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27 results on '"Mendoza, Sebastián N."'

4. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Author

Heirendt, Laurent, Arreckx, Sylvain, Pfau, Thomas, Mendoza, Sebastián N, Richelle, Anne, Heinken, Almut, Haraldsdóttir, Hulda S, Wachowiak, Jacek, Keating, Sarah M, Vlasov, Vanja, Magnusdóttir, Stefania, Ng, Chiam Yu, Preciat, German, Žagare, Alise, Chan, Siu HJ, Aurich, Maike K, Clancy, Catherine M, Modamio, Jennifer, Sauls, John T, Noronha, Alberto, Bordbar, Aarash, Cousins, Benjamin, El Assal, Diana C, Valcarcel, Luis V, Apaolaza, Iñigo, Ghaderi, Susan, Ahookhosh, Masoud, Ben Guebila, Marouen, Kostromins, Andrejs, Sompairac, Nicolas, Le, Hoai M, Ma, Ding, Sun, Yuekai, Wang, Lin, Yurkovich, James T, Oliveira, Miguel AP, Vuong, Phan T, El Assal, Lemmer P, Kuperstein, Inna, Zinovyev, Andrei, Hinton, H Scott, Bryant, William A, Aragón Artacho, Francisco J, Planes, Francisco J, Stalidzans, Egils, Maass, Alejandro, Vempala, Santosh, Hucka, Michael, Saunders, Michael A, Maranas, Costas D, Lewis, Nathan E, Sauter, Thomas, Palsson, Bernhard Ø, Thiele, Ines, and Fleming, Ronan MT

Subjects
Information and Computing Sciences, Biochemistry and Cell Biology, Biological Sciences, Biotechnology, Genome, Metabolic Networks and Pathways, Models, Biological, Software, Systems Biology, q-bio.QM, Chemical Sciences, Medical and Health Sciences, Bioinformatics
Abstract

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Published
2019

5. Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0

Author

Heirendt, Laurent, Arreckx, Sylvain, Pfau, Thomas, Mendoza, Sebastián N., Richelle, Anne, Heinken, Almut, Haraldsdóttir, Hulda S., Wachowiak, Jacek, Keating, Sarah M., Vlasov, Vanja, Magnusdóttir, Stefania, Ng, Chiam Yu, Preciat, German, Žagare, Alise, Chan, Siu H. J., Aurich, Maike K., Clancy, Catherine M., Modamio, Jennifer, Sauls, John T., Noronha, Alberto, Bordbar, Aarash, Cousins, Benjamin, Assal, Diana C. El, Valcarcel, Luis V., Apaolaza, Iñigo, Ghaderi, Susan, Ahookhosh, Masoud, Guebila, Marouen Ben, Kostromins, Andrejs, Sompairac, Nicolas, Le, Hoai M., Ma, Ding, Sun, Yuekai, Wang, Lin, Yurkovich, James T., Oliveira, Miguel A. P., Vuong, Phan T., Assal, Lemmer P. El, Kuperstein, Inna, Zinovyev, Andrei, Hinton, H. Scott, Bryant, William A., Artacho, Francisco J. Aragón, Planes, Francisco J., Stalidzans, Egils, Maass, Alejandro, Vempala, Santosh, Hucka, Michael, Saunders, Michael A., Maranas, Costas D., Lewis, Nathan E., Sauter, Thomas, Palsson, Bernhard Ø., Thiele, Ines, and Fleming, Ronan M. T.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods.

Published
2017

8. Yeast9: A Consensus Yeast Metabolic Model Enables Quantitative Analysis of Cellular Metabolism By Incorporating Big Data

Author

Zhang, Chengyu, primary, Sánchez, Benjamín J., additional, Li, Feiran, additional, Eiden, Cheng Wei Quan, additional, Scott, William T., additional, Liebal, Ulf W., additional, Blank, Lars M., additional, Mengers, Hendrik G., additional, Anton, Mihail, additional, Rangel, Albert Tafur, additional, Mendoza, Sebastián N., additional, Zhang, Lixin, additional, Nielsen, Jens, additional, Lu, Hongzhong, additional, and Kerkhoven, Eduard J., additional

Published
2023
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13. Ethanol-Lactate Transition of Lachancea Thermotolerans Is Linked to Nitrogen Metabolism

Author

Battjes, Julius, Melkonian, Chrats, Mendoza, Sebastián N., Haver, Auke, Al-Nakeeb, Kosai, Koza, Anna, Schrubbers, Lars, Wagner, Marijke, Zeidan, Ahmad Adel, Molenaar, Douwe, Teusink, Bas, Battjes, Julius, Melkonian, Chrats, Mendoza, Sebastián N., Haver, Auke, Al-Nakeeb, Kosai, Koza, Anna, Schrubbers, Lars, Wagner, Marijke, Zeidan, Ahmad Adel, Molenaar, Douwe, and Teusink, Bas

Published
2022

14. Metabolic Modeling of Wine Fermentation at Genome Scale

Author

Mendoza, Sebastián N., Saa, Pedro A., Teusink, Bas, Agosin, Eduardo, Cortassa, Sonia, Aon, Miguel A., Systems Bioinformatics, AIMMS, Cortassa, Sonia, and Aon, Miguel A.

