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Phenotype-centric modeling for rational metabolic engineering.
Valderrama-Gómez, Miguel Á; Savageau, Michael A.
Afiliación
  • Valderrama-Gómez MÁ; Department of Microbiology and Molecular Genetics, University of California, Davis. One Shields Avenue, Davis, CA, 95616, USA. Electronic address: mvalderr@amgen.com.
  • Savageau MA; Department of Microbiology and Molecular Genetics, University of California, Davis. One Shields Avenue, Davis, CA, 95616, USA; Department of Biomedical Engineering, University of California, Davis. One Shields Avenue, Davis, CA, 95616, USA. Electronic address: masavageau@ucdavis.edu.
Metab Eng ; 72: 365-375, 2022 07.
Article en En | MEDLINE | ID: mdl-35537663
ABSTRACT
Phenotype-centric modeling enables a paradigm shift in the analysis of mechanistic models. It brings the focus to a network's biochemical phenotypes and their relationship with measurable traits (e.g., product yields, system dynamics, signal amplification factors, etc.) and away from computationally intensive simulation-centric modeling. Here, we explore applications of this new modeling strategy in the field of rational metabolic engineering using the amorphadiene biosynthetic network as a case study. This network has previously been studied using a mechanistic model and the simulation-centric strategy, and thus provides an excellent means to compare and contrast results obtained from these two very different strategies. We show that the phenotype-centric strategy, without values for the parameters, not only identifies beneficial intervention strategies obtained with the simulation-centric strategy, but it also provides an understanding of the mechanistic context for the validity of these predictions. Additionally, we propose a set of hypothetical strains with the potential to outperform reported production strains and to enhance the mechanistic understanding of the amorphadiene biosynthetic network. Further, we identify the landscape of possible intervention strategies for the given model. We believe that phenotype-centric modeling can advance the field of rational metabolic engineering by enabling the development of next generation kinetics-based algorithms and methods that do not rely on a priori knowledge of kinetic parameters but allow a structured, global analysis of system design in the parameter space.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Ingeniería Metabólica / Modelos Biológicos Tipo de estudio: Prognostic_studies Idioma: En Revista: Metab Eng Asunto de la revista: ENGENHARIA BIOMEDICA / METABOLISMO Año: 2022 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Ingeniería Metabólica / Modelos Biológicos Tipo de estudio: Prognostic_studies Idioma: En Revista: Metab Eng Asunto de la revista: ENGENHARIA BIOMEDICA / METABOLISMO Año: 2022 Tipo del documento: Article