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

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

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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?

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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

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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

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>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

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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?

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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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