IGEM:Imperial/2010/2010/08/26

{| width="800"
 * style="background-color: #EEE"|[[Image:owwnotebook_icon.png|128px]] Project name
 * style="background-color: #F2F2F2" align="center"|  |Main project page
 * style="background-color: #F2F2F2" align="center"|  |Main project page


 * colspan="2"|
 * colspan="2"|

Meeting with Dr. Matthieu Bultelle
Output Amplification Model (Catechol)
 * Costrain the system: Force our system to be positive by imposing constraints (use max-function in Matlab).
 * Change time scale, remember to rescale constraints (we have tried this but it didn't seem to work).
 * Look up spline-function in Matlab.
 * Look up Rouge-Kutta, which is a better way of solving ODEs than Euler. Rouch-Kutta is what the Matlab ode-solvers is based on.
 * Create an interpolated array to allow running the program until a certain point in time. (This is because Matlab does not deal very well with memory allocation?)
 * ode45: All we need as inputs is initial condition, initial time and final time. ode-solvers do not adapt themselves, which can be a problem!
 * Simulate the system with very high precision for a very short period of time. (Very important for time periods where our system varies very fast.)
 * For the reaction A + B <--> C: Solve this equation by conservation of mass. i.e. X = k(A0 - X)(Bo - X). Solve this equation for Xlimit to obtain an analytical solution. This is to get an idea of how fast A, B or C increase (or decrease) to their final value. This is the crucial timestep that we need to simulate with high precision!

Protein Display Model
 * Check this model for false positives.


 * }