Energetic-Mechanical Impedance in Vulnerable Nigral Dopamine Neurons: Computational Framework & Figures

Overview This repository contains the Python scripts and generated figures used to construct the conceptual and mathematical framework for a biophysical hypothesis in Parkinson's Disease. It serves as the companion computational repository for the manuscript: Energetic-Mechanical Impedance in Vulnerable Nigral Dopamine Neurons: A Biophysical Hypothesis for Parkinson's Disease (Prepared for Progress in Biophysics and Molecular Biology). The codebase translates the biological realities of the substantia nigra pars compacta (SNpc) dopamine neuron—specifically its massive unmyelinated arborization and autonomous calcium pacemaking—into a testable thermodynamic and viscoelastic framework. The Python scripts generate the rigorous conceptual schematics for inclusion assembly mechanics, interfacial strain (kinetic wedging), and the two-layer Neural Physiological Load Index (NLI). Critical Note This repository contains conceptual, hypothesis-generating schematics mapping proposed physical relationships. It does not contain empirical clinical measurements, in-vivo datasets, or fitted physical models of L-DOPA-induced heating, strain, or neuronal injury. Repository Contents Python Scripts (.py): Source code to procedurally generate the biological topology schematic, alpha-synuclein assembly boundaries, the two-layer thermodynamic framework flowchart, and the experimental falsification roadmap. Figures (.png / .pdf): High-resolution diagrams and schematics utilized in the manuscript.