Establishing Ratiometric Characterisation in Bacillus Subtilis for Biosensing Applications

Abstract

Arsenic contamination of groundwater remains a serious health concern in many areas of the world. Developing countries such as Bangladesh and Nepal are particularly affected because access to high quality water infrastructure is low. Since the 1970s, most water in these countries is sourced from shallow tube wells installed to reduce the spread of diseases associated with poor water hygiene. In this goal they were successful, however by the mid 1990s it became apparent that many of these wells were contaminated by arsenic and that these countries’ rural poor were being slowly poisoned. No simple, cheap, and reliable test for arsenic exists, and efforts to mitigate arsenic contamination have been severely limited by this over the past two decades. Government backed well-testing efforts using commercially available field kits have many issues with reliability, safety, rigour, and transparency, and have lost their urgency over the past decade, while the expensive field test kits remain out of the reach of most ordinary people in these areas. Synthetic Biology offers the technology to develop a new class of biosensor by exploiting bacteria’s natural ability to sense and respond to levels of arsenic considerably lower than commercially available kits which are based on analytical chemistry. In order to reach this goal, we must first develop our understanding of the natural response to arsenic in our chosen host, B. subtilis. Although we have a reasonably good qualitative understanding of the operon responsible for arsenic sensing, very little quantitative analysis has been carried out, and a robust system for ratiometric characterisation has not been established in the bacteria. In this work, a robust platform for rapid ratiometric characterisation is established in B. subtilis. A rigorous mathematical model of the ars operon is developed and analysed before being verified experimentally. This new knowledge is then used to explore synthetic permutations to the natural system aimed at improving the sensor properties of the system. Finally, a biological architecture for an easily tunable biosensor with good characteristics is recommended.