  Bacterial sensor systems can be used for the detection and measurement of
molecular signal concentrations. The dynamics of the sensor directly depend on
the biological properties of the bacterial sensor cells; manipulation of these
features in the wet lab enables the engineering and optimization of the
bacterial sensor kinetics. This necessitates the development of biologically
meaningful computational models for bacterial sensors comprising a variety of
different molecular mechanisms, which further facilitates a systematic and
quantitative evaluation of optimization strategies. In this work, we dissect
the detection chain of bacterial sensors from a mathematical perspective from
which we derive, supported by wet-lab data, a complete computational model for
a Streptococcus mutans-based bacterial sensor as a case example. We address the
engineering of bacterial sensors by investigating the impact of altered
bacterial cell properties on the sensor response characteristics, specifically
sensor sensitivity and response signal intensity. This is achieved through a
sensitivity analysis targeting both the steady-state and transient sensor
response characteristics. Alongside the demonstration of suitability of our
methodological approach, our analysis shows that an increase of sensor
sensitivity, through a targeted manipulation of bacterial physiology, often
comes at the cost of generally diminished sensor response intensity.
