  This study combines a one-dimensional (1D) model with micro-CT imaging and
hemodynamic data to quantify uncertainty of flow and pressure predictions in
the pulmonary arteries in a control and hypoxia induced hypertensive mouse. We
use local and global sensitivity and correlation analysis to determine
parameters that can be inferred from the model and available data. Least
squares optimization is used to estimate mouse specific parameters, and
Bayesian as well as asymptotic uncertainty quantification techniques are
employed to determine confidence, credible, and prediction intervals for the
model parameters and response. These techniques are used to examine the effects
of network size and to understand how parameters change with disease
(hypertension). Results showed that the peripheral vascular resistance is the
most sensitive, and as the network size increases the parameter behavior
changes. Correlation analysis revealed that in hypertension large vessel
stiffness is correlated with proximal resistance in the boundary. We were able
to estimate identifiable parameters using both deterministic and Bayesian
techniques (the maxima of the parameter distributions determined using Bayesian
analysis aligned with local optima). From these estimates we determined
confidence and prediction intervals, which all were within physiological
expectation. Analysis of estimated parameter values for the representative mice
studied here showed that the hypertensive mouse has stiffer (but larger)
vessels and that compliance is decreased both in the proximal and peripheral
vasculature.
