Generative diffusion model surrogates for mechanistic agent-based biological models

2025-05-01

AI Summary

Cellular-Potts model (CPM)-based in vitro angiogenesis simulation suffers from inherent stochasticity and massive spatiotemporal computational demands, hindering effective surrogate modeling. To address this, we propose the first generative surrogate model for CPM grounded in denoising diffusion probabilistic models (DDPMs). Our method treats CPM’s stochastic outputs as image distributions, enabling DDPMs to learn high-dimensional configuration dynamics; a 2D image classifier further guides parameter-space partitioning and surrogate validation. Experiments demonstrate that the surrogate generates 20,000-step, single-cell-resolution spatial configurations autoregressively—achieving ~22× speedup in inference time versus full CPM simulation. This work extends the applicability of diffusion models to stochastic multicellular systems and establishes a verifiable, high-fidelity, and computationally efficient simulation paradigm for digital twins of biological systems.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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 extit{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.

Problem

• Accelerate Cellular-Potts Model evaluation using generative surrogates
• Overcome stochastic variability in agent-based biological model outputs
• Reduce computational time for large-scale biological simulations

Innovation

• Generative diffusion models for agent-based surrogates
• Image classifier aids parameter space analysis
• 22x faster surrogate with 20,000-step prediction