Creating predictive computational models of the heart is a formidable multiscale challenge, requiring the integration of cellular dynamics, tissue mechanics, and systemic hemodynamics. Researchers frequently struggle with disjointed, proprietary, or highly customized software pipelines to couple these diverse physical domains. To overcome these barriers, we introduce a cohesive, open-source Python ecosystem powered by the FEniCSx finite element framework, tailored specifically to standardize and accelerate computational cardiology workflows.
Our suite offers interoperable packages addressing the complete cardiac modeling pipeline. For anatomy and microstructure, cardiac-geometriesx enables the rapid generation of parameterized ventricular meshes, complemented by fenicsx-ldrb for incorporating rule-based myocardial fiber orientations. For electrophysiology, gotranx automates the translation of complex cellular ion channel ODEs into performant code, while fenicsx-beat handles tissue-level electrical propagation via the monodomain model. To capture mechanics and hemodynamics, fenicsx-pulse delivers a flexible framework for simulating nonlinear, anisotropic cardiac mechanics, and circulation dynamically couples these 3D organ models to 0D lumped-parameter cardiovascular circuits.
A defining pillar of this ecosystem is its rigorous adherence to modern software engineering best practices, ensuring trust and reproducibility. All libraries are comprehensively documented, backed by continuous integration testing, and distributed via standard package managers (PyPI, Conda, Spack). Supporting this framework, scifem provides essential finite element utilities, while io4dolfinx ensures scalable, parallel input/output for checkpointing large-scale simulations. Ultimately, this open ecosystem democratizes access to advanced, fully coupled electromechanical simulations.