Unknown

Dataset Information

0

Modeling enamel matrix secretion in mammalian teeth.


ABSTRACT: The most mineralized tissue of the mammalian body is tooth enamel. Especially in species with thick enamel, three-dimensional (3D) tomography data has shown that the distribution of enamel varies across the occlusal surface of the tooth crown. Differences in enamel thickness among species and within the tooth crown have been used to examine taxonomic affiliations, life history, and functional properties of teeth. Before becoming fully mineralized, enamel matrix is secreted on the top of a dentine template, and it remains to be explored how matrix thickness is spatially regulated. To provide a predictive framework to examine enamel distribution, we introduce a computational model of enamel matrix secretion that maps the dentine topography to the enamel surface topography. Starting from empirical enamel-dentine junctions, enamel matrix deposition is modeled as a diffusion-limited free boundary problem. Using laboratory microCT and synchrotron tomographic data of pig molars that have markedly different dentine and enamel surface topographies, we show how diffusion-limited matrix deposition accounts for both the process of matrix secretion and the final enamel distribution. Simulations reveal how concave and convex dentine features have distinct effects on enamel surface, thereby explaining why the enamel surface is not a straightforward extrapolation of the dentine template. Human and orangutan molar simulations show that even subtle variation in dentine topography can be mapped to the enamel surface features. Mechanistic models of extracellular matrix deposition can be used to predict occlusal morphologies of teeth.

SUBMITTER: Hakkinen TJ 

PROVIDER: S-EPMC6541238 | BioStudies | 2019-01-01

REPOSITORIES: biostudies

Similar Datasets

2013-01-01 | S-EPMC3691165 | BioStudies
2015-01-01 | S-EPMC4634312 | BioStudies
2019-01-01 | S-EPMC6790768 | BioStudies
2017-01-01 | S-EPMC5558931 | BioStudies
2017-01-01 | S-EPMC5519568 | BioStudies
2018-01-01 | S-EPMC6169863 | BioStudies
1000-01-01 | S-EPMC2885206 | BioStudies
2015-01-01 | S-EPMC4406681 | BioStudies
2019-01-01 | S-EPMC6821052 | BioStudies
1000-01-01 | S-EPMC3878928 | BioStudies