Many applications in the life sciences involve unknown system parameters that must be estimated using little to no prior information. In addition, these parameters may be time-varying and possibly subject to structural characteristics such as periodicity. We show how nonlinear Bayesian filtering techniques can be employed in this setting to estimate unknown, time-varying parameters, while naturally providing a measure of uncertainty in the estimation. Results are demonstrated with real data from several biological applications, including cardiovascular dynamics and the modeling of infectious diseases.