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Advances in genome-scale metabolic models of industrially important fungi.
Han, Yichao; Tafur Rangel, Albert; Pomraning, Kyle R; Kerkhoven, Eduard J; Kim, Joonhoon.
Afiliación
  • Han Y; Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Agile BioFoundry, Department of Energy, Emeryville, CA, USA.
  • Tafur Rangel A; Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
  • Pomraning KR; Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Agile BioFoundry, Department of Energy, Emeryville, CA, USA.
  • Kerkhoven EJ; Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark; SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
  • Kim J; Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Agile BioFoundry, Department of Energy, Emeryville, CA, USA; Joint BioEnergy Institute, Department of Energy, Emeryville, CA, USA. Electronic address: joonhoon.kim@pnnl.gov.
Curr Opin Biotechnol ; 84: 103005, 2023 Dec.
Article en En | MEDLINE | ID: mdl-37797483
ABSTRACT
Many fungal species have been used industrially for production of biofuels and bioproducts. Developing strains with better performance in biomanufacturing contexts requires a systematic understanding of cellular metabolism. Genome-scale metabolic models (GEMs) offer a comprehensive view of interconnected pathways and a mathematical framework for downstream analysis. Recently, GEMs have been developed or updated for several industrially important fungi. Some of them incorporate enzyme constraints, enabling improved predictions of cell states and proteome allocation. Here, we provide an overview of these newly developed GEMs and computational methods that facilitate construction of enzyme-constrained GEMs and utilize flux predictions from GEMs. Furthermore, we highlight the pivotal roles of these GEMs in iterative design-build-test-learn cycles, ultimately advancing the field of fungal biomanufacturing.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Proteoma / Modelos Biológicos Tipo de estudio: Prognostic_studies Idioma: En Revista: Curr Opin Biotechnol Asunto de la revista: BIOTECNOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Proteoma / Modelos Biológicos Tipo de estudio: Prognostic_studies Idioma: En Revista: Curr Opin Biotechnol Asunto de la revista: BIOTECNOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos