Difference between revisions of "Team:Warwick/Modelling6"

 
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<h5 class="sidebartitle">Simple Exponential Model and Gompertz Function</h5>
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<p><img src="https://static.igem.org/mediawiki/2015/4/42/Warwickbubbles4.png" height="120px" width="800px" border="1px"></p>
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There was an inherent issue with a previous model in that the structure of the created shape would change as cells grew. We needed to model how they would grow and how the placement of the cells in the start would affect growth.
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<p>________________________________________________________________________________________________________________________________________________</p><h5>Stochastic Modelling for Cell Growth</h5>
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We wanted to understand how the placement of cells changes as time progresses. Cells divide, roughly in the same direction in which they were formed. To model cell division we imagined them situated on a Cartesian coordinate system, and division to place on a polar coordinate system with the original cell as the origin. The population count is modelled with the Gompertz function. To decide which direction the division occur we used two criteria:
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<br>1.      It must not overlap other cells or DNA
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<br>2.      The probability is weighted by a normal distribution with the peak in the same direction as the previous generational division
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<br>The user inputs the DNA structure, the standard deviation for the normal distribution, and the maximum number of cells it should model. It then outputs an animation of the cell community as it develops.
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<p>________________________________________________________________________________________________________________________________________________</p><h5>Simple Exponential Model and Gompertz Function</h5>
 
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<p style="float: right;"><img src="https://static.igem.org/mediawiki/2015/6/63/Warwickgompertz.png" align="right" height="150px" width="270px" border="1px"></p>
 
<p style="float: right;"><img src="https://static.igem.org/mediawiki/2015/6/63/Warwickgompertz.png" align="right" height="150px" width="270px" border="1px"></p>
 
<p style="float: left;"><img src="https://static.igem.org/mediawiki/2015/b/b5/Warwickexponential_equation.png" height="300px" width="180px" border="1px"></p>
 
<p style="float: left;"><img src="https://static.igem.org/mediawiki/2015/b/b5/Warwickexponential_equation.png" height="300px" width="180px" border="1px"></p>
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The equation to the left is the simplest model possible to model cell growth and just displays an exponential increase in cells with respect to time. This is a bad model because E.coli has an upper limit to the number of cells that can survive in a given location before they begin to fight for the same resources and some will die.
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The equation to the left is the simplest model possible to model cell growth and just displays an exponential increase in cells with respect to time. This is a bad model because <i>E.coli</i> has an upper limit to the number of cells that can survive in a given location before they begin to fight for the same resources and some will die.
 
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<h5 class="sidebartitle">Comparing the two</h5>
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<p>________________________________________________________________________________________________________________________________________________</p><h5>Comparing the two</h5>
 
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<img src="https://static.igem.org/mediawiki/2015/9/94/WarwickDna_origami_ic.png" class="pics" alt=""> This shows how the DNA strands come together. Three double stranded strings of DNA are denatured and then when slowly cooled will come together to form the Y shape. However after the denaturing each strand of DNA has an equal chance of bonding to the original piece of DNA as it does to the correct origami side. Therefore the more complex the structure the less likely it is that that structure will fully form.
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<img src="https://static.igem.org/mediawiki/2015/f/f1/Warwickgrpahsgompertz.png" class="pics" alt="">  
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<p style="float: left;"><img src="https://static.igem.org/mediawiki/2015/4/44/WarwickY_PlasmidSequences.png" height="300px" width="300px" border="1px"></p>
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The image to the left shows the population of cells as a function of time under the first model and the second shows the Gompertz model.   
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The graph to the left of the image shows the population of cells as a function of time under the first model and the second shows the Gompertz model. As you can see there is a drop off on the second model.   
 
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<h5 class="sidebartitle">Cell growth with Interactions</h5>
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<p>________________________________________________________________________________________________________________________________________________</p> <h5>Cell growth with Interactions</h5>
 
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Our idea of forcing different cell types to live together in close vicinity will alter this model however. Taking for example forcing 3 cell types together;
 
Our idea of forcing different cell types to live together in close vicinity will alter this model however. Taking for example forcing 3 cell types together;
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It is important for cells to have a starting point where they are all stuck together instead of just 3 different cell types being put into a solution and let grow until they come together, as the point of the test is to see how cells grow when they are together with different cell types, and to see if it is possible to control the overall concentrations of the cell types by just changing the arrangement of the original cell cluster.<br>
 
It is important for cells to have a starting point where they are all stuck together instead of just 3 different cell types being put into a solution and let grow until they come together, as the point of the test is to see how cells grow when they are together with different cell types, and to see if it is possible to control the overall concentrations of the cell types by just changing the arrangement of the original cell cluster.<br>
 
Because we plan to be able to use any cell types for any of the colonies it is important to come up with a model which can explain the cell growth of all 3 cell types over a period of time.
 
Because we plan to be able to use any cell types for any of the colonies it is important to come up with a model which can explain the cell growth of all 3 cell types over a period of time.
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<img src="https://static.igem.org/mediawiki/2015/3/3d/Warwickfristeqiuation.png" class="pics" alt="">
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Where:
 
 
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Where:
 
<br>GA  = Total cell growth of A with respect to time
 
<br>GA  = Total cell growth of A with respect to time
 
<br>nA  = Cell growth of A with no external forces acting   
 
<br>nA  = Cell growth of A with no external forces acting   
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<br>SC  = Concentration of Cell type C
 
<br>SC  = Concentration of Cell type C
 
<br>CCA = Constant of affect (how A affects B)
 
<br>CCA = Constant of affect (how A affects B)
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<br>The constants of affects will be determined experimentally and the concentrations can then be found by rearranging the equations.  
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<br>The constants of affects will be determined experimentally and the concentrations can then be found by rearranging the equations. In order to obtain the constants you would need to stick cells together with known concentrations, for example to find CCA you would stick C to A and monitor how quickly A grows. CCA would then be equal to this value divided by the original growth rate of A. <br>
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<img src="https://static.igem.org/mediawiki/2015/6/63/Warwickgompertz.png" class="pics" alt="">
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<br>This equation combines the above two and is the final form of the cell growth of A. The growth of B and C can be found by substituting out A for B or C appropriately. </p>
 
<br>This equation combines the above two and is the final form of the cell growth of A. The growth of B and C can be found by substituting out A for B or C appropriately. </p>
 
 
 
 
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<h5 class="sidebartitle">How Constants affect Growth</h5>
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<p>________________________________________________________________________________________________________________________________________________</p><h5>How Constants affect Growth</h5>
 
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<p style="float: right;"><img src="https://static.igem.org/mediawiki/2015/c/c1/Warwickcellgrowthgraph.png" align="right" height="500px" width="600px" border="1px"></p>
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<p style="float: left;"><img src="https://static.igem.org/mediawiki/2015/9/9f/WarwickTableofprob.png" height="200px" width="200px" border="1px"></p>
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This graph shows how A grows depending on the affect constants which are displayed at the bottom of the graph. As you can see a negative affect constant decreases cell growth exponentially and a positive affect constant has the opposite effect.
 
This graph shows how A grows depending on the affect constants which are displayed at the bottom of the graph. As you can see a negative affect constant decreases cell growth exponentially and a positive affect constant has the opposite effect.
 
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From this graph we can infer that changing the affect constants (by using different cell types) will change the cell growth rate of A. However there always appears to be a limit to the maximum or minimum growth rate as shown by the clear asymptotes of the graph.
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Latest revision as of 20:39, 17 September 2015

Warwick iGEM 2015

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