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Team:Calgary/Project/OurSensor/Modelling
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<h1>Modelling</h1> | <h1>Modelling</h1> | ||
| - | <p>The questions which the modelling team set out to answer were inspired by the questions the biologists were asking in the wet lab. We had to select a reporter enzyme that would be effective and durable for our lateral flow strip system. As we scrutinized how reporters, the detector, and other biological devices could merge together in our sensor, we realized these modular protein components could be assembled in many configurations. We were eager to learn the optimal configuration in which they could be combined to be most effective in our prototype.</p> | + | <p>The questions which the modelling team set out to answer were <span class="Yellow"><b>inspired by the questions</span></b> the biologists were asking in the wet lab. We had to select a reporter enzyme that would be effective and durable for our lateral flow strip system. As we scrutinized how reporters, the detector, and other biological devices could merge together in our sensor, we realized these modular protein components could be assembled in many configurations. We were eager to learn the <span class="Yellow"><b>optimal configuration</b></span> in which they could be combined to be most effective in our prototype.</p> |
| - | <p>To reduce the number of wet lab experiments to develop our sensor, we followed a two-pronged modelling approach to help answer some of these questions. First, we used spatial models in Autodesk Maya in which we assembled and animated 3D protein structures for insight into their function in the prototype. Also, we analyzed the kinetic properties of several common reporter enzymes, culminating in our selection of two novel reporters. Finally, we deployed a quantitative Mathematica prototype model to test how amounts of target DNA versus detector proteins would influence sensitivity of our lateral flow strip.</p> | + | <p>To reduce the number of wet lab experiments to develop our sensor, we followed a <span class="yellow"><b>two-pronged modelling approach</b></span> to help answer some of these questions. First, we used spatial models in Autodesk Maya in which we assembled and animated 3D protein structures for insight into their function in the prototype. Also, we analyzed the kinetic properties of several common reporter enzymes, culminating in our selection of two novel reporters. Finally, we deployed a quantitative Mathematica prototype model to test how amounts of target DNA versus detector proteins would influence sensitivity of our lateral flow strip.</p> |
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Revision as of 02:59, 28 September 2013
Modelling
Modelling
The questions which the modelling team set out to answer were inspired by the questions the biologists were asking in the wet lab. We had to select a reporter enzyme that would be effective and durable for our lateral flow strip system. As we scrutinized how reporters, the detector, and other biological devices could merge together in our sensor, we realized these modular protein components could be assembled in many configurations. We were eager to learn the optimal configuration in which they could be combined to be most effective in our prototype.
To reduce the number of wet lab experiments to develop our sensor, we followed a two-pronged modelling approach to help answer some of these questions. First, we used spatial models in Autodesk Maya in which we assembled and animated 3D protein structures for insight into their function in the prototype. Also, we analyzed the kinetic properties of several common reporter enzymes, culminating in our selection of two novel reporters. Finally, we deployed a quantitative Mathematica prototype model to test how amounts of target DNA versus detector proteins would influence sensitivity of our lateral flow strip.
