Quantitative Aspects, Engineering and Optimization of Bacterial Sensor Systems

TL;DR

This paper develops a computational model of bacterial sensors, specifically for Streptococcus mutans, to analyze how biological property manipulations affect sensor sensitivity and response, aiding in sensor optimization.

Contribution

It introduces a comprehensive mathematical model of bacterial sensors supported by wet-lab data, enabling systematic evaluation of how bacterial property changes influence sensor performance.

Findings

Increased sensor sensitivity often reduces response signal intensity.

Manipulating bacterial physiology can optimize sensor sensitivity.

The model effectively predicts sensor response characteristics.

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 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.