In the beginning of the project we were looking for a reporter system. The wet lab team has made a list of possible reporters that can be used in vitro. We wanted to ensure that the reporter system gave us a rapid output, was stable and did not require cellular machinery to work. We wanted to test this in silico before we tested it in the wetlab. We wanted to ensure there was concrete evidence to support our choice of one reporter enzyme over another. Given that it is much easier to do simulations using models than to do experiments in the lab, we simulated horseradish peroxidase (HRP) and beta-galactosidase which are one of the most rapid reporters known in the literature. We programmed differential equations modelling enzyme kinetics in Mathematica to produce a interactive plot of output over time, with parameters such as initial enzyme concentration and initial substrate concentration being the parameters that can be adjusted.
After careful examination of the models under various parameters, we decided to use HRP because it is fast and give accurate response, evident from the kinetic models. However it turns out that we couldn't produce HRP in E. coli. After some more literature search we found a substitue of horseradish peroxidase,
In addition to deploying the model on the webpage we had also investigated the effects of changing the ratios between positive and negative DNA, and the number of TALEs available. For 5 levels of each of those parameters, 3 runs were conducted since the method is stochastic, resulting a total of 125 experiments. They are summarized in the graph below.
Figure 1. Model results of sensitivity tuning for TALE test strip.
From the graph we clearly see that not much TALE or DNA is required to have enough DNA bind to TALE, such that the signal cascade starting from the TALE can be read on the strip. Thus we have some theoretical evidence that our constructs will work the way they are designed. Also note that this experiment is not designed for specificity and sensitivity because of the simplicity of the binding probabilities chosen. We do acknowledge that as percentage of positive DNA increases, the total binding number will also increase, so it is not a "fair" comparison of performance of various levels of percentage of positive DNA. But for the purpose of ensuring the signals will be large enough to be read, this suffices.
What to be done?
In the future the model will be expanded to include parameters changing the probability of binding to TALE for both the positive and negative DNA. After which more experiments will be run to investigate the effects of those probabilities upon the sensitivity and specificity of the strip. We wish to conclude from those experiments, what is the bare minimal amount of TALE required on the strip, such that it gives good enough sensitivity and specificity for all possible ranges of probability of binding. This will in turn translate to the concentration of TALE needed, an important design parameter concerning the strip.
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