Cell Behavior Science (CBS): A Physically Grounded Framework Integrating Non-Equilibrium Thermodynamics, Active Matter Theory, and Mechanobiology

Cell Behavior Science (CBS): A Physically Grounded Framework Integrating Non-Equilibrium Thermodynamics, Active Matter Theory, and Mechanobiology We present Cell Behavior Science (CBS), a predictive, physics-first framework that unifies non-equilibrium thermodynamics, active matter theory, and cytoskeletal mechanobiology to model cellular dynamics across ontogenesis, homeostasis, and disease progression. CBS formalizes the living cell as an open, dissipative system driven by ATP-dependent biochemical processes and governed by entropy production principles derived from stochastic thermodynamics. We introduce two experimentally testable hypotheses grounded in contemporary biophysics: (1) the Coherent Vibrational Network (CVN), where ultrafast (10 fs – 1 ps) anharmonic vibrations in hydrophobic protein cores enable efficient intramolecular energy redistribution, consistent with two-dimensional infrared (2D-IR) spectroscopy; (2) Mechano-Geometric Memory (MGM), wherein prestressed cytoskeletal architectures encode mechanical history through low-frequency conformational eigenmodes detectable via traction force microscopy and epigenomic assays (e.g., ATAC-seq). These constructs are integrated within the Entropic Flux Model (EFM), a continuum field theory that quantifies local entropy reduction sustained by chemical free energy from ATP hydrolysis. The EFM satisfies the second law of thermodynamics globally while permitting transient local order through energy dissipation. We operationalize CBS using a hybrid computational architecture combining agent-based modeling (ABM) with message-passing graph neural networks (GNNs) trained on synthetic and experimental datasets. Validation across three biological contexts—tumor cell invasion in 3D matrices, organoid transplantation compatibility, and evolutionary trajectories of antibiotic resistance—demonstrates predictive accuracies of 85–91%, outperforming established baselines (PhysiCell, CellOracle, DeepCell) by 12–28% in standardized benchmarks (RMSE, AUC, classification accuracy). All simulation code, trained models, parameter sweeps, and data generation pipelines are open-sourced under an MIT license to ensure full reproducibility, auditability, and community extension. CBS provides a rigorous, falsifiable, and scalable foundation for next-generation predictive cell biology.