Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering

Recent progress in synthetic biology
allows the construction of
dynamic control circuits for metabolic engineering. This technology
promises to overcome many challenges encountered in traditional pathway
engineering, thanks to its ability to self-regulate gene expression
in response to bioreactor perturbations. The central components in
these control circuits are metabolite biosensors that read out pathway
signals and actuate enzyme expression. However, the construction of
metabolite biosensors is a major bottleneck for strain design, and
a key challenge is to understand the relation between biosensor dose-response
curves and pathway performance. Here we employ multiobjective optimization
to quantify performance trade-offs that arise in the design of metabolite
biosensors. Our approach reveals strategies for tuning dose-response
curves along an optimal trade-off between production flux and the
cost of an increased expression burden on the host. We explore properties
of control architectures built in the literature and identify their
advantages and caveats in terms of performance and robustness to growth
conditions and leaky promoters. We demonstrate the optimality of a
control circuit for glucaric acid production in Escherichia
coli, which has been shown to increase the titer by 2.5-fold
as compared to static designs. Our results lay the groundwork for
the automated design of control circuits for pathway engineering,
with applications in the food, energy, and pharmaceutical sectors.