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Mechanistic Modeling of Biochemical Systems without A Priori Parameter Values Using the Design Space Toolbox v.3.0.
Valderrama-Gómez, Miguel Á; Lomnitz, Jason G; Fasani, Rick A; Savageau, Michael A.
Afiliación
  • Valderrama-Gómez MÁ; Department of Microbiology & Molecular Genetics, University of California, Davis, CA 95616, USA.
  • Lomnitz JG; VSP Global, Rancho Cordova, CA 95670, USA.
  • Fasani RA; Agilent Technologies, Santa Clara, CA 95051, USA.
  • Savageau MA; Department of Microbiology & Molecular Genetics, University of California, Davis, CA 95616, USA; Department of Biomedical Engineering, University of California, Davis, CA 95616, USA. Electronic address: masavageau@ucdavis.edu.
iScience ; 23(6): 101200, 2020 Jun 26.
Article en En | MEDLINE | ID: mdl-32531747
Mechanistic models of biochemical systems provide a rigorous description of biological phenomena. They are indispensable for making predictions and elucidating biological design principles. To date, mathematical analysis and characterization of these models encounter a bottleneck consisting of large numbers of unknown parameter values. Here, we introduce the Design Space Toolbox v.3.0 (DST3), a software implementation of the Design Space formalism enabling mechanistic modeling without requiring previous knowledge of parameter values. This is achieved by using a phenotype-centric modeling approach, in which the system is first decomposed into a series of biochemical phenotypes. Parameter values realizing phenotypes of interest are subsequently predicted. DST3 represents the most generally applicable implementation of the Design Space formalism and offers unique advantages over earlier versions. By expanding the Design Space formalism and streamlining its distribution, DST3 represents a valuable tool for elucidating biological design principles and designing novel synthetic circuits.
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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: IScience Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: IScience Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos