Stochastic intracellular calcium dynamics show preserved structures identified by deep learning classification.

Intracellular calcium ions (Ca2+) exhibit diverse dynamical behaviors linked with cellular physiological states related to health and disease. While deterministic models predict how biochemical parameters create distinct dynamical regimes - steady states, oscillations, bursting, chaos, and multiple periodicity - real biological systems are inherently stochastic due to finite molecular populations. Previous studies using conventional statistical measures demonstrated that increasing intrinsic fluctuations render these dynamical states increasingly indistinguishable, particularly for chaotic and multiple-periodicity patterns. This raises whether parameter-dependent organizational principles persist under realistic noise levels to remain biologically meaningful and computationally detectable. We address this using a large-kernel convolutional neural network (LKCNN) designed to capture global dynamical features across noise levels. Using chemical Langevin equations to generate synthetic training data with realistic intrinsic fluctuations, the LKCNN achieves ~90% accuracy in classifying eight distinct dynamical states despite noise levels that visually obscure distinctions. Validation with experimental Ca2+ data from pancreatic [Formula: see text]-cells as well as other cells, including WT-HEK293, STIM-KO, and ORAI TKO, achieves 96.8% accuracy, confirming generalizability beyond synthetic datasets, substantially outperforming conventional baselines (Support Vector Machine and Random Forest), which achieve only 54.0% and 51.6% accuracy respectively on the same experimental data. These results demonstrate that deterministic organizational signatures persist through realistic biological noise, suggesting parameter-dependent dynamical structures represent robust principles governing cellular function. Our findings establish that sophisticated pattern recognition can bridge theoretical deterministic dynamics and noisy biological reality, offering a framework for extracting meaningful dynamical information from inherently stochastic oscillatory biological processes.