Subjects
Constraint-based metabolic modeling, Genome-scale network reconstruction, Wine fermentation, Metabolic flux, food and beverages, Saccharomyces cerevisiae, Oenococcus oeni
Abstract

Wine fermentation is an ancient biotechnological process mediated by different microorganisms such as yeast and bacteria. Understanding of the metabolic and physiological phenomena taking place during this process can be now attained at a genome scale with the help of metabolic models. In this chapter, we present a detailed protocol for modeling wine fermentation using genome-scale metabolic models. In particular, we illustrate how metabolic fluxes can be computed, optimized and interpreted, for both yeast and bacteria under winemaking conditions. We also show how nutritional requirements can be determined and simulated using these models in relevant test cases. This chapter introduces fundamental concepts and practical steps for applying flux balance analysis in wine fermentation, and as such, it is intended for a broad microbiology audience as well as for practitioners in the metabolic modeling field.

Published
2022

17. Metabolic modeling of fungi

Author

Mendoza, Sebastián N., Calhoun, Sara, Teusink, Bas, Aguilar-Pontes, María Victoria, Zaragoza, Óscar, Casadevall, Arturo, Systems Bioinformatics, AIMMS, Zaragoza, Óscar, and Casadevall, Arturo

Subjects
Computer science, Process (engineering), Physics::Instrumentation and Detectors, Metabolic modeling, Biochemical engineering
Abstract

Fungi have received special interest from the biotechnological sector focused on the production of active biomolecules and strain engineering. Genome-scale metabolic models (GEMs) are used to understand and improve their metabolism. GEMs can be obtained using computational methods, but they are susceptible to errors. Here, we describe the process to reconstruct a GEM and discuss several methodologies for analysis and validation of the model. We review different applications and explore the latest advances in GEM reconstruction to ease the process. With the new developments, we will be able to reconstruct and analyze high-quality GEMs for non-model species.

Published
2021

18. A Multiphase Multiobjective Dynamic Genome-Scale Model Shows Different Redox Balancing among Yeast Species of the Saccharomyces Genus in Fermentation

Author

Henriques, David, Minebois, Romain, Mendoza, Sebastián N., Macías, L. G., Pérez-Torrado, Roberto, Barrio, E., Teusink, Bas, Querol, Amparo, Baslsa-Canto, Eva, Henriques, David, Minebois, Romain, Mendoza, Sebastián N., Macías, L. G., Pérez-Torrado, Roberto, Barrio, E., Teusink, Bas, Querol, Amparo, and Baslsa-Canto, Eva

Abstract

Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories

Published
2021

20. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Author

Universidad de Alicante. Departamento de Matemáticas, Heirendt, Laurent, Arreckx, Sylvain, Pfau, Thomas, Mendoza, Sebastián N., Richelle, Anne, Heinken, Almut, Haraldsdóttir, Hulda S., Wachowiak, Jacek, Keating, Sarah M., Vlasov, Vanja, Magnusdóttir, Stefania, Ng, Chiam Yu, Preciat, German, Žagare, Alise, Chan, Siu H.J., Aurich, Maike K., Clancy, Catherine M., Modamio, Jennifer, Sauls, John T., Noronha, Alberto, Bordbar, Aarash, Cousins, Benjamin, El Assal, Diana C., Valcarcel, Luis V., Apaolaza, Iñigo, Ghaderi, Susan, Ahookhosh, Masoud, Ben Guebila, Marouen, Kostromins, Andrejs, Sompairac, Nicolas, Le, Hoai M., Ma, Ding, Sun, Yuekai, Wang, Lin, Yurkovich, James T., Oliveira, Miguel A.P., Vuong, Phan T., El Assal, Lemmer P., Kuperstein, Inna, Zinovyev, Andrei, Hinton, H. Scott, Bryant, William A., Aragón Artacho, Francisco Javier, Planes, Francisco J., Stalidzans, Egils, Maass, Alejandro, Vempala, Santosh, Hucka, Michael, Saunders, Michael A., Maranas, Costas D., Lewis, Nathan E., Sauter, Thomas, Palsson, Bernhard Ø., Thiele, Ines, Fleming, Ronan M.T., Universidad de Alicante. Departamento de Matemáticas, Heirendt, Laurent, Arreckx, Sylvain, Pfau, Thomas, Mendoza, Sebastián N., Richelle, Anne, Heinken, Almut, Haraldsdóttir, Hulda S., Wachowiak, Jacek, Keating, Sarah M., Vlasov, Vanja, Magnusdóttir, Stefania, Ng, Chiam Yu, Preciat, German, Žagare, Alise, Chan, Siu H.J., Aurich, Maike K., Clancy, Catherine M., Modamio, Jennifer, Sauls, John T., Noronha, Alberto, Bordbar, Aarash, Cousins, Benjamin, El Assal, Diana C., Valcarcel, Luis V., Apaolaza, Iñigo, Ghaderi, Susan, Ahookhosh, Masoud, Ben Guebila, Marouen, Kostromins, Andrejs, Sompairac, Nicolas, Le, Hoai M., Ma, Ding, Sun, Yuekai, Wang, Lin, Yurkovich, James T., Oliveira, Miguel A.P., Vuong, Phan T., El Assal, Lemmer P., Kuperstein, Inna, Zinovyev, Andrei, Hinton, H. Scott, Bryant, William A., Aragón Artacho, Francisco Javier, Planes, Francisco J., Stalidzans, Egils, Maass, Alejandro, Vempala, Santosh, Hucka, Michael, Saunders, Michael A., Maranas, Costas D., Lewis, Nathan E., Sauter, Thomas, Palsson, Bernhard Ø., Thiele, Ines, and Fleming, Ronan M.T.

