Image-based Modelling of Organogenesis

TL;DR

This paper presents an image-based modeling pipeline for organogenesis, integrating 3D imaging, segmentation, displacement analysis, and PDE solutions to understand morphogenesis across scales.

Contribution

It introduces a comprehensive pipeline combining imaging and mathematical modeling to study organ development and morphogenesis.

Findings

Developed a pipeline for image segmentation and displacement field determination.

Discussed challenges in mathematical model development and parameter inference.

Highlighted the potential of integrated models to understand complex biological processes.

Abstract

One of the major challenges in biology concerns the integration of data across length and time scales into a consistent framework: how do macroscopic properties and functionalities arise from the molecular regulatory networks - and how can they change as a result of mutations? Morphogenesis provides an excellent model system to study how simple molecular networks robustly control complex processes on the macroscopic scale in spite of molecular noise, and how important functional variants can emerge from small genetic changes. Recent advancements in 3D imaging technologies, computer algorithms, and computer power now allow us to develop and analyse increasingly realistic models of biological control. Here we present our pipeline for image-based modeling that includes the segmentation of images, the determination of displacement fields, and the solution of systems of partial differential equations (PDEs) on the growing, embryonic domains. The development of suitable mathematical models, the data-based inference of parameter sets, and the evaluation of competing models are still challenging, and current approaches are discussed.