Surrogate modeling of Cellular-Potts Agent-Based Models as a segmentation task using the U-Net neural network architecture
This work addresses the problem of slow simulations for researchers in computational biology, offering a significant speed-up but is incremental as it applies an existing neural network architecture to a specific domain.
The authors tackled the computational expense of Cellular-Potts models (CPMs) by developing a U-Net-based convolutional neural network surrogate model, achieving a 590x acceleration in simulation evaluations while capturing emergent behaviors like vessel sprouting and anastomosis.
The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 590 times compared to CPM code execution. Over multiple recursive evaluations, our model effectively captures the emergent behaviors demonstrated by the original Cellular-Potts model of such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM of biological processes at greater spatial and temporal scales.