Abstract

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Published
2019

21. Traceability, reproducibility and wiki-exploration for 'à-la-carte' reconstructions of genome-scale metabolic models

Author

Aite, Méziane, Chevallier, Marie, Frioux, Clémence, Trottier, Camille, Got, Jeanne, Cortés, María Paz, Mendoza, Sebastián N., Carrier, Grégory, Dameron, Olivier, Guillaudeux, Nicolas, Latorre, Mauricio, Loira, Nicolás, Markov, Gabriel V., Maass, Alejandro, Siegel, Anne, Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Center for Mathematical Modelling - Centro de Modelamiento Matematico [Santiago] (CMM), Universidad de Chile = University of Chile [Santiago] (UCHILE)-Centre National de la Recherche Scientifique (CNRS), Centro de Modelamiento Matemático [Santiago] (CMM), Universidad de Santiago de Chile [Santiago] (USACH)-Centre National de la Recherche Scientifique (CNRS), Physiologie et biotechnologie des Algues (PBA), Biotechnologies et Ressources Marines (BRM), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Instituto de Ciencias de la Ingenieria (ICIn - UOH), Universidad de O'Higgins (UOH), Instituto de Nutricion y Tecnologia de los Alimentos [Santiago] (INTA), Universidad de Chile = University of Chile [Santiago] (UCHILE), Laboratoire de Biologie Intégrative des Modèles Marins (LBI2M), Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre de Modélisation Mathématique / Centro de Modelamiento Matemático (CMM), Centre National de la Recherche Scientifique (CNRS), ANR-10-BTBR-04, Agence Nationale de la Recherche, Project Lab Algae-In-Silico, Institut national de recherche en informatique et en automatique, 21140822, Consejo Nacional de Innovación, Ciencia y Tecnología, 11150679, Fondecyt, Dynamics, Logics and Inference for biological Systems and Sequences ( Dyliss ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -GESTION DES DONNÉES ET DE LA CONNAISSANCE ( IRISA_D7 ), Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ), Ecosystèmes, biodiversité, évolution [Rennes] ( ECOBIO ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -INEE-Observatoire des Sciences de l'Univers de Rennes ( OSUR ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire des Sciences du Numérique de Nantes ( LS2N ), Université de Nantes ( UN ) -École Centrale de Nantes ( ECN ) -Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ), Center for Mathematical Modelling - Centro de Modelamiento Matematico [Santiago] ( CMM ), University of Chile [Santiago]-Centre National de la Recherche Scientifique ( CNRS ), Centro de Modelamiento Matemático ( CMM ), Universidad de Santiago de Chile [Santiago] ( USACH ) -Centre National de la Recherche Scientifique ( CNRS ), Physiologie et biotechnologie des Algues ( PBA ), Institut Français de Recherche pour l'Exploitation de la Mer ( IFREMER ), Instituto de Ciencias de la Ingenieria ( ICIn - UOH ), Universidad de O'Higgins ( UOH ), Instituto de Nutricion y Tecnologia de los Alimentos [Santiago] ( INTA ), Universidad de Santiago de Chile [Santiago] ( USACH ), Laboratoire de Biologie Intégrative des Modèles Marins ( LBI2M ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Centre de Modélisation Mathématique / Centro de Modelamiento Matemático ( CMM ), Centre National de la Recherche Scientifique ( CNRS ), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Universidad de Santiago de Chile [Santiago] (USACH), and Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)

