Difference between revisions of "Team:Manchester/Model"

Line 3: Line 3:
 
<head>
 
<head>
 
<style>
 
<style>
.dropdown {
+
.tooltip {
 
     position: relative;
 
     position: relative;
 
     display: inline-block;
 
     display: inline-block;
 +
    border-bottom: 1px dotted black;
 
}
 
}
  
.dropdown-content {
+
.tooltip .tooltiptext {
     display: none;
+
     visibility: hidden;
 +
    width: 350px;
 +
    background-color: black;
 +
    color: #fff;
 +
    text-align: center;
 +
    border-radius: 6px;
 +
    padding: 5px 0;
 +
   
 +
    /* Position the tooltip */
 
     position: absolute;
 
     position: absolute;
     background-color: #f9f9f9;
+
     z-index: 1;
     min-width: 80%;
+
     top: -5px;
    box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
+
     left: 105%;
     padding: 12px 16px;
+
 
}
 
}
  
.dropdown:hover .dropdown-content {
+
.tooltip:hover .tooltiptext {
     display: block;
+
     visibility: visible;
 
}
 
}
 
</style>
 
</style>
Line 51: Line 59:
  
 
<h2>Hover for solutions</h2>
 
<h2>Hover for solutions</h2>
<p> What is ensemble modelling</p>
 
  
<div class="dropdown">
+
 
  <span>Mouse over me</span>
+
<body style="text-align:center;">
  <div class="dropdown-content">
+
 
    <p>The short version…….
+
<div class="tooltip"> What is Ensemble modelling?
Instead of running your model once with some specific parameters, you find all the possible parameters in literature and run your model for lots of combinations in a clever way. By doing this you take into account the uncertainty in your parameters and as such make meaningful conclusions. An example of this is what is your exact reaction scheme, you can run these simulations for different models and see which can be consistent with experimental data. Furthermore by testing your specific parameters set fit to data you can find out what values work and relationships between your parameters, better understanding them and providing physical insight. </p>
+
  <span class="tooltiptext">The short version…….
  </div>
+
Instead of running your model once with some specific parameters, you find all the possible parameters in literature and run your model for lots of combinations in a clever way. By doing this you take into account the uncertainty in your parameters and as such make meaningful conclusions. An example of this is what is your exact reaction scheme, you can run these simulations for different models and see which can be consistent with experimental data. Furthermore by testing your specific parameters set fit to data you can find out what values work and relationships between your parameters, better understanding them and providing physical insight.</span>
 
</div>
 
</div>
 +
 +
</body>
  
  
 
</html>
 
</html>

Revision as of 12:46, 26 September 2016

Manchester iGEM 2016

★ ALERT!

This page is used by the judges to evaluate your team for the Best Model award.

Delete this box in order to be evaluated for this medal. See more information at Instructions for Pages for awards.

Modeling

Mathematical models and computer simulations provide a great way to describe the function and operation of BioBrick Parts and Devices. Synthetic Biology is an engineering discipline, and part of engineering is simulation and modeling to determine the behavior of your design before you build it. Designing and simulating can be iterated many times in a computer before moving to the lab. This award is for teams who build a model of their system and use it to inform system design or simulate expected behavior in conjunction with experiments in the wetlab.

Inspiration

Here are a few examples from previous teams:

Hover for solutions

What is Ensemble modelling? The short version……. Instead of running your model once with some specific parameters, you find all the possible parameters in literature and run your model for lots of combinations in a clever way. By doing this you take into account the uncertainty in your parameters and as such make meaningful conclusions. An example of this is what is your exact reaction scheme, you can run these simulations for different models and see which can be consistent with experimental data. Furthermore by testing your specific parameters set fit to data you can find out what values work and relationships between your parameters, better understanding them and providing physical insight.