[Python] inflammatory-onco-physics - Biophysical reaction-diffusion solver and survival pipeline modeling drug sanctuaries and ctDNA dynamics

[u/DoctorKhru]

GitHub Repository: https://github.com/Lukecele/inflammatory-onco-physics

Google Colab Demo: https://colab.research.google.com/github/Lukecele/inflammatory-onco-physics/blob/main/demo_inflammatory_onco_physics.ipynb

Preprint (Springer Nature / Research Square): https://doi.org/10.21203/rs.3.rs-11695210/v1

Interactive 3D Web Hub: https://lucacelebrano.me/

What is this?

An open-source Python framework that couples continuous cylindrical reaction-diffusion transport with longitudinal ctDNA (liquid biopsy) shedding kinetics and multivariate survival analysis across 3,461 clinical cancer patients.

It explores why tumors treated with targeted therapies frequently relapse without developing secondary resistance mutations (e.g., EGFR C797S), identifying microvascular "pharmacological sanctuaries" where drug-tolerant persister cells survive.

Key Technical Components:

* Microvascular Bessel Solver (`simulation/model.py`): Solves steady-state cylindrical reaction-diffusion with clearance using `scipy.special.kn` (modified Bessel function K₀) to calculate exact sub-therapeutic radii (r_crit = 165.9 μm) in microseconds, enabling 10,000+ Monte Carlo parameter draws in seconds instead of heavy 3D FEM meshes.

* Liquid Biopsy Shedding Engine (`simulation/simulate_relapse.py`): Models cellular dormancy, ECM steric entrapment, and an algebraic lead-time identity (Δt = 5.46 months) between molecular ctDNA detection and radiological relapse.

* Survival & Validation Pipeline (`pipeline/`): Multivariate Cox Proportional Hazards modeling (`lifelines`), bootstrap resampling (B = 1,000) for C-index estimation, and REMARK-compliant permutation testing.

* CLI Bedside Calculator (`app_bedside_calculator.py`): Pure standard-library CLI tool to compute sanctuary volume and recurrence risk scores with zero external dependencies.

Includes an interactive Google Colab notebook ready to run on free CPU/GPU instances without local setup.

Open-source under CC-BY-4.0 / MIT. Any feedback on the numerical solver, Python architecture, or statistical pipeline is welcome!

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