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Team:UC Davis/Signal Test
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Revision as of 02:10, 18 October 2014
Mathematical Approach
Mathematical Approach
Testing Our Model
Testing Our Model
Machine Learning
Machine Learning
To test our model we built a combinatorial set of aldehydes. We chose three representative aldehydes from each bin, saturated medium, saturated long, and unsaturated. The three aldehydes were chosen such that the enzyme's response to each would best represent their respective groups. We created a total of 64 different combinations by mixing Pentanal, Decanal, and E-2-Decenal in four different values, 0 µM, 10 µM, 100 µM, & 1000 µM. Three combinatorial well plates were made and mixed with each enzyme separately. The observed velocity is recorded in each well.
The catalytic matrix is inverted and multiplied by the observed velocity in each well and out comes our predicted concentrations!
In high concentrations, we found that aldehydes would crash out of solution. We chose to focus on only the data set below 1000 µM. A more serious issue was brought to light however. The observed velocities from the combinatorial plates suggested competitive inhibition was occurring when E-2-Decenal was present in solution. This obfuscated our model considerably. Our primary assumption was that competitive inhibition would not come into play. We needed to think more abstractly. We asked a simple question: If measured catalytic values would not work in our suggested model, was there still a set of numbers that would work?
