# IGEM:Imperial/2010/2010/09/01

(diff) ←Older revision | Current revision (diff) | Newer revision→ (diff)
Project name Main project page
Previous entry

• 2 pm, RSM 3.03

## Meeting with Matthieu Bultelle

Output Amplification Model

• Check for our initial conditions. We are disturbing our system, which is in steady state, by adding the Catechol. But we need to know exactly what our initial conditions are for the simulation.
• Predict different behaviours of our model. We need to show the different possible behaviours of our system and its dependance on variables, such as initial concentration, time at which Catechol is added etc.

State when the system doesn't work and determine the most sensitive variables.

Peptide Display Model - False Positives

• Our calculation is redundant!
• Also, our assumption of how many proteins will be produced in a B.sub cell is not good enough because we are basing this calculation on how many proteins were displayed. However, how many proteins are being displayed does not mean that this is the number of proteins produced. We could do an experiment to find out how many proteins are bound to the cell wall and how many are not.
• Instead, we need to work backwards. Find out how many receptors need to be bound to the cell wall. How many proteins need to "escape" for a false positive response? See if we can actually achieve this concentration. If not, then we don't have to worry about false positives.
• The affinity of the whole protein for the receptor will be smaller than the affinity of the peptide. If the affinity is reduced by a factor of x, then we will need x more proteins to trigger a response.
• Also consider: Peptide binding to the cell membrane. Are they going to bind in thre right conformation?
• Also check for electromagnetic forces (should be negligible because the ration between B.sub and Control Volume is quite small!). Google isotropic diffusion.

General Remarks

• Update the Wiki. Make several pages instead of one long one. Give references.
• Make a list of experiments.
• State variables (knowns and unknowns). Knowns: give reference and state which ones are from a paper and which ones we have deduced (and maybe criticise that). Unknowns: Study influence of the unknowns.
• Recommendations of our model (e.g. justify why we chose 1-step amplification over 2-step etc.)
• Consider making animation for the Jamboree (e.g. diffusion of peptide).