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Team:UC Davis/Signal Processing
From 2014.igem.org
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| + | <h2>Mathematical Approach</h2> | ||
| + | <a href="https://2014.igem.org/Team:UC_Davis/Signal_Math"> | ||
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| + | <h2>Testing Our Model</h2> | ||
| + | <a href="https://2014.igem.org/Team:UC_Davis/Signal_Test"> | ||
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| + | <h2>Olive Oil</h2> | ||
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| - | + | <img src="https://static.igem.org/mediawiki/2014/5/54/CatalyticMatrix.png" width="400px" style="border:1.5px solid #212f20;"/> | |
| - | + | 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>To | + | <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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| - | <div | + | <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> | <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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| - | The | + | 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> |
| - | </p> | + | |
| - | < | + | <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 | + | <p>Olive Oil</p> |
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| - | + | <p>With a working model, it was time for the ultimate test: Olive Oil<br><br> | |
| - | + | 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> | ||
| + | <p> | ||
| + | <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
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.
