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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Generative diffusion model surrogates for mechanistic agent-based biological models.

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

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

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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.