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Park SY, Choi DH, Song J, Lakshmanan M, Richelle A, Yoon S, Kontoravdi C, Lewis NE, Lee DY. Driving towards digital biomanufacturing by CHO genome-scale models. Trends Biotechnol 2024; 42:1192-1203. [PMID: 38548556 DOI: 10.1016/j.tibtech.2024.03.001] [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: 01/01/2024] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 05/20/2024]
Abstract
Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms.
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Affiliation(s)
- Seo-Young Park
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Dong-Hyuk Choi
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Jinsung Song
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Meiyappan Lakshmanan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, and Centre for Integrative Biology and Systems Medicine (IBSE), Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Anne Richelle
- Sartorius Corporate Research, Avenue Ariane 5, 1200 Brussels, Belgium
| | - Seongkyu Yoon
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA 01850, USA
| | - Cleo Kontoravdi
- Department of Chemical Engineering and Chemical Technology, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
| | - Nathan E Lewis
- Departments of Pediatrics and Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea.
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2
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Liyanaarachchi VC, Nishshanka GKSH, Nimarshana PHV, Chang JS, Ariyadasa TU, Nagarajan D. Modeling of astaxanthin biosynthesis via machine learning, mathematical and metabolic network modeling. Crit Rev Biotechnol 2024; 44:996-1017. [PMID: 37587012 DOI: 10.1080/07388551.2023.2237183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/04/2023] [Accepted: 06/17/2023] [Indexed: 08/18/2023]
Abstract
Natural astaxanthin is synthesized by diverse organisms including: bacteria, fungi, microalgae, and plants involving complex cellular processes, which depend on numerous interrelated parameters. Nonetheless, existing knowledge regarding astaxanthin biosynthesis and the conditions influencing astaxanthin accumulation is fairly limited. Thus, manipulation of the growth conditions to achieve desired biomass and astaxanthin yields can be a complicated process requiring cost-intensive and time-consuming experiment-based research. As a potential solution, modeling and simulation of biological systems have recently emerged, allowing researchers to predict/estimate astaxanthin production dynamics in selected organisms. Moreover, mathematical modeling techniques would enable further optimization of astaxanthin synthesis in a shorter period of time, ultimately contributing to a notable reduction in production costs. Thus, the present review comprehensively discusses existing mathematical modeling techniques which simulate the bioaccumulation of astaxanthin in diverse organisms. Associated challenges, solutions, and future perspectives are critically analyzed and presented.
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Affiliation(s)
| | | | - P H Viraj Nimarshana
- Department of Mechanical Engineering, Faculty of Engineering, University of Moratuwa, Moratuwa, Sri Lanka
| | - Jo-Shu Chang
- Department of Chemical Engineering, National Cheng Kung University, Tainan, Taiwan
- Department of Chemical and Materials Engineering, Tunghai University, Taichung, Taiwan
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung, Taiwan
- Department of Chemical Engineering and Materials Science, Yuan Ze University, Chung-Li, Taiwan
| | - Thilini U Ariyadasa
- Department of Chemical and Process Engineering, Faculty of Engineering, University of Moratuwa, Moratuwa, Sri Lanka
| | - Dillirani Nagarajan
- Department of Chemical Engineering, National Cheng Kung University, Tainan, Taiwan
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3
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Zehetner L, Széliová D, Kraus B, Hernandez Bort JA, Zanghellini J. Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways. PLoS Comput Biol 2024; 20:e1012236. [PMID: 38913731 PMCID: PMC11226097 DOI: 10.1371/journal.pcbi.1012236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 07/05/2024] [Accepted: 06/07/2024] [Indexed: 06/26/2024] Open
Abstract
Genome-scale metabolic models (GSMMs) offer a holistic view of biochemical reaction networks, enabling in-depth analyses of metabolism across species and tissues in multiple conditions. However, comparing GSMMs Against each other poses challenges as current dimensionality reduction algorithms or clustering methods lack mechanistic interpretability, and often rely on subjective assumptions. Here, we propose a new approach utilizing logisitic principal component analysis (LPCA) that efficiently clusters GSMMs while singling out mechanistic differences in terms of reactions and pathways that drive the categorization. We applied LPCA to multiple diverse datasets, including GSMMs of 222 Escherichia-strains, 343 budding yeasts (Saccharomycotina), 80 human tissues, and 2943 Firmicutes strains. Our findings demonstrate LPCA's effectiveness in preserving microbial phylogenetic relationships and discerning human tissue-specific metabolic profiles, exhibiting comparable performance to traditional methods like t-distributed stochastic neighborhood embedding (t-SNE) and Jaccard coefficients. Moreover, the subsystems and associated reactions identified by LPCA align with existing knowledge, underscoring its reliability in dissecting GSMMs and uncovering the underlying drivers of separation.
