Bueno-Orovio finitewave model
Two-dimensional implementation of the Bueno-Orovio–Cherry–Fenton (BOCF) model for simulating human ventricular tissue electrophysiology.
The BOCF model is a minimal phenomenological model developed to capture key ionic mechanisms and reproduce realistic human ventricular action potential dynamics, including restitution, conduction block, and spiral wave behavior. It consists of four variables: transmembrane potential (u), two gating variables (v, w), and one additional slow variable (s), representing calcium-related dynamics.
This model implementation can be used separately from the Finitewave, allowing for standalone simulations and testing of the model dynamics without the need for the entire framework.
Reference
Bueno-Orovio, A., Cherry, E. M., & Fenton, F. H. (2008). Minimal model for human ventricular action potentials in tissue. J Theor Biol., 253(3), 544-60.
DOI: https://doi.org/10.1016/j.jtbi.2008.03.029
Modification of the model for atrial tissue:
Lenk, C., Weber, F. M., Bauer, M., Einax, M., Maass, P., & Seeman, G. (2015). Initiation of atrial fibrillation by interaction of pacemakers with geometrical constraints. Journal of Theoretical Biology, 366, 13-23.
DOI: https://doi.org/10.1016/j.jtbi.2014.10.030
How to use (quickstart)
python -m examples.bueno_orovio_example
How to test
python -m pytest -q
Repository structure
.
├── bueno_orovio/ # equations package (ops.py)
│ ├── __init__.py
│ └── ops.py # model equations (pure functions)
├── implementation/ # 0D model implementation
│ ├── __init__.py
│ └── bueno_orovio_0d.py
├── example/
│ └── bueno_orovio_example.py # minimal script to run a short trace
├── tests/
│ └── test.py # smoke test; reproducibility checks
├── .gitignore
├── LICENSE # MIT
├── pyproject.toml # configuration file
└── README.md # this file
Variables
u = 0.0— Membrane potentialv = 1.0- Fast gating variable representing sodium channel inactivation.w = 1.0- Slow recovery variable representing calcium and potassium gating.s = 0.0- Slow variable related to calcium inactivation.
Parameters
This implementation corresponds to the EPI (epicardial) parameter set described in the paper.
u_o = 0.0- Resting membrane potential.u_u = 1.55- Peak potential (upper bound).theta_v = 0.3- Activation threshold for v.theta_w = 0.13- Activation threshold for w.theta_v_m = 0.006- Threshold for switching time constants for v.theta_o = 0.006- Threshold for switching time constants for w.tau_v1_m = 60- Time constant for v below threshold.tau_v2_m = 1150- Time constant for v above threshold.tau_v_p = 1.4506- Decay constant for v.tau_w1_m = 60- Base time constant for w.tau_w2_m = 15- Transition time constant for w.k_w_m = 65- Parameter controlling shape of τw curve.u_w_m = 0.03- Parameter controlling shape of τw curve.tau_w_p = 200- Decay constant for w above threshold.tau_fi = 0.11- Time constant for fast inward current (J_fi).tau_o1 = 400- Time constant for outward current below threshold.tau_o2 = 6- Time constant for outward current above threshold.tau_so1 = 30.0181- Time constant for repolarizing tail current below threshold.tau_so2 = 0.9957- Time constant for repolarizing tail current above threshold.k_so = 2.0458- Parameter controlling nonlinearity in tau_so.u_so = 0.65- Parameter controlling nonlinearity in tau_so.tau_s1 = 2.7342- Time constant for s below threshold.tau_s2 = 16- Time constant for s above threshold.k_s = 2.0994- Parameter for tanh activation of s variable.u_s = 0.9087- Parameter for tanh activation of s variable.tau_si = 1.8875- Time constant for slow inward current (J_si).tau_w_inf = 0.07- Slope of w∞ below threshold.w_inf_ = 0.94- Asymptotic value of w∞ above threshold.
Metadata
Release files for finitewave-model-bueno-orovio 0.6.0
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| finitewave_model_bueno_orovio-0.6.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 109.8 kB
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