The questions which the modelling team set out to answer were inspired by the questions the wetlab groups were asking. We had to select a reporter enzyme that would be effective and durable for our lateral flow strip system. As we scrutinized how reporters, the detector, and other biological devices could merge together in our sensor, we realized these modular protein components could be assembled in many configurations. We were eager to learn the optimal configuration in which they could be combined to be most effective in our prototype so that we could inform the direction of our experiments in the wetlab. We aimed to understand how the system would work so that we could develop the best system possible to impact the lives of those this system could impact.

To reduce the number of wet lab experiments to develop our sensor, we followed a two-pronged modelling approach to help answer our questions. First, we used spatial models in Autodesk Maya in which we assembled and animated 3D protein structures for insight into their function in the prototype. Also, we analyzed the kinetic properties of several common reporter enzymes, culminating in our selection of two novel reporters. Finally, we deployed a quantitative Mathematica prototype model to test how amounts of target DNA versus detector proteins would influence sensitivity of our lateral flow strip, and a Scilab differential model to predict how our final system would function.