Deep Neural Cellular Potts Models
This work addresses the problem of accurately simulating complex multicellular systems for researchers in computational biology, representing an incremental improvement by integrating neural networks into existing CPM frameworks.
The authors tackled the limitation of traditional cellular Potts models (CPMs) in simulating biological cell dynamics by proposing NeuralCPM, a model with a neural network-based Hamiltonian trained on observational data, which successfully modeled dynamics unaccounted for by analytical Hamiltonians.
The cellular Potts model (CPM) is a powerful computational method for simulating collective spatiotemporal dynamics of biological cells. To drive the dynamics, CPMs rely on physics-inspired Hamiltonians. However, as first principles remain elusive in biology, these Hamiltonians only approximate the full complexity of real multicellular systems. To address this limitation, we propose NeuralCPM, a more expressive cellular Potts model that can be trained directly on observational data. At the core of NeuralCPM lies the Neural Hamiltonian, a neural network architecture that respects universal symmetries in collective cellular dynamics. Moreover, this approach enables seamless integration of domain knowledge by combining known biological mechanisms and the expressive Neural Hamiltonian into a hybrid model. Our evaluation with synthetic and real-world multicellular systems demonstrates that NeuralCPM is able to model cellular dynamics that cannot be accounted for by traditional analytical Hamiltonians.