There is an existing algorithm that is able to understand bacterial evolution by finding key parameters about the potential mutations. This is a recent innovation by Drs. Sergey Sarkisov II, Robert Azencott, Ilya Timofeyev, Ricardo Azevedo, et al. However, it has not yet been applied to the fields of medicine, finance, technology, etc. The team decided to apply this algorithm to the field of medicine.
What it does
It helps understand the means of developing medicines that attempt to counteract emerging strains of bacteria. The common notion is that with existing drugs, antigens often mutate to decrease the drugs' impact. We simulated ways of overcoming these problems.
How we built it
We modified the model to incorporate differing medicines and how they reduced the growth rates of specific resistances.
Challenges we ran into
Sharing and implementing codes; coordinating the teamwork and role assignments; identifying the feasible scope of work (time constraints, etc.); connecting the wide-array of skills among individuals
Accomplishments that we're proud of
Able to organize the team successfully to obtain meaningful results.
What we learned
Octave can run Matlab, different codes (Matlab/Python), the understanding behind how asexual populations grow and evolve in time. Medicines that target strongest resistances but not the ancestor strains are the best.
What's next for Improving Medicine via Computer Simulations
Running more simulations and performing statistical analysis to verify the accuracy of our model simulations. Further exploring how medicines can interact with different bacterial strains.