Subjects
Computer and Information Sciences, Algae, Databases, Factual, QH301-705.5, Enzyme Metabolism, Information Storage and Retrieval, Research and Analysis Methods, Biochemistry, Antioxidants, Metabolic Networks, Database and Informatics Methods, Genetics, Metabolites, Microalgae, [INFO]Computer Science [cs], Biology (General), Enzyme Chemistry, lcsh:QH301-705.5, Internet, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, Organisms, Biology and Life Sciences, Computational Biology, Eukaryota, Reproducibility of Results, Genomics, Plants, Models, Theoretical, Genomic Databases, Genome Analysis, Biological Databases, Metabolism, lcsh:Biology (General), Enzymology, Metabolic Pathways, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Network Analysis, Metabolic Networks and Pathways, Research Article
Abstract

Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from “à la carte” pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway., Author summary Genome-scale metabolic models describe an organism’s metabolism. Building good models requires the integration of all relevant available information, obtained by exploring different data types and biological databases. This process is not straightforward and choices are made along the way, for example, which data is analyzed, with what tools. It matters that all reconstruction steps are well documented so that models can be fully exploited by the community. Having this metadata allows other researchers to reproduce, improve or reuse a model as a blueprint to create new ones. Sadly, this information is usually scattered and its proper distribution is the exception rather than the norm when using “à la carte” pipelines that combine main platforms and individual tools. We created a platform for “à la carte” metabolic model generation that responds to the need of transparency and data-connection in the field. It includes a battery of tools to exploit heterogeneous data through customizable pipelines. At each step, relevant information is stored, ensuring reproducibility and documentation of processes. Furthermore, exploration of models and metadata during the reconstruction process is facilitated through the automatic generation of local wikis. This view offers a user-friendly solution to iteratively explore genome-scale metabolic models produced with personalized pipelines and poorly interoperable tools. We highlight these benefits by building models for organisms with various input data. Among them, we show why the combination of heterogeneous information is necessary to elucidate specificities of Tisochrysis lutea, a eukaryotic microalga, for anti-oxidant production.

Published
2018

25. Metabolic Modeling of Wine Fermentation at Genome Scale.

Author

Mendoza SN, Saa PA, Teusink B, and Agosin E

Subjects
Bacteria genetics, Bacteria metabolism, Models, Biological, Saccharomyces cerevisiae genetics, Saccharomyces cerevisiae metabolism, Fermentation genetics, Fermentation physiology, Models, Genetic, Wine analysis, Wine microbiology
Abstract

Wine fermentation is an ancient biotechnological process mediated by different microorganisms such as yeast and bacteria. Understanding of the metabolic and physiological phenomena taking place during this process can be now attained at a genome scale with the help of metabolic models. In this chapter, we present a detailed protocol for modeling wine fermentation using genome-scale metabolic models. In particular, we illustrate how metabolic fluxes can be computed, optimized and interpreted, for both yeast and bacteria under winemaking conditions. We also show how nutritional requirements can be determined and simulated using these models in relevant test cases. This chapter introduces fundamental concepts and practical steps for applying flux balance analysis in wine fermentation, and as such, it is intended for a broad microbiology audience as well as for practitioners in the metabolic modeling field., (© 2022. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Published
2022
Full Text
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26. A Multiphase Multiobjective Dynamic Genome-Scale Model Shows Different Redox Balancing among Yeast Species of the Saccharomyces Genus in Fermentation.

Author

Henriques D, Minebois R, Mendoza SN, Macías LG, Pérez-Torrado R, Barrio E, Teusink B, Querol A, and Balsa-Canto E

Abstract

Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts' metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories. IMPORTANCE Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the Saccharomyces genus in rich medium fermentation. The study revealed that cryotolerant Saccharomyces species might use the γ-aminobutyric acid (GABA) shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model (along with the provided code) can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect nonconventional yeast species metabolism and engineering novel cell factories.

Published
2021
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27. Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models.

Author

Aite M, Chevallier M, Frioux C, Trottier C, Got J, Cortés MP, Mendoza SN, Carrier G, Dameron O, Guillaudeux N, Latorre M, Loira N, Markov GV, Maass A, and Siegel A

Subjects
Antioxidants metabolism, Microalgae genetics, Microalgae metabolism, Models, Theoretical, Reproducibility of Results, Databases, Factual, Genomics methods, Genomics standards, Information Storage and Retrieval methods, Information Storage and Retrieval standards, Internet, Metabolic Networks and Pathways genetics
Abstract

Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from "à la carte" pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway., Competing Interests: The authors have declared that no competing interests exist.

Published
2018
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