| Abstract: |
| Critical transitions in intracellular regulatory networks, driven by fluctuations in protein concentrations, are fundamental to cellular decision-making and disease-related state transition. We investigate the stochastic dynamics of the Cdc2-Cyclin B/Wee1 regulatory module under Gaussian noise, focusing on how variations in feedback strength modulate system stability and transition behavior. By analyzing time-series data of protein concentrations, we evaluate a set of statistical, informational, and dynamical indicators to identify early signatures of critical transitions. Feature selection techniques are employed to determine the most informative indicators, which are subsequently incorporated into a deep learning framework based on a multilayer perceptron (MLP) regression model to predict the future evolution of Cdc2-Cyclin B concentrations and provide early warning of impending transitions. |
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