Abstract
Synthetic biology seeks to design biological parts and circuits that implement new functions in cells. Major accomplishments have been reported in this field, yet predicting a priori the in vivo behaviour of synthetic gene circuits is major a challenge. Mathematical models offer a means to address this bottleneck. However, in biology, modelling is perceived as an expensive, time-consuming task. Indeed, the quality of predictions depends on the accuracy of parameters, which are traditionally inferred from poorly informative data. How much can parameter accuracy be improved by using model-based optimal experimental design (MBOED)? To tackle this question, we considered an inducible promoter in the yeast S. cerevisiae. Using in vivo data, we re-fit a dynamic model for this component and then compared the performance of standard (e.g., step inputs) and optimally designed experiments for parameter inference. We found that MBOED improves the quality of model calibration by ∼60%. Results further improve up to 84 % when considering on-line optimal experimental design (OED). Our in silico results suggest that MBOED provides a significant advantage in the identification of models of biological parts and should thus be integrated into their characterisation.
Highlights
Synthetic biology, a discipline at the interface of biology, engineering and computer science, seeks to engineer cells with new functionalities
Our results suggest that iterative off-line and on-line model-based optimal experimental design (MBOED) enable the design of more informative experiments for the characterisation of biological parts, e.g., synthetic promoters
We recovered MPLac, the model published by Gnügge and colleagues [10], and independently assessed its ability to describe the experimental data reported in the original paper [10]
Summary
A discipline at the interface of biology, engineering and computer science, seeks to engineer cells with new functionalities. Despite great progress towards this goal [1], the prediction of the in vivo behaviour of synthetic circuits is still a challenge that hinders technological applications. For the field to reach its full potential, the accurate prediction of the dynamics of synthetic circuits needs to be achieved. Mathematical models can serve as a tool to gain a mechanistic understanding of a system and could tackle this issue; their adoption in synthetic biology has so far been limited [2]. While biophysical and static models have been successfully proposed as tools to guide the automated design of biological circuits [3,4], the use of mathematical models is often confined to the interpretation of experimental data.
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