TumorResistanceOS v1.4: Production-Ready Multiscale Simulation Engine for Hysteresis-Driven Resistance Dynamics in the Tumor Microenvironment

TumorResistanceOS v1.4 is a production‑grade multiscale simulation engine modeling the emergence, stabilization, and collapse of maximal drug resistance in the tumor microenvironment (TME). The engine integrates: Continuous PDE fields for oxygen diffusion and ECM stiffness Discrete intracellular signaling dynamics (NF‑κB, Akt) Hill‑function nonlinearities for mechanotransduction, vascular suppression, metabolic oxygen consumption, and hypoxic response Event‑triggered hysteresis‑aware control logic enabling sequential therapeutic strategies This release includes a camera‑ready Python implementation, comparative simulation outputs, and figure assets suitable for academic publication, preprint deposition, and reproducible research workflows.Developed using the OAOL (O'Valley AI Orchestration Lab) framework, an AI-orchestrated research workflow for computational science and systems architecture.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-28
DOI
https://doi.org/10.5281/zenodo.21635157
Primary Topic
Mathematical Biology Tumor Growth
Type
preprint
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preprint

TumorResistanceOS v1.4: Production-Ready Multiscale Simulation Engine for Hysteresis-Driven Resistance Dynamics in the Tumor Microenvironment

Yuji Marutani
Zenodo (CERN European Organization for Nuclear Research)
Mathematical Biology Tumor Growth
preprint

TumorResistanceOS v1.4: Production-Ready Multiscale Simulation Engine for Hysteresis-Driven Resistance Dynamics in the Tumor Microenvironment

Yuji Marutani
preprint en

Abstract

TumorResistanceOS v1.4 is a production‑grade multiscale simulation engine modeling the emergence, stabilization, and collapse of maximal drug resistance in the tumor microenvironment (TME). The engine integrates: Continuous PDE fields for oxygen diffusion and ECM stiffness Discrete intracellular signaling dynamics (NF‑κB, Akt) Hill‑function nonlinearities for mechanotransduction, vascular suppression, metabolic oxygen consumption, and hypoxic response Event‑triggered hysteresis‑aware control logic enabling sequential therapeutic strategies This release includes a camera‑ready Python implementation, comparative simulation outputs, and figure assets suitable for academic publication, preprint deposition, and reproducible research workflows.Developed using the OAOL (O'Valley AI Orchestration Lab) framework, an AI-orchestrated research workflow for computational science and systems architecture.

Zenodo (CERN European Organization for Nuclear Research)
Mathematical Biology Tumor Growth
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