Generative diffusion model surrogates for mechanistic agent-based biological models

• URL: http://arxiv.org/abs/2505.09630v2
• Date: Wed, 18 Jun 2025 04:13:19 GMT
• Title: Generative diffusion model surrogates for mechanistic agent-based biological models
• Authors: Tien Comlekoglu, J. Quetzalcoatl Toledo-Marín, Douglas W. DeSimone, Shayn M. Peirce, Geoffrey Fox, James A. Glazier,
• Abstract summary: The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating CPMs.<n>Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems.<n>We train a generative AI surrogate classifier of a CPM used to investigate in vitro vasculogenesis.<n>Our work represents a step towards the implementation of DDPMs to develop digital twins of biological systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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