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

• Main Authors: Nathan Braniff, Matthew Scott, Brian Ingalls
• Format: Article
• Language: English
• Published: MDPI AG 2019-01-01
• Series: Processes
• Subjects: synthetic biology; model fitting; characterization; optimal experimental design; optimal control; cell physiology; host-context effects
• Online Access: https://www.mdpi.com/2227-9717/7/1/52

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.