A Hybrid Framework to Link scRNA-seq-Inferred Tumor Heterogeneity with Glycolytic Dynamics and Active Matter Behavior

Tumors are complex ecosystems composed of transcriptionally diverse cells undergoing metabolic reprogramming and collective physical behaviors. While single-cell RNA sequencing (scRNA-seq) has revealed key functional states such as proliferation, EMT, and immune evasion, these analyses often lack integration with metabolic and biophysical insights. In this study, we present a unified framework that combines scRNA-seq with glycolysis simulations via SymChemAI, introducing indices such as Glycolytic Flux Index (GFI), Lactate Production Rate (LPR), Glycolytic Dropout 50 (GD50), and ATPgly to quantify metabolic states. Our results reveal that high-GFI, EMT-like subpopulations adopt Warburg-like glycolytic profiles and correspond to active fluid phases in an active matter framework, while adhesive, low-GFI clusters resemble jammed or glassy states. This work reframes the tumor as a non-equilibrium active matter system fueled by metabolic heterogeneity and provides a novel systems-level approach to understanding cancer progression.