Rick A Fasani1, Michael A Savageau. 1. Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.
Abstract
MOTIVATION: Our recent work introduced a generic method to construct the design space of biochemical systems: a representation of the relationships between system parameters, environmental variables and phenotypic behavior. In design space, the qualitatively distinct phenotypes of a biochemical system can be identified, counted, analyzed and compared. Boundaries in design space indicate a transition between phenotypic behaviors and can be used to measure a system's tolerance to large changes in parameters. Moreover, the relative size and arrangement of such phenotypic regions can suggest or confirm global properties of the system. RESULTS: Our work here demonstrates that the construction and analysis of design space can be automated. We present a formal description of design space and a detailed explanation of its construction. We also extend the notion to include variable kinetic orders. We describe algorithms that automate common steps of design space construction and analysis, introduce new analyses that are made possible by such automation and discuss challenges of implementation and scaling. In the end, we demonstrate the techniques using software we have created. AVAILABILITY: The Design Space Toolbox for MATLAB is freely available at http://www.bme.ucdavis.edu/savageaulab/ CONTACT: masavageau@ucdavis.edu
MOTIVATION: Our recent work introduced a generic method to construct the design space of biochemical systems: a representation of the relationships between system parameters, environmental variables and phenotypic behavior. In design space, the qualitatively distinct phenotypes of a biochemical system can be identified, counted, analyzed and compared. Boundaries in design space indicate a transition between phenotypic behaviors and can be used to measure a system's tolerance to large changes in parameters. Moreover, the relative size and arrangement of such phenotypic regions can suggest or confirm global properties of the system. RESULTS: Our work here demonstrates that the construction and analysis of design space can be automated. We present a formal description of design space and a detailed explanation of its construction. We also extend the notion to include variable kinetic orders. We describe algorithms that automate common steps of design space construction and analysis, introduce new analyses that are made possible by such automation and discuss challenges of implementation and scaling. In the end, we demonstrate the techniques using software we have created. AVAILABILITY: The Design Space Toolbox for MATLAB is freely available at http://www.bme.ucdavis.edu/savageaulab/ CONTACT: masavageau@ucdavis.edu
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Authors: Agustino Martínez-Antonio; Jason G Lomnitz; Santiago Sandoval; Maximino Aldana; Michael A Savageau Journal: PLoS One Date: 2012-02-21 Impact factor: 3.240