Computational Validation of the Taylor Cellular Recoverability Scale: 600-Trial Digital Twin Simulation and PINN Trajectory Reconstruction

This article presents a computational evaluation of the Taylor Cellular Recoverability Scale (CRS), a proposed multidimensional framework for characterizing cellular condition, pathological progression, and the capacity for recovery through dynamic system behavior. Using synthetic digital twin simulations, the study examines the framework’s four principal state variables: proximity to the Cellular Point of No Return (CPNR), drift velocity and trajectory, metabolic and energetic reserve, and homeostatic elasticity. Across a 600-system baseline cohort, the four-input model achieved 72.2% held-out classification accuracy, compared with 67.8% for a scalar-score model. A separate paired counterfactual experiment involving 600 matched systems demonstrated that the modeled closed-loop stabilizer increased simulated recovery from 45.2% to 68.8%, while reducing deterioration from 45.8% to 30.0%. The study also explores physics-informed neural network (PINN) trajectory reconstruction as a computational approach for estimating continuous cellular-state dynamics from sparse observations. A supplementary experiment examines a candidate recoverability function under two synthetic initial-condition scenarios. Collectively, these investigations provide an initial computational examination of cellular recoverability as a multidimensional, dynamic state-space problem. All reported results are based on synthetic data and specified modeling assumptions; experimental biological calibration, independent computational benchmarking, and further mathematical development remain necessary to establish biological validity.