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Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology
Article

Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design

Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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Processes 2019, 7(1), 52; https://doi.org/10.3390/pr7010052
Received: 28 November 2018 / Revised: 10 January 2019 / Accepted: 15 January 2019 / Published: 21 January 2019
(This article belongs to the Special Issue Computational Synthetic Biology)
Synthetic biology design challenges have driven the use of mathematical models to characterize genetic components and to explore complex design spaces. Traditional approaches to characterization have largely ignored the effect of strain and growth conditions on the dynamics of synthetic genetic circuits, and have thus confounded intrinsic features of the circuit components with cell-level context effects. We present a model that distinguishes an activated gene’s intrinsic kinetics from its physiological context. We then demonstrate an optimal experimental design approach to identify dynamic induction experiments for efficient estimation of the component’s intrinsic parameters. Maximally informative experiments are chosen by formulating the design as an optimal control problem; direct multiple-shooting is used to identify the optimum. Our numerical results suggest that the intrinsic parameters of a genetic component can be more accurately estimated using optimal experimental designs, and that the choice of growth rates, sampling schedule, and input profile each play an important role. The proposed approach to coupled component–host modelling can support gene circuit design across a range of physiological conditions. View Full-Text
Keywords: synthetic biology; model fitting; characterization; optimal experimental design; optimal control; cell physiology; host-context effects synthetic biology; model fitting; characterization; optimal experimental design; optimal control; cell physiology; host-context effects
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MDPI and ACS Style

Braniff, N.; Scott, M.; Ingalls, B. Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design. Processes 2019, 7, 52. https://doi.org/10.3390/pr7010052

AMA Style

Braniff N, Scott M, Ingalls B. Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design. Processes. 2019; 7(1):52. https://doi.org/10.3390/pr7010052

Chicago/Turabian Style

Braniff, Nathan, Matthew Scott, and Brian Ingalls. 2019. "Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design" Processes 7, no. 1: 52. https://doi.org/10.3390/pr7010052

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