Generative diffusion model surrogates for mechanistic agent-based biological models

TL;DR

This paper introduces a generative diffusion model surrogate for agent-based biological models, significantly reducing computational costs and enabling faster exploration of complex tissue simulation parameters.

Contribution

It applies denoising diffusion probabilistic models to create a surrogate for a stochastic CPM, improving efficiency and aiding model verification.

Findings

Surrogate generates configurations 20,000 timesteps ahead

Achieves approximately 22x reduction in computational time

Uses an image classifier for parameter space characterization

Abstract

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.