A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy

TL;DR

This paper introduces a parallel computational method for simulating personalized breast cancer therapy using a hybrid cellular automaton model, enabling large-scale, efficient, and reproducible simulations that incorporate patient-specific data.

Contribution

The authors developed parallel algorithms based on FEniCS to efficiently couple stochastic cellular automata with differential equations, significantly improving scalability for complex cancer models.

Findings

Nearly linear scaling on single-core processors.

Moderate growth in weak parallel scaling.

Enabled simulation of 500 times larger problems than previous work.

Abstract

Mathematical modeling and simulation is a promising approach to personalized cancer medicine. Yet, the complexity, heterogeneity and multi-scale nature of cancer pose significant computational challenges. Coupling discrete cell-based models with continuous models using hybrid cellular automata is a powerful approach for mimicking biological complexity and describing the dynamical exchange of information across different scales. However, when clinically relevant cancer portions are taken into account, such models become computationally very expensive. While efficient parallelization techniques for continuous models exist, their coupling with discrete models, particularly cellular automata, necessitates more elaborate solutions. Building upon FEniCS, a popular and powerful scientific computing platform for solving partial differential equations, we developed parallel algorithms to link stochastic cellular automata with differential equations ( https://bitbucket.org/HTasken/cansim ). The algorithms minimize the communication between processes that share cellular automata neighborhood values while also allowing for reproducibility during stochastic updates. We demonstrated the potential of our solution on a complex hybrid cellular automaton model of breast cancer treated with combination chemotherapy. On a single-core processor, we obtained nearly linear scaling with an increasing problem size, whereas weak parallel scaling showed moderate growth in solving time relative to increase in problem size. Finally we applied the algorithm to a problem that is 500 times larger than previous work, allowing us to run personalized therapy simulations based on heterogeneous cell density and tumor perfusion conditions estimated from magnetic resonance imaging data on an unprecedented scale.