  The complexity and size of state-of-the-art cell models have significantly
increased in part due to the requirement that these models possess complex
cellular functions which are thought--but not necessarily proven--to be
important. Modern cell models often involve hundreds of parameters; the values
of these parameters come, more often than not, from animal experiments whose
relationship to the human physiology is weak with very little information on
the errors in these measurements. The concomitant uncertainties in parameter
values result in uncertainties in the model outputs or Quantities of Interest
(QoIs). Global Sensitivity Analysis (GSA) aims at apportioning to individual
parameters (or sets of parameters) their relative contribution to output
uncertainty thereby introducing a measure of influence or importance of said
parameters. New GSA approaches are required to deal with increased model size
and complexity; a three stage methodology consisting of screening (dimension
reduction), surrogate modeling, and computing Sobol' indices, is presented. The
methodology is used to analyze a physiologically validated numerical model of
neurovascular coupling which possess 160 uncertain parameters. The sensitivity
analysis investigates three quantities of interest (QoIs), the average value of
$K^+$ in the extracellular space, the average volumetric flow rate through the
perfusing vessel, and the minimum value of the actin/myosin complex in the
smooth muscle cell. GSA provides a measure of the influence of each parameter,
for each of the three QoIs, giving insight into areas of possible physiological
dysfunction and areas of further investigation.
