Team:UC Davis/Signal Processing

From 2014.igem.org

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<p><b>Our full signal processing data set can be downloaded <a href="https://static.igem.org/mediawiki/2014/0/09/MultiplexingFinalData.xls" class="brightlink">here</a></b>.</p>
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To model our system, we first focused our attention on the linear range of each enzymes Michaelis Menten plot. The linear range of this plot is governed by the relationship:<br></p>
To model our system, we first focused our attention on the linear range of each enzymes Michaelis Menten plot. The linear range of this plot is governed by the relationship:<br></p>
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<p><b>Our full signal processing data set can be downloaded <a href="https://static.igem.org/mediawiki/2014/0/09/MultiplexingFinalData.xls" class="brightlink">here</a></b>.</p>
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Revision as of 00:48, 18 October 2014

UC Davis iGEM 2014

Mathematical Approach

Our full signal processing data set can be downloaded here.

To model our system, we first focused our attention on the linear range of each enzymes Michaelis Menten plot. The linear range of this plot is governed by the relationship:


This was useful, but Olive Oil contains many aldehyes and the enzymatic response is different for each one.



To describe this mathematically we started with the assumption that none of the substrates would induce competitive inhibition. If this was the case, the modeling would be simple. We would consider the observed velocity to be a linear combination of the three singular responses to aldehyde.


To test our model we built a combinatorial set of aldehydes. We chose three aldehydes from each bin, saturated medium, saturated long, and unsaturated. The three aldehydes were chosen such that the enzymes response to each would represent their respective groups. We created a total of 64 different combinations by mixing Pentanal, Decanal, and E-2-Decenal in four different concentration ranges, 0 µM, 10 µM, 100 µM, & 1000 µM. Three combinatorial well plates were made and mixed with each enzyme separately.