Abstract
AbstractSutures, the thin, soft tissue between skull bones, serve as the major craniofacial growth centers during postnatal development. In a newborn skull, the sutures are straight; however, as the skull develops, the sutures wind dynamically to form an interdigitation pattern. Moreover, the final winding pattern had been shown to have fractal characteristics. Although various molecules involved in suture development have been identified, the mechanism underlying the pattern formation remains unknown. In a previous study, we reproduced the formation of the interdigitation pattern in a mathematical model combining an interface equation and a convolution kernel. However, the generated pattern had a specific characteristic length, and the model was unable to produce a fractal structure with the model.In the present study, we focused on the anterior part of the sagittal suture and formulated a new mathematical model with time–space-dependent noise that was able to generate the fractal structure. We reduced our previous model to represent the linear dynamics of the centerline of the suture tissue and included a time–space-dependent noise term. We showed theoretically that the final pattern from the model follows a scaling law due to the scaling of the dispersion relation in the full model, which we confirmed numerically. Furthermore, we observed experimentally that stochastic fluctuation of the osteogenic signal exists in the developing skull, and found that actual suture patterns followed a scaling law similar to that of the theoretical prediction.Author summarySkull sutures (thin, undifferentiated tissue between bones) act as the growth centers for the skull. Sutures are straight at birth but later develop an interdigitated pattern that ultimately becomes a fractal structure. While our previous mathematical model of sutures generated a periodic pattern, the mechanism underlying the fractal structure formation remained to be elucidated. Here, we focused only on the anterior part of the sagittal suture and formulated a reduced model representing the initial linear phase of pattern formation with the addition of a time–space-dependent noise term. We showed analytically that the model generates patterns with a scaling law. This result was confirmed numerically and experimentally.
Publisher
Cold Spring Harbor Laboratory