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Math wrap-up

Math wrap-up. Research 2014 Grace Chester, Katelyn Gutteridge , Morgan Spencer, Joel Henningsen , Sean Callen. Questions. Caffeine diffusion modeling What experimental data do we need? Cellular absorption (when? How much?). Questions cont. Programmed evolution Generating data sets

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Math wrap-up

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  1. Math wrap-up Research 2014 Grace Chester, Katelyn Gutteridge, Morgan Spencer, Joel Henningsen, Sean Callen

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

  3. Questions cont. • 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. Programmed Evolution • 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. Questions cont. • 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. Programmed Evolution • >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. Questions cont. • 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. Priorities • 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

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