Team:UC Davis/Signal Processing

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  <h2>Mathematical Approach</h2>
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  <a href="https://2014.igem.org/Team:UC_Davis/Signal_Math">
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    <span><h2>Mathematical Approach</h2></span>
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  <h2>Testing Our Model</h2>
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    <span><h2>Testing Our Model</h2></span>
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  <h2>Olive Oil</h2>
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    <span><h2>Olive Oil</h2></span>
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<p><b>Our 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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<p>Mathematical Approach</p>
<p>Mathematical Approach</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>
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Our mathematical model consists of a simple 3x3 array which we call the catalytic matrix. Using a few tricks from linear algebra, we created a way of predicting the concentrations in a three-enzyme biosensor. The main assumption of the model is that the substrates involved do not competiviely inhibit each other. <br><br></p>
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<p>This was useful, but Olive Oil contains many aldehyes and the enzymatic response is different for each one.</p><br>
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<p>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.</p><br>
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<p><b>To read more about our mathematical approach, click <a href="https://2014.igem.org/Team:UC_Davis/Signal_Math" class="brightlink">here</a></b>.</p>
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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 <i>E</i>-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.<br>
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<p>Testing Our Model</p>
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<p align="center"><img src="https://static.igem.org/mediawiki/2014/1/1d/64Combinations.png" width="800px" style="margin-left:auto;margin-right:auto;"/></p>
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To test our model, we built a combinatorial set of aldehydes, and compared our predicted concentrations with known values. The results suggested we needed to take a new approach. We taught our computer to solve the problem, and it worked. By randomizing the values in the catalytic matrix, we found that there was a vector space that could model our competitively inhibited system.</p><br><br>
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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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<p><b>To read more about our mathematical approach, click <a href="https://2014.igem.org/Team:UC_Davis/Signal_Test" class="brightlink">here</a></b>.</p>
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<p>Olive Oil</p>
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<p>With a working model, it was time for the ultimate test: Olive Oil<br><br>
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Nine samples of Extra Virgin Olive Oil were obtained and <a href="https://2014.igem.org/Team:UC_Davis/Protein_Engineering_Test" class="brightlink">prepared</a> for assay. The velocities were recorded with each enzyme for a total of 27 data points. We used the best catalytic matrix from our previous model and again inverted the matrix and multiplied by the observed velocity. The results are plotted below.</p><br>
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<p align="center"><img src="https://static.igem.org/mediawiki/2014/1/19/OliVEoil_Set.jpg" style="margin-left:auto;margin-right:auto;border:1.5px solid #212f20;"/></p><br>
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<p><b>To read more about how our model did when testing olive oil, click <a href="https://2014.igem.org/Team:UC_Davis/Signal_oil" class="brightlink">here</a></b>.</p>
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Latest revision as of 03:49, 18 October 2014

UC Davis iGEM 2014

Mathematical Approach

Mathematical Approach

Testing Our Model

Testing Our Model

Olive Oil

Olive Oil

Our signal processing data set can be downloaded here.

Mathematical Approach

Our mathematical model consists of a simple 3x3 array which we call the catalytic matrix. Using a few tricks from linear algebra, we created a way of predicting the concentrations in a three-enzyme biosensor. The main assumption of the model is that the substrates involved do not competiviely inhibit each other.

To read more about our mathematical approach, click here.

Testing Our Model


To test our model, we built a combinatorial set of aldehydes, and compared our predicted concentrations with known values. The results suggested we needed to take a new approach. We taught our computer to solve the problem, and it worked. By randomizing the values in the catalytic matrix, we found that there was a vector space that could model our competitively inhibited system.



To read more about our mathematical approach, click here.

Olive Oil

With a working model, it was time for the ultimate test: Olive Oil

Nine samples of Extra Virgin Olive Oil were obtained and prepared for assay. The velocities were recorded with each enzyme for a total of 27 data points. We used the best catalytic matrix from our previous model and again inverted the matrix and multiplied by the observed velocity. The results are plotted below.



To read more about how our model did when testing olive oil, click here.