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Revision as of 09:14, 28 September 2016
Initially we generated a mass action based model. This accurately modelled the system however we quickly realised that finding parameter values was going to prove particularly difficult.
We looked into the research and found BRENDA, the enzyme database. This had a great source of kinetic parameters for Michalis Menten for the rate equation. So our focus turned to this - we lost the accuracy in the early, transient, stage of the model and this introduced a limitation on the enzyme:substrate ratio to ensure the validity conditions we always met but since this region was of little interest to us and enzyme will be a major cost in the system so should be minimized when at all possible, this was a tradeoff we were willing to make for the more well reported parameters.
Off to the lab! Unfortunately the experimental results didn’t fit. Our model was reacting to completion given enough time which was causing problems.
With the model broken we had a chat with Aliyah, a postdoc in the MIB, who suggested we should look into the reversible form of Michalis Menten as this would help overcome the problem we had noticed - the inevitable same steady state concentration. This model was implemented and data was generated.
Then we took it to the lab, comparing what the model suggested with experimental results. Something else was wrong! The model wasn’t accounting for the amount of ABTS used at all. Only experiments where ABTS was in a large excess were being modelled accurately. The patch design relies on having variable limiting amount of ABTS so this was a problem.
Back to the drawing board again. Taking note that the problem was almost certainly in the rate equations we were using we hit the library. After another session cramming on Enzyme Kinetics we decided a Bi-Uni rate law would be able to account for the ABTS.
