To resolve these issues, this paper presents a model evaluation and uncertainty propagation approach that uses a novel combination of data clustering, model parameter fine-tuning, model screening and ranking, model uncertainty quantification, and Monte Carlo simulation methods.
Parameter variations have become essential in the design of micro- and nano-electronic (-mechanical) systems as well as of coupled electro-thermal problems, since in many analyses such as optimization and uncertainty quantification, modeling and simulation at many values of the parameters are unavoidable.
Experimental procedures for MecI and BlaI (cloning and purification), MecI ultracentrifugation experiments (sedimentation velocity and sedimentation equilibrium), MecI quantification in S. aureus cells, fluorescence anisotropy experiments, and simulation of species.
To realize the benefits of modeling and simulation, concerted efforts in the areas of uncertainty quantification and sensitivity analysis are required.
The results of this study confirm that a modelling and simulation approach should be used in guiding public health policies, since it allows quantification of the impact of diverse rules for treatment administration before they are applied.
With further quantification and characterization of the system, such simulations using this data-driven modeling paradigm will be critical for guiding experimental design, studying disease biology, and could similarly be used for pharmaco- and toxicodynamic studies.
Interestingly, the simulation allows the prediction (quantification and dynamic) of parameters that would be difficult to measure experimentally, such as the ICF and ECF volume.
Extended simulation data and simulation video.
