Hair follicle (HF) is the model system of choice for studying mechanisms of regeneration. Each HF features a prominent stem cell compartment and a tractable regeneration cycle, consisting of the anagen, catagen, and telogen phases. To-date, the fundamental mechanism underlying the timing of hair growth, aka “hair cycle clock” remains largely unknown. One possibility is that the hair cycle clock is composed of two or more activator and inhibitor signaling pathway pairs and that key hair cycle phase transitions occur at certain cumulative thresholds for these pathway activities. Herein we developed a mathematical model that accounts for natural HF geometry and cell dynamics. Incorporating activator/inhibitor signals in the context of this model produces stable periodicity and excitability – hallmark features of the natural hair cycle. In the context of our model, activator/inhibitor signals were predicted to have opposing effects on anagen and telogen phase periodicity. Increasing activator levels was predicted to shorten telogen and lengthen anagen. The inverse effects were modeled for an inhibitor. Here, we focused on anagen phase length and validated these predictions for BMP/WNT signaling pair. Using mouse models we show that decreasing inhibitory BMP signaling leads to the production of longer hairs, thus indicating a longer anagen. We also demonstrate that decreasing activating WNT signaling in mutant mice results in shorter hairs. Finally, we showed that some hair types were the most sensitive to changes in BMP levels than others, suggesting differential effects of BMP modulation. Simulations of this phenomenon suggests that changes in just one background model parameter is sufficient to recapitulate differential sensitivity of hair types to the same net change in the activator/inhibitor signaling levels. Taken together, we provide the first example of a diffusion-based mathematical model that accounts for realistic changes in HF geometry, displays stable periodicity and excitability. It creates a novel opportunity for studying the hair cycle clock mechanism using Systems Biology approach.