Instead of running our model with a set of specific parameters (for example rate constants.), we run our model multiple times using different sets of parameter values and analysed the predictions as an ensemble. We collected all the possible parameter values from published literature and took into account the uncertainties that are associated with them. These parameters where described by probability density functions. This has created probabilistic outputs allowing us to make rigorous conclusions about our reaction mechanism. Ensemble modelling has yet to ‘breakthrough’ into the world of biological modelling and systems biology in general. Essentially, we analyse multiple predictions from our model as a composite, which is different from traditional predictive modelling methods. Instead of running a single prediction from a fixed single set of kinetic parameter values, we run our AlcoPatch model a number of times from different sets of kinetic values by sampling from the probability distributions generated from the previous step. We then analyse the entire set of predictions as an ensemble to draw probabilistic conclusions about the system. To explore the theory of this process please click the boxes on the diagram below.
Alternatively you can click on the step name below: