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Updated: Jun 9, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
Published on: October 17, 2025
Automated construction and analysis of the design space for biochemical systems
Rick A Fasani1, Michael A Savageau
1Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.
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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