A Quantitative Biophysical Framework for Constraint-Driven Design of Microbial Tumor Microenvironment Reprogramming and Immunological Optimization

This dataset contains the complete computational resources supporting the manuscript “A Quantitative Biophysical Framework for Constraint-Driven Design of Microbial Tumor Microenvironment Reprogramming and Immunological Optimization.” The deposit includes all source code, parameter sets, and simulation workflows used to generate the numerical results, figures, and phase-space analyses presented in the study. The materials are organized into three main components: microbiotic_model_core.zipCore dynamical system implementing the coupled tumor–immune–acidity model.Includes: Ordinary differential equation (ODE) and reaction–diffusion formulations Enzyme-kinetic representations of microbial alkalinization mechanisms Stability analysis tools (Jacobian evaluation, eigenvalue spectra) Baseline parameter sets defining physiological and pathological regimes microbiotic_prediction_framework.zipPredictive modeling and parameter inference framework.Includes: Sensitivity analysis and parameter sweep utilities Bayesian posterior inference and uncertainty quantification Phase-boundary detection and regime classification tools Scripts for generating probabilistic forecasts of tumor–immune outcomes microbiotic_predictive_control.zipControl-theoretic and optimization modules.Includes: Model Predictive Control (MPC) implementations for microbial dosing strategies Constraint-driven optimization of alkalinization and immunomodulation Early-response classifiers for therapeutic success/failure prediction Simulation pipelines linking control inputs to qualitative system transitions Together, these datasets provide a complete, reproducible implementation of the biophysical framework developed in the manuscript. They enable independent verification of all numerical results and allow extension of the modeling framework to alternative microbial strains, tumor microenvironment parameters, or therapeutic objectives. All simulations were performed using Python and standard scientific computing libraries (NumPy, SciPy, Matplotlib). Detailed documentation and example scripts are included within each archive to facilitate reuse and reproducibility. This deposit establishes a permanent, citable computational record of the theoretical and numerical foundations of the study. License: MIT License. The software is freely available for use, modification, and redistribution with attribution.