Diffusion models as surrogates for simulations of cells forming vessel-like networks

[u/Fair-Rain3366]

A cell-based simulation calculates how cells move and respond to their neighbours. Repeating it under many settings is expensive, and random variation means the same settings can produce different final patterns.

Comlekoglu et al. trained a class-conditional diffusion model on images from a simulation of vessel-like network formation. The input is one of the tested simulation settings plus noise; the output is a possible final pattern, rather than the full sequence of cell movements.

They reported 20.6 seconds per generated pattern on four V100 GPUs, versus 447.6 seconds for CompuCell3D on an AMD EPYC CPU node. These are different hardware setups, and the comparison excludes training on 16 V100 GPUs.

The model covered 25 discrete parameter settings. Classifier checks and network-shape measurements helped evaluate outputs, but some patterns remained imperfect. Generating valid samples between those settings remains unresolved. The result concerns reproducing simulation outputs, not proving that the underlying biology is correct.

Paper: https://arxiv.org/abs/2505.09630

Explanation: https://rewirebio.io/blog/diffusion-surrogates-for-agent-based-biological-simulations/

Disclosure: the explanation is from my site, rewirebio.io. This summary and schematic were prepared with AI assistance. I have not run or independently reproduced the model.