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  3. Generative Diffusion Model Surrogates For Mechanistic Agent-based Biological Models.
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  3. Generative Diffusion Model Surrogates For Mechanistic Agent-based Biological Models.

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Tien Comlekoglu1,2, J Quetzalcoatl Toledo-Marín3,4, Douglas W DeSimone2

  • 1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America.

Machine Learning: Science and Technology
|October 31, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study uses generative AI, specifically denoising diffusion probabilistic models (DDPMs), to create faster surrogate models for complex biological simulations like the Cellular-Potts Model (CPM). This AI approach significantly reduces computation time for investigating systems such as in vitro vasculogenesis.

Keywords:
agent-based modeldenoising diffusion modeldenoising diffusion probabilistic modeldigital twingenerativevasculogenesis

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Area of Science:

  • Computational Biology
  • Artificial Intelligence
  • Biophysics

Background:

  • Mechanistic, multicellular, agent-based models (MCMs) like the Cellular-Potts Model (CPM) are crucial for single-cell resolution biological investigations.
  • Computational expense of MCMs at large scales hinders their application.
  • Stochasticity in MCMs complicates the development of surrogate models.

Purpose of the Study:

  • To develop a generative AI surrogate model for CPMs using denoising diffusion probabilistic models (DDPMs).
  • To accelerate the evaluation of complex biological systems simulated by CPMs.
  • To enable the creation of digital twins for stochastic biological systems.

Main Methods:

  • Leveraged denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM.
  • Employed an image classifier to identify unique regions within a 2D parameter space.
  • Utilized the classifier for surrogate model selection and verification.

    • Main Results:

    • The DDPM-based surrogate model successfully generated CPM configurations 20,000 timesteps ahead of a reference.
    • Achieved an approximate 22x reduction in computational time compared to native CPM code execution.
    • Demonstrated the feasibility of using AI for accelerating complex biological simulations.

    Conclusions:

    • DDPMs can be effectively implemented to create efficient surrogate models for stochastic agent-based models.
    • This approach significantly reduces computational burden, facilitating the study of complex biological processes.
    • The developed surrogate models represent a significant step towards creating accurate digital twins of biological systems.