1. Research 2014 Grace Chester, Katelyn Gutteridge, Morgan Spencer, Joel Henningsen, Sean Callen

2.  Caffeine diffusion modeling  What experimental data do we need?  Cellular absorption (when? How much?)

3.  Programmed evolution  Generating data sets Parameterized, “patternized”, randomized real data, totally randomized… Test our scores model on different types of data sets to see how well it works  Iteration (one experiment) and Cycle (3 experiments in which every variable is varied exactly once) Enhance the program to do more iterations/cycles How does our model work after more iterations/cycles?

4.  Increasing # of iterations and cycles  The program currently works for 1 cycle (3 iterations), but we want to generalize it to work for ‘m’ number of iterations to see how our model works in the long run  Progress: We are almost done figuring out the logic of extra iterations, but we still need to translate that logic into Python programming

5.  Programmed evolution cont.  Stopping criteria: when is our data considered to be informational enough? (60% “winner”? 30%? Etc.)  Scaling up and managing output Make the model work for more variables Graphically represent the results  Colony counts vs scores Biology experiment to test the colony count predictions

6.  >3 variables  Example: Want to vary Promoter = P, PCN = N, Chaperone = C, and Degradation tag = D  To do every combination of variable orders, we need a pattern to code it: PNCD – NPCD – CPND – PCND – NCPD – CNPD – DPNC… (12) – (13) – (12) – (13) – (12) – (14)(23)  Generalizing for >4 variables

7.  Programmed evolution cont.  Multiple starting points Vary the promoter starting with clones HH3, HLN, LH2 for example  Other applications What other things can our model be applied to?

8.  Finishing the scoring program  Perfecting the multiple variable algorithm  Make data to test both of the above  Caffeine diffusion (need biological input)  Find other application for scoring program, make it public access