Skip to main content

Bueno-Orovio model.

Project description

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

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 potential
  • v = 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

  • 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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

finitewave_model_bueno_orovio-0.2.0.tar.gz (10.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

finitewave_model_bueno_orovio-0.2.0-py3-none-any.whl (7.2 kB view details)

Uploaded Python 3

File details

Details for the file finitewave_model_bueno_orovio-0.2.0.tar.gz.

File metadata

File hashes

Hashes for finitewave_model_bueno_orovio-0.2.0.tar.gz
Algorithm Hash digest
SHA256 f4fbac07d79381e96af7e497475da3089f9f224a29c9762c1dc14615de3e88a4
MD5 d4a7161417f25801efb06b7d3b30af52
BLAKE2b-256 4d5a2f135d2e165a17f494256a3df5a8704add20f57a5efbd2c2f4ff49fb5208

See more details on using hashes here.

File details

Details for the file finitewave_model_bueno_orovio-0.2.0-py3-none-any.whl.

File metadata

File hashes

Hashes for finitewave_model_bueno_orovio-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 3c60ab0dbb4011eac711ea19f5e46840e7259fc10db5b04f74d49fd7772da91f
MD5 b6c7ef913daca8bea62688d04b1dec36
BLAKE2b-256 25cb126a6844c5007092e4261a4dd798eef787342e9a8490a15bdb7b5f324e36

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page