Low-cost, modular, multi-strain diagnostic (ongoing)
This is the application part of the project I am finishing.
We engineered yeast to induce growth arrest using a number of genes/inducers that we then characterized. We then used them as a diagnostic tool.
We used nonlinear differential equations. We model our co-culture experiments and made predictions.
We used a flow cytometer to collect our data. We also took images of our system as we used a yeast strain that turns purple in some cases so I did some image processing of our pretty colored colonies.
All the code and data will be release with the publication in github and ebioome. Specifically, I used python (opencv, pandas, seaborn, lmfit, scikit-learn).