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.本数据集包含支撑论文《面向微生物肿瘤微环境重编程与免疫优化的约束驱动设计的定量生物物理框架》的完整计算资源。本存档包含本研究中用于生成数值结果、图表及相空间分析的全部源代码、参数集与仿真工作流。 本数据集的材料分为三个核心组件： microbiotic_model_core.zip：实现肿瘤-免疫-酸碱度耦合模型的核心动力学系统，包含： 常微分方程（Ordinary Differential Equation, ODE）与反应扩散建模公式 微生物碱化机制的酶动力学表征 稳定性分析工具（雅可比矩阵求解、特征值谱分析） 定义生理与病理状态区间的基准参数集 microbiotic_prediction_framework.zip：预测建模与参数推断框架，包含： 敏感性分析与参数扫描工具 贝叶斯后验推断与不确定性量化 相边界检测与状态分类工具 生成肿瘤-免疫结局概率预测的脚本 microbiotic_predictive_control.zip：控制论与优化模块，包含： 面向微生物给药策略的模型预测控制（Model Predictive Control, MPC）实现 碱化与免疫调节的约束驱动优化 治疗成败预测的早期响应分类器 连接控制输入与系统定性转变的仿真流水线 上述数据集共同构成了论文中提出的生物物理框架的完整可复现实现，可独立验证全部数值结果，并支持将该建模框架拓展至其他微生物菌株、肿瘤微环境参数或治疗目标。 所有仿真均基于Python及标准科学计算库（NumPy、SciPy、Matplotlib）完成。每个存档文件中均包含详细文档与示例脚本，以方便复用与可复现性研究。 本存档建立了本研究理论与数值基础的永久可引用计算记录。 许可证：MIT许可证。本软件可在注明出处的前提下自由使用、修改与再分发。