  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.
