Hair follicles are stem cell-rich skin mini-organs that can undergo oscillation-like cycles of regeneration throughout their lifetimes. In mammals, hairs have evolved for thermoregulation, camouflage, display, and mechanical protection. Importantly, hair follicle has emerged as a leading model system for studying general mechanisms of stem cell control, tissue patterning during morphogenesis, regeneration and aging. Though powerful, traditional reductionist research approaches are limited by technological barriers and high experiment costs. This project aims to develop an interdisciplinary approach between computational modeling and experimentations to understand crucial questions that arise from the growth control of the follicle and stem cell lineage dynamics. It is anticipated that the new mathematical model can be generalized to study multiple aspects of skin biology, beyond hair growth. The new theory on the role of cellular response to dynamic growth regulation will have wide applications to other biological growth systems. This project will also provide students with the opportunities to get exposed to state-of-the-art interdisciplinary research and will greatly promote diversity in mathematics and biological sciences by recruiting students from underrepresented groups.
This project will study the control mechanisms of hair follicle growth dynamics both at spatial and temporal levels by adopting an interdisciplinary research approach between computational multiscale modeling and single-cell RNA-sequencing experiments. On the modeling side, a coupled stochastic partial differential equation sub-model for signaling dynamics and a discrete off-lattice sub-model using Voronoi tessellation technique for inter-cellular interactions will be developed. Several numerical coupling algorithms between the continuum and discrete sub-models will be evaluated, which will greatly benefit broader multiscale modeling research in biological developmental systems. On the experiment side, new single-cell RNA-sequencing data will be collected to calibrate the model and validate model predictions. Using this interdisciplinary approach, a novel growth control mechanism will be verified, which states that cells’ heterogeneous responses to signals play a critical regulatory role in the follicle growth dynamics. This project will provide significant insight into the biological problems, including spatially defining the maximum length of a follicle, temporally regulating the periodic follicle growth dynamics and mechano-chemical coupling in biological growth. Finally, by merging multiscale modeling with single-cell RNA-sequencing results, this project will also demonstrate a new path for synthesizing modeling and biological data in broader interdisciplinary research.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
