Speaker
Description
Biological systems undergo dynamic developmental processes involving shape growth and deformation. Understanding these shape changes is key to exploring developmental mechanisms and factors influencing morphological change. One such phenomenon is the formation of the anterior-posterior (A-P) body axis of an embryo through symmetry breaking, elongation, and polarized Brachyury gene expression. This process can be modeled using stem-cell-derived mouse gastruloids (Veenvliet et al.; Science, 2020), which may form one or several A-P axes, modeling both development and disease.
We propose a way of quantifying and comparing continuous shape development in space and time. We emphasize the necessity of a structure-preserving metric that captures shape dynamics and accounts for observational invariances, such as rotation and translation.
The proposed metric compares the time-dependent probability distributions of different geometric features, such as curvature and elongation, in a rotationally invariant manner using the signed distance function of the shape over time. This enables the integration of time-dependent probability distributions of gene expression, thus coupling geometric and genetic features.
Importantly, the metric is differentiable by design, rendering it suitable for use in machine-learning models, particularly autoencoders. This allows us to impose the structure of the shape dynamics in a latent-space representation.
We benchmark the metric's effectiveness on synthetic data of shape classification, validating its correctness. We then apply the new metric to quantifying A-P body axis development in mouse gastruloids and predict the most likely resulting shapes. This approach can potentially leverage predictive control, enabling the application of perturbations to guide development towards desired outcomes.
| Authors | Roua Rouatbi*,Juan Esteban Suarez,Ivo F. Sbalzarini |
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| Keywords | Biological dynamics, Gastruloids, Organoids, Shape development, morphogenic dynamics, Gene Expression |