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Affiliation(s)
- Leopold Zehetner
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
- Vienna Doctoral School in Chemistry (DoSChem), University of Vienna, Vienna, Austria
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Diana Széliová
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Barbara Kraus
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Juan A. Hernandez Bort
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
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4
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Kinet R, Richelle A, Colle M, Demaegd D, von Stosch M, Sanders M, Sehrt H, Delvigne F, Goffin P. Giving the cells what they need when they need it: Biosensor-based feeding control. Biotechnol Bioeng 2024; 121:1271-1283. [PMID: 38258490 DOI: 10.1002/bit.28657] [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: 07/28/2023] [Revised: 12/11/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024]
Abstract
"Giving the cells exactly what they need, when they need it" is the core idea behind the proposed bioprocess control strategy: operating bioprocess based on the physiological behavior of the microbial population rather than exclusive monitoring of environmental parameters. We are envisioning to achieve this through the use of genetically encoded biosensors combined with online flow cytometry (FCM) to obtain a time-dependent "physiological fingerprint" of the population. We developed a biosensor based on the glnA promoter (glnAp) and applied it for monitoring the nitrogen-related nutritional state of Escherichia coli. The functionality of the biosensor was demonstrated through multiple cultivation runs performed at various scales-from microplate to 20 L bioreactor. We also developed a fully automated bioreactor-FCM interface for on-line monitoring of the microbial population. Finally, we validated the proposed strategy by performing a fed-batch experiment where the biosensor signal is used as the actuator for a nitrogen feeding feedback control. This new generation of process control, -based on the specific needs of the cells, -opens the possibility of improving process development on a short timescale and therewith, the robustness and performance of fermentation processes.
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Affiliation(s)
| | | | | | | | | | | | - Hannah Sehrt
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Frank Delvigne
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Philippe Goffin
- Molecular and Cellular Biology, University of Brussels, Brussels, Belgium
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Oarga A, Bannerman BP, Júlvez J. CONTRABASS: exploiting flux constraints in genome-scale models for the detection of vulnerabilities. Bioinformatics 2023; 39:7000333. [PMID: 36692133 PMCID: PMC9907045 DOI: 10.1093/bioinformatics/btad053] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 11/16/2022] [Accepted: 01/23/2023] [Indexed: 01/25/2023] Open
Abstract
MOTIVATION Despite the fact that antimicrobial resistance is an increasing health concern, the pace of production of new drugs is slow due to the high cost and uncertain success of the process. The development of high-throughput technologies has allowed the integration of biological data into detailed genome-scale models of multiple organisms. Such models can be exploited by means of computational methods to identify system vulnerabilities such as chokepoint reactions and essential reactions. These vulnerabilities are appealing drug targets that can lead to novel drug developments. However, the current approach to compute these vulnerabilities is only based on topological data and ignores the dynamic information of the model. This can lead to misidentified drug targets. RESULTS This work computes flux constraints that are consistent with a certain growth rate of the modelled organism, and integrates the computed flux constraints into the model to improve the detection of vulnerabilities. By exploiting these flux constraints, we are able to obtain a directionality of the reactions of metabolism consistent with a given growth rate of the model, and consequently, a more realistic detection of vulnerabilities can be performed. Several sets of reactions that are system vulnerabilities are defined and the relationships among them are studied. The approach for the detection of these vulnerabilities has been implemented in the Python tool CONTRABASS. Such tool, for which an online web server has also been implemented, computes flux constraints and generates a report with the detected vulnerabilities. AVAILABILITY AND IMPLEMENTATION CONTRABASS is available as an open source Python package at https://github.com/openCONTRABASS/CONTRABASS under GPL-3.0 License. An online web server is available at http://contrabass.unizar.es. SUPPLEMENTARY INFORMATION A glossary of terms are available at Bioinformatics online.
