrolytic enzymes. Subsequently, a medium rich in glucose and arabinose was obtained, which was used to produce LA by fermentation. For LA production, two Lactobacillus strains were assayed and the effects of the supplementation of the hydrolysate with a nitrogen source and the mode of pH regulation of the fermentation were investigated. Moreover, a kinetic model for LA fermentation by Lactobacillus plantarum of ESBPP hydrolysates was developed. RESULTS L. plantarum produced a LA concentration 34% higher than that produced by L. casei. The highest LA concentration (30 g L-1 ) was obtained with L. plantarum when the hydrolysate was supplemented with 5 g L-1 yeast extract and the pH was controlled with CaCO3 . The concentration of acetic acid differed depending on the concentration of CaCO3 added, producing its maximum value with 27 g L-1 CaCO3 . The proposed kinetic model was able to predict the evolution of substrates and products depending on the variation of the pH in the hydrolysate, according to the amount of CaCO3 added. CONCLUSIONS ESBPP can be revalorised to produce LA. A pure LA stream or a mixture of LA and acetic acid, depending on the pH control method of the fermentation, can be produced. Thus, this control is of great interest depending on the destination of the effluent. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Cristina Marzo
- Department of Chemical Engineering and Food Technology, Faculty of Sciences, IVAGRO, University of Cádiz, Puerto Real, Spain
| | - Ana Belén Díaz
- Department of Chemical Engineering and Food Technology, Faculty of Sciences, IVAGRO, University of Cádiz, Puerto Real, Spain
| | - Ildefonso Caro
- Department of Chemical Engineering and Food Technology, Faculty of Sciences, IVAGRO, University of Cádiz, Puerto Real, Spain
| | - Ana Blandino
- Department of Chemical Engineering and Food Technology, Faculty of Sciences, IVAGRO, University of Cádiz, Puerto Real, Spain
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Khaleghi MK, Savizi ISP, Lewis NE, Shojaosadati SA. Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters. Biotechnol J 2021; 16:e2100212. [PMID: 34390201 DOI: 10.1002/biot.202100212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 08/10/2021] [Accepted: 08/11/2021] [Indexed: 11/06/2022]
Abstract
Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mohammad Karim Khaleghi
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Iman Shahidi Pour Savizi
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, USA.,Department of Pediatrics, University of California, San Diego, USA
| | - Seyed Abbas Shojaosadati
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
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Wiechert W, Nöh K. Quantitative Metabolic Flux Analysis Based on Isotope Labeling. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Improvements in Conventional Modeling Practices for Effective Simulation and Understanding of Microalgal Growth in Photobioreactors: an Experimental Study. BIOTECHNOL BIOPROC E 2021. [DOI: 10.1007/s12257-020-0293-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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41
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Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches. Processes (Basel) 2021. [DOI: 10.3390/pr9020322] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.
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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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Xia J, Wang G, Fan M, Chen M, Wang Z, Zhuang Y. Understanding the scale-up of fermentation processes from the viewpoint of the flow field in bioreactors and the physiological response of strains. Chin J Chem Eng 2021. [DOI: 10.1016/j.cjche.2020.12.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Patra P, Das M, Kundu P, Ghosh A. Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts. Biotechnol Adv 2021; 47:107695. [PMID: 33465474 DOI: 10.1016/j.biotechadv.2021.107695] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 12/14/2020] [Accepted: 01/09/2021] [Indexed: 12/14/2022]
Abstract
Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using 13C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.
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Affiliation(s)
- Pradipta Patra
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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Model Parameterization with Quantitative Proteomics: Case Study with Trehalose Metabolism in Saccharomyces cerevisiae. Processes (Basel) 2021. [DOI: 10.3390/pr9010139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
When Saccharomyces cerevisiae undergoes heat stress it stimulates several changes that are necessary for its survival, notably in carbon metabolism. Notable changes include increase in trehalose production and glycolytic flux. The increase in glycolytic flux has been postulated to be due to the regulatory effects in upper glycolysis, but this has not been confirmed. Additionally, trehalose is a useful industrial compound for its protective properties. A model of trehalose metabolism in S. cerevisiae was constructed using Convenient Modeller, a software that uses a combination of convenience kinetics and a genetic algorithm. The model was parameterized with quantitative omics under standard conditions and validated using data collected under heat stress conditions. The completed model was used to show that feedforward activation of pyruvate kinase by fructose 1,6-bisphosphate during heat stress contributes to the increase in metabolic flux. We were also able to demonstrate in silico that overexpression of enzymes involved in production and degradation of trehalose can lead to higher trehalose yield in the cell. By integrating quantitative proteomics with metabolic modelling, we were able to confirm that the flux increase in trehalose metabolic pathways during heat stress is due to regulatory effects and not purely changes in enzyme expression. The overexpression of enzymes involved in trehalose metabolism is a potential approach to be exploited for trehalose production without need for increasing temperature.
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A multi-enzyme strategy for the production of a highly valuable lactonized statin side-chain precursor. Chem Eng Res Des 2020. [DOI: 10.1016/j.cherd.2020.09.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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47
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Guermonprez C, Declercq L, Decaux G, Grimaud JA. Safety and efficacy of a novel home-use device for light-potentiated (LED) skin treatment. JOURNAL OF BIOPHOTONICS 2020; 13:e202000230. [PMID: 32949447 DOI: 10.1002/jbio.202000230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 07/17/2020] [Accepted: 07/28/2020] [Indexed: 06/11/2023]
Abstract
Skin structure and function results from a dynamic interplay between dermal and epidermal cell types. Optimizing skin health through an effective and long-lasting skin care regime therefore requires a global approach, encompassing various mechanisms to stimulate this interplay beyond the action scope of a classical topical solution. This study evaluates the impact of a novel home-use device combining a topical serum, light-emitting diodes and massage on the clinical signs of extrinsic skin aging. The innovative principle relies on potentiating the effect of active ingredients contained in the topical serum with visible and near infra-red photons to prevent extracellular matrix degradation and promote its reconstruction. After in vitro and ex vivo investigations, a clinical study assessed the safety and efficacy of a daily treatment with the home-use device for 28 days. A significant increases in skin density and radiance while reducing the wrinkles was obtained with no side effects.
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Affiliation(s)
| | | | | | - Jean-Alexis Grimaud
- LightInDerm R&D, Hôpital Cochin, Paris, France
- Matriscience, Hôpital Cochin, Paris, France
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Balasubramanian S, Chandrasekran P, Yesudhas AJR, Ganapathyraman P, Eiteman MA, Subramanian R. In vivo interpretation of model predicted inhibition in acrylate pathway engineered
Lactococcus lactis. Biotechnol Bioeng 2020; 117:3785-3798. [DOI: 10.1002/bit.27517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 07/13/2020] [Accepted: 07/21/2020] [Indexed: 11/08/2022]
Affiliation(s)
| | | | | | | | - Mark A. Eiteman
- School of Chemical, Materials and Biomedical Engineering University of Georgia Athens Georgia
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Predicting By-Product Gradients of Baker’s Yeast Production at Industrial Scale: A Practical Simulation Approach. Processes (Basel) 2020. [DOI: 10.3390/pr8121554] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.
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Ortega-Quintana FA, Trujillo-Roldán MA, Botero-Castro H, Alvarez H. Modeling the interaction between the central carbon metabolism of Escherichia coli and bioreactor culture media. Biochem Eng J 2020. [DOI: 10.1016/j.bej.2020.107753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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