  Mathematical models can provide quantitative insight into immunoreceptor
signaling, but require parameterization and uncertainty quantification before
making reliable predictions. We review currently available methods and software
tools to address these problems. We consider gradient-based and gradient-free
methods for point estimation of parameter values, and methods of profile
likelihood, bootstrapping, and Bayesian inference for uncertainty
quantification. We consider recent and potential future applications of these
methods to systems-level modeling of immune-related phenomena.
