Story of our Model
Initially we generated a mass action-based model. This accurately described the system: however, we quickly realised that finding parameter values was going to prove particularly difficult. We needed to simplify our approach. The obvious strategy was to use Michaelis-Menten kinetics instead of a full mass action description of our system. BRENDA, the enzyme database turned out to be a great source of kinetic parameters for Michaelis-Menten equations. So our focus turned to this - we lost accuracy in the early, transient stage of the simulations and had to introduce a limitation on the enzyme:substrate ratio to ensure the validity of the Michaelis-Menten assumptions, but as the early transient region was of little interest to us and all the laboratory experiments performed satisfy the ratio condition this was a tradeoff we were willing to accept. Furthermore, we realised that the Michaelis-Menten approximations remove the need to measure substrate concentrations in the lab which is extremely hard to do. Discussions with Aliah, a postdoc in the MIB, suggested we should look into the reversible form of the Michaelis-Menten equations, as this might help overcome the problem we had noticed. This improved model was implemented and simulations run. Then we took the results to the lab, comparing what the model suggested with the experimental results. They still didn't fit. Something else was clearly 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 predicted accurately. The AlcoPAtch design relies on having a limiting amount of ABTS, so this was a problem. Back to the drawing board again. Noting that the problem was almost certainly in the rate equations we were using we hit the library. After another session cramming on enzyme kinetics and the analysis of the results, we decoded a Bi-Uni rate law (a somewhat more elaborate derivative of the Michaelis-Menten approach) would be able to account for the ABTS kinetics This change indeed dramatically improved the match between model predictions and experimental results, and this is what we used for our ensemble modelling studies.
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