A Mathematical Model of Cysteine-Driven Metabolic Adaptation to Hypoxia in Ovarian Cancer

Ovarian cancer progression is strongly influenced by tumour hypoxia and associated oxidative stress. Experimental evidence indicates that cysteine availability supports ovarian cancer cell fitness under hypoxic conditions, yet the quantitative integration of cysteine metabolism, redox control, and energetic maintenance remains incompletely understood. We present a reduced mechanistic mathematical model describing intracellular cysteine allocation between glutathione (GSH) synthesis and hydrogen sulfide production under experimentally imposed hypoxia. The model integrates extracellular cysteine uptake, GSH-dependent reactive oxygen species (ROS) detoxification, hypoxia-amplified ROS generation, and redox-modulated ATP maintenance. Parameter estimation was performed using experimentally derived extracellular metabolite fluxes measured over a 24 h interval. Uncertainty was assessed via bootstrap resampling, and variance-based sensitivity analysis was conducted within (patho)physiologically constrained parameter domains. The calibrated model reproduces extracellular fluxes with relative deviations below 7% and identifies GSH synthesis capacity as the dominant determinant of ATP maintenance within experimentally supported ranges. Hydrogen sulfide (H₂S) production exerts a secondary stabilising influence, whereas hypoxia-driven ROS amplification negatively impacts energetic state. Numerical continuation across hypoxia levels reveals distinct qualitative response regions but does not imply a formal bifurcation structure. Importantly, intracellular metabolite dynamics are inferred as latent variables consistent with extracellular constraints and established biochemical knowledge; the model does not uniquely identify intracellular pool sizes or enzyme kinetics. The framework therefore provides flux-consistent mechanistic plausibility rather than direct intracellular validation. This systems-level analysis supports cysteine allocation as a quantitatively influential control point in hypoxic adaptation and establishes a constrained modelling framework for subsequent metabolic network expansions and experimental validation.