Accounting for variability and uncertainty in a complex brain metabolic model via a probabilistic framework
In this talk we propose a probabilistic interpretation of the parameters in the system of differential equations describing a complex cellular brain metabolism model. Uncertainty in the parameter values, variability of the data over a population and errors in the collected data contribute to the variance of the distributions of the parameters. Markov chain Monte Carlo sampling schemes are employed to draw parameter sets which identify models in statistical agreement with the available data and with the a priori belief about the system. The ensemble of solutions of the differential equations corresponding to the different parameter sets provides a measure of how uncertainty in the parameters translates into variability of the predictive output of the model.