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Affiliation(s)
- Alexandru Oarga
- Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza 50018, Spain
| | - Bridget P Bannerman
- Lucy Cavendish College, Biological Sciences, University of Cambridge, Cambridge CB3 0BU, UK.,Science Resources Foundation, Health Unit, London EC1V 2NX, UK
| | - Jorge Júlvez
- Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza 50018, Spain
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Cuperlovic-Culf M, Nguyen-Tran T, Bennett SAL. Machine Learning and Hybrid Methods for Metabolic Pathway Modeling. Methods Mol Biol 2023; 2553:417-439. [PMID: 36227553 DOI: 10.1007/978-1-0716-2617-7_18] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.
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Affiliation(s)
- Miroslava Cuperlovic-Culf
- Digital Technologies Research Centre, National Research Council of Canada, Ottawa, ON, Canada.
- Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, ON, Canada.
| | - Thao Nguyen-Tran
- Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, ON, Canada
- Neural Regeneration Laboratory, Ottawa Institute of Systems Biology, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
- Department of Chemistry and Biomolecular Sciences, Centre for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, Canada
| | - Steffany A L Bennett
- Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, ON, Canada
- Neural Regeneration Laboratory, Ottawa Institute of Systems Biology, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
- Department of Chemistry and Biomolecular Sciences, Centre for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, Canada
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7
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Dzianach PA, Pérez-Reche FJ, Strachan NJC, Forbes KJ, Dykes GA. The Use of Interdisciplinary Approaches to Understand the Biology of Campylobacter jejuni. Microorganisms 2022; 10:2498. [PMID: 36557751 PMCID: PMC9786101 DOI: 10.3390/microorganisms10122498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Campylobacter jejuni is a bacterial pathogen recognised as a major cause of foodborne illness worldwide. While Campylobacter jejuni generally does not grow outside its host, it can survive outside of the host long enough to pose a health concern. This review presents an up-to-date description and evaluation of biological, mathematical, and statistical approaches used to understand the behaviour of this foodborne pathogen and suggests future avenues which can be explored. Specifically, the incorporation of mathematical modelling may aid the understanding of C. jejuni biofilm formation both outside and inside the host. Predictive studies may be improved by the introduction of more standardised protocols for assessments of disinfection methods and by assessment of novel physical disinfection strategies as well as assessment of the efficiency of plant extracts on C. jejuni eradication. A full description of the metabolic pathways of C. jejuni, which is needed for the successful application of metabolic models, is yet to be achieved. Finally, a shift from animal models (except for those that are a source of human campylobacteriosis) to human-specific data may be made possible due to recent technological advancements, and this may lead to more accurate predictions of human infections.
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Affiliation(s)
- Paulina A. Dzianach
- Geospatial Health and Development, Telethon Kids Institute, Perth 6009, Australia
| | | | - Norval J. C. Strachan
- School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3FX, UK
| | - Ken J. Forbes
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB24 3FX, UK
| | - Gary A. Dykes
- School of Agriculture and Food Sciences, University of Queensland, Brisbane 4072, Australia
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8
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Arduino Soft Sensor for Monitoring Schizochytrium sp. Fermentation, a Proof of Concept for the Industrial Application of Genome-Scale Metabolic Models in the Context of Pharma 4.0. Processes (Basel) 2022. [DOI: 10.3390/pr10112226] [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
Schizochytrium sp. is a microorganism cultured for producing docosahexaenoic acid (DHA). Genome-scale metabolic modeling (GEM) is a promising technique for describing gen-protein-reactions in cells, but with still limited industrial application due to its complexity and high computation requirements. In this work, we simplified GEM results regarding the relationship between the specific oxygen uptake rate (−rO2), the specific growth rate (µ), and the rate of lipid synthesis (rL) using an evolutionary algorithm for developing a model that can be used by a soft sensor for fermentation monitoring. The soft sensor estimated the concentration of active biomass (X), glutamate (N), lipids (L), and DHA in a Schizochytrium sp. fermentation using the dissolved oxygen tension (DO) and the oxygen mass transfer coefficient (kLa) as online input variables. The soft sensor model described the biomass concentration response of four reported experiments characterized by different kLa values. The average range normalized root-mean-square error for X, N, L, and DHA were equal to 1.1, 1.3, 1.1, and 3.2%, respectively, suggesting an acceptable generalization capacity. The feasibility of implementing the soft sensor over a low-cost electronic board was successfully tested using an Arduino UNO, showing a novel path for applying GEM-based soft sensors in the context of Pharma 4.0.
