Computational Predictions for Conductive-Substrate Perturbation of the Massed–Spaced Cellular Learning Effect

Independent computational analysis of the massed–spaced cellular learning effect reported in CRE-luciferase reporter cells. This study compares a standard biochemical ERK→CREB→luciferase signaling model against a weakly coupled ΔΦ-modulated perturbation framework in which transient electrostatic dynamics modestly influence inter-pulse transcriptional gain. Ordinary differential equation (ODE) simulations were performed at 30-second temporal resolution across a 25-hour window using the exact stimulation paradigms reported in the original experimental study, including four 3-minute pulses delivered at inter-trial intervals (ITIs) of 10, 20, and 30 minutes versus equivalent massed stimulation. The biochemical model reproduces the baseline spacing effect (Spaced > Massed). The ΔΦ-modulated model predicts modest resonance-like enhancement on insulating substrates and partial attenuation of this enhancement on grounded conductive substrates such as indium tin oxide (ITO)-coated glass. The grounded condition shifts the response back toward the biochemical baseline rather than eliminating the spacing effect entirely. This work does not claim proof of electrostatic cellular memory. Instead, it provides a falsifiable computational perturbation framework generating experimentally testable predictions for continuous live-cell luciferase imaging under substrate-controlled conditions. Included in this deposit: full computational manuscript, simulation figures, model equations, parameter descriptions, and experimental predictions for conductive-substrate perturbation testing. Related experimental context: Kukushkin et al. (Nature Communications 2024) DOI: 10.1038/s41467-024-53922-x