Quantitative Aspects, Engineering and Optimization of Bacterial Sensor Systems

Abstract

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, focusing on computational aspects. As a case example, we derive, supported by wet-lab data, a complete computational model for a Streptococcus mutans-based bacterial sensor. We address the engineering of bacterial sensors by mathematically 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 the suitability of our methodological approach, our analysis shows that an increase in sensor sensitivity through targeted manipulation of bacterial physiology often comes at the cost of generally diminished sensor response intensity.