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Team:UC Davis/Signal Machine
From 2014.igem.org
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Instead of using the measured K<sub>cat</sub>/K<sub>m</sub>, we randomized the catalytic matrix and tested the variants for prediction accuracy. One million variants later and we were starting to produce consistently better predictions.</p><br> | Instead of using the measured K<sub>cat</sub>/K<sub>m</sub>, we randomized the catalytic matrix and tested the variants for prediction accuracy. One million variants later and we were starting to produce consistently better predictions.</p><br> | ||
Latest revision as of 02:11, 18 October 2014
Mathematical Approach
Mathematical Approach
Testing Our Model
Testing Our Model
Machine Learning
Machine Learning
Instead of using the measured Kcat/Km, we randomized the catalytic matrix and tested the variants for prediction accuracy. One million variants later and we were starting to produce consistently better predictions.
With our success we redesigned the experiment with different concentrations. This time we would vary aldehyde in the following concentrations: 0 µM, 12.5 µM, 25 µM, & 50 µM. We would also train on an incomplete set and test the best matrices on the full set. This mimics a concept from protein crystallography, R-Free, where several atoms are removed from a model and later used to test the models accuracy. The results were astonishing. With our best mutant matrix, we were able to predict aldehyde concentration with an average error of only 6.25 µM!
Olive Oil
Nine samples of Extra Virgin Olive Oil were obtained and prepared for assay.
