This research attempts to span the gap between the AI in medicine (AIM) and consistency-based diagnosis (CBD) communities by applying CBD to physiology. The highly-regulated nature of physiological systems challenges standard CBD algorithms, which are not tailored for complex dynamic systems. To combat this problem, we separate static from dynamic analysis, so that CBD is performed over the steady-state constraints at only a selected set of time slices. Regulatory models help link static inter-slice diagnoses into a complete dynamic account of the physiological progression. This provides a simpler approach to CBD in dynamic systems that (a) preserves information-reuse capabilities, (b) extends information-theoretic probing, and (c) adds a new capability to CBD: the detection of dynamic faults (i.e., those that do not necessarily persist throughout diagnosis) .