Artificial Intelligence-Based Modeling of the Dynamics and Regulation of Intracellular Signaling Pathways
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Intracellular signaling pathways regulate essential cellular processes including proliferation, differentiation, and apoptosis. Their dysregulation contributes to cancer, diabetes, and neurodegenerative diseases. However, the complexity of these networks limits traditional modeling approaches. We developed a hybrid computational framework integrating Physics-Informed Neural Networks (PINN), Temporal Convolutional Networks (TCN), and Graph Neural Networks (GNN) to model signaling dynamics. The model was trained on 23,456 multi-omics datasets covering MAPK/