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Narayanan H, Luna M, Sokolov M, Butté A, Morbidelli M. Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Cell Culture Processes. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Harini Narayanan
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Martin Luna
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | | | | | - Massimo Morbidelli
- DataHow AG, Zürichstrasse 137, 8600 Dübendorf, Switzerland
- Dipartimento di Chimica, Materiali e Ingegneria Chimica, Giulio Natta, Politecnico di Milano, 20131 Milano, Italy
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10
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Escandell JM, Pais DA, Carvalho SB, Vincent K, Gomes-Alves P, Alves PM. Leveraging rAAV bioprocess understanding and next generation bioanalytics development. Curr Opin Biotechnol 2022; 74:271-277. [PMID: 35007989 DOI: 10.1016/j.copbio.2021.12.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/10/2021] [Accepted: 12/19/2021] [Indexed: 12/18/2022]
Abstract
Recombinant adeno-associated (rAAV) vector-based gene therapy has been the focus of intense research driven by the safety profile and several recent clinical breakthroughs. As of April 2021, there are two rAAV-based gene therapies approved and more than two-hundred active clinical trials (approximately thirty in Phase III). However, the expected increase in demand for rAAV vectors still poses several challenges. Discussed herein are key aspects related to R&D needs and Chemistry, Manufacturing and Control (CMC) efforts required to attend this growing demand. Authors provide their perspective on strategic topics for rAAV-based therapies success: scalability and productivity; improved safety; increased process understanding combined with development of orthogonal bioanalytics that are able to identify, monitor and control Critical Quality Attributes (CQAs) during bioprocessing.
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Affiliation(s)
- Jose M Escandell
- iBET, Instituto de Biologia Experimental e Tecnológica, Apartado 12, Oeiras, 2780-901, Portugal; ITQB-NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. Da República, Oeiras, 2780-157, Portugal
| | - Daniel Am Pais
- iBET, Instituto de Biologia Experimental e Tecnológica, Apartado 12, Oeiras, 2780-901, Portugal; ITQB-NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. Da República, Oeiras, 2780-157, Portugal
| | - Sofia B Carvalho
- iBET, Instituto de Biologia Experimental e Tecnológica, Apartado 12, Oeiras, 2780-901, Portugal; ITQB-NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. Da República, Oeiras, 2780-157, Portugal
| | - Karen Vincent
- SANOFI, 49 New York Avenue, Framingham, MA 01701, USA
| | - Patrícia Gomes-Alves
- iBET, Instituto de Biologia Experimental e Tecnológica, Apartado 12, Oeiras, 2780-901, Portugal; ITQB-NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. Da República, Oeiras, 2780-157, Portugal
| | - Paula M Alves
- iBET, Instituto de Biologia Experimental e Tecnológica, Apartado 12, Oeiras, 2780-901, Portugal; ITQB-NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. Da República, Oeiras, 2780-157, Portugal.
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11
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Nogales J, Garmendia J. Bacterial metabolism and pathogenesis intimate intertwining: time for metabolic modelling to come into action. Microb Biotechnol 2022; 15:95-102. [PMID: 34672429 PMCID: PMC8719832 DOI: 10.1111/1751-7915.13942] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 09/25/2021] [Indexed: 11/26/2022] Open
Abstract
We take a snapshot of the recent understanding of bacterial metabolism and the bacterial-host metabolic interplay during infection, and highlight key outcomes and challenges for the practical implementation of bacterial metabolic modelling computational tools in the pathogenesis field.
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Affiliation(s)
- Juan Nogales
- Department of Systems BiologyCentro Nacional de BiotecnologíaCSICMadridSpain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy‐Spanish National Research Council (SusPlast‐CSIC)MadridSpain
| | - Junkal Garmendia
- Instituto de AgrobiotecnologíaConsejo Superior de Investigaciones Científicas (IdAB‐CSIC)‐Gobierno de NavarraMutilvaSpain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES)MadridSpain
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12
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Zielinski DC, Patel A, Palsson BO. The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale. Microorganisms 2020; 8:E2050. [PMID: 33371386 PMCID: PMC7767376 DOI: 10.3390/microorganisms8122050] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/07/2020] [Accepted: 12/16/2020] [Indexed: 02/06/2023] Open
Abstract
Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.
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Affiliation(s)
- Daniel Craig Zielinski
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA; (D.C.Z.); (A.P.)
| | - Arjun Patel
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA; (D.C.Z.); (A.P.)
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA; (D.C.Z.); (A.P.)
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
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