Difference between revisions of "Team:Warwick/BindingAffinity"
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<br><b>Law of Mass Action</b> | <br><b>Law of Mass Action</b> | ||
<br>When modelling chemical reactions, we used the law of mass action which explains and predicts behaviours of solutions in dynamic equilibrium. The basis for this law stems from the research conducted by Cato M. Guldberg and Peter Waage in the mid 18 hundreds. Guldberg and Waage recognized that chemical equilibrium is a dynamic process in which rates of reaction for the forward and backward reactions must be equal at chemical equilibrium. In order to find the equilibrium values for the chemicals involved, the differential equation governing the process must be found. | <br>When modelling chemical reactions, we used the law of mass action which explains and predicts behaviours of solutions in dynamic equilibrium. The basis for this law stems from the research conducted by Cato M. Guldberg and Peter Waage in the mid 18 hundreds. Guldberg and Waage recognized that chemical equilibrium is a dynamic process in which rates of reaction for the forward and backward reactions must be equal at chemical equilibrium. In order to find the equilibrium values for the chemicals involved, the differential equation governing the process must be found. | ||
| − | <br><br>We have found said differential equation and then analytically solved it. After substituting in empirical data from the experiments, it is impossible to solve the equations to find the binding and unbinding constants (association and disassociation constants). | + | <br><br> |
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| − | <br>We are currently working on a piece of code that will test the DNA sequence to see if they are viable options for our experiment. | + | |
| + | We have found said differential equation and then analytically solved it. After substituting in empirical data from the experiments, it is impossible to solve the equations to find the binding and unbinding constants (association and disassociation constants). Instead, we used a computational method to find these constants. Put simply this involved trying all the possible combinations of the constants (to the first significant figure) to find which best matches the empirical data. These results tell the biologists which zinc fingers have the best binding ability.<br> | ||
| + | Another piece of code then uses these new found values to find out how much reactant to use, the related expenses, and how long the reaction will take.<br> | ||
| + | We are currently working on a piece of code that will test the DNA sequence to see if they are viable options for our experiment. | ||
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| + | <br><b>Stochastic Modelling for Cell Growth</b> | ||
| + | <br>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: | ||
| + | <br>1. It must not overlap other cells or DNA | ||
| + | <br>2. The probability is weighted by a normal distribution with the peak in the same direction as the previous generational division | ||
| + | <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. | ||
<br><b>The Equations</b><br> | <br><b>The Equations</b><br> | ||
Revision as of 18:13, 16 September 2015
