TumorResistanceOS v1.4: Event-Triggered Control Framework for Investigating Hysteresis-Driven Resistance in the Tumor Microenvironment (Camera-Ready Manuscript & Research Artifact Release)

TumorResistanceOS v1.4 is a reproducible multiscale computational framework for investigating hysteresis-driven resistance dynamics in the tumor microenvironment (TME). Rather than reconstructing tumor biology in full mechanistic detail, the framework provides an executable operational representation of multiscale biological feedback processes, enabling controlled computational experiments, hypothesis generation, and reproducible systems-level analysis. The framework is organized into a conceptual systems architecture and an executable computational implementation comprising three coupled computational layers: Macro-scale transport (oxygen diffusion and microenvironmental fields) Meso-scale mechanochemical dynamics (ECM remodeling and biomechanical feedback) Micro-scale intracellular regulation (stress-responsive signaling dynamics) These layers are connected through explicit cross-scale coupling operators, allowing systematic investigation of spatial heterogeneity, mechanochemical reinforcement, nonlinear transitions, hysteresis, and event-triggered sequential intervention strategies under controlled computational conditions. This Camera-Ready Research Artifact Release includes all materials required for reproducible computational research: Camera-ready manuscript (PDF) Publication-quality figures (SVG, PDF, PNG) Supplementary Information Mathematical formulation Version-controlled parameter sets Configuration files Computational experiment protocols Reproducibility environment specifications Source code implementing TumorResistanceOS Metadata describing the complete research artifact The implementation emphasizes modularity, deterministic execution, explicit state interfaces, transparent parameterization, and hardware-independent reproducibility. Model evaluation includes numerical verification, qualitative biological consistency, robustness analyses, subsystem ablation studies, and computational hypothesis generation. TumorResistanceOS is intended as an extensible computational research platform rather than a comprehensive mechanistic reconstruction of tumor biology. The framework supports future extensions including stochastic dynamics, three-dimensional spatial domains, expanded signaling networks, vascular and immune interactions, and integration with experimental or clinical datasets. Keywords: multiscale modeling, tumor microenvironment, hysteresis, mechanochemical feedback, systems biology, computational framework, reproducibility, event-triggered control, computational oncology, systems medicine.