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<p id = "MethodologyTitle" style="border-bottom: 1px black solid ;font-size:25px;text-weight:bold;display:inline-block">Methodology</p> | <p id = "MethodologyTitle" style="border-bottom: 1px black solid ;font-size:25px;text-weight:bold;display:inline-block">Methodology</p> | ||
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+ | <p>For each combination of different rate laws used to model our network, the following steps were taken: </br> | ||
1) Generate probability distributions for each kinetic parameter required. </br> | 1) Generate probability distributions for each kinetic parameter required. </br> | ||
2) Simulate the model with different sets of kinetic values that are sampled from probability distributions. In our study, 1000 samples for each reaction were modelled, i.e. the model was simulated with 1000 different sets of kinetic values. </br> | 2) Simulate the model with different sets of kinetic values that are sampled from probability distributions. In our study, 1000 samples for each reaction were modelled, i.e. the model was simulated with 1000 different sets of kinetic values. </br> |
Revision as of 14:11, 18 October 2016
Network Mechanism Analysis
Contents
Overview and Motivation Methodology Results ConclusionsOverview and Motivation
During discussions with the experimental team it became clear to us that the exact reaction mechanism in place was not clearly understood. By modelling a range of different potential mechanisms and comparing the outputs to experimental data we could draw conclusions about the accuracy of the mechanisms and hence refine the model in an effort to produce more accurate predictions and improve our understanding of the system.
The combinations of different rate laws used to model our reactions are as below:
Reaction One (GOx) | Reaction Two (HRP) |
---|---|
Irreversible Michaelis-Menten | Irreversible Michaelis-Menten |
Reversible Michaelis-Menten | Reversible Michaelis-Menten |
Uni-Bi Reversible Michaelis-Menten | Bi-Uni Reversible Michaelis-Menten |
Methodology
For each combination of different rate laws used to model our network, the following steps were taken: 1) Generate probability distributions for each kinetic parameter required. 2) Simulate the model with different sets of kinetic values that are sampled from probability distributions. In our study, 1000 samples for each reaction were modelled, i.e. the model was simulated with 1000 different sets of kinetic values. 3) Analysed the model by retrieving concentrations at reaction completion for each sample and plotted in a histogram. 4) Compared experimental data with our model predictions.
Return to top of pageResults
PLACEHOLDER FOR GRAPHS
Return to top of pageConclusions
From the graphs you can see that...
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