The quantification of uncertainties inherent to biological and pharmacokinetic models, parameters, and experiments is critical to assess the accuracy of predictions. This presentation will focus on the use of sensitivity analysis and uncertainty quantification techniques to establish the predictive capabilities of selected models. The initial discussion will illustrate how sensitivity analysis and parameter subset selection techniques can be used to isolate model parameters, which are informed by measured data. To demonstrate the role of parameter selection techniques, we consider a minimal physiologically-based pharmacokinetic (mPBPK) model of the brain, which was developed by Bloomingdale, Bakshi, Maass, et al. (2021) to investigate antibody therapeutics. This example will illustrate both the reduction in parameter complexity, which can be achieved, and the dependence of parameter sensitivity on dosage. The presentation will then focus on the use of Bayesian inference techniques to quantify parameter uncertainties in a manner that can be subsequently employed to construct prediction intervals for model responses. This inverse and forward uncertainty analysis will be illustrated in the context of an HIV model. Open questions and future research directions will be noted throughout the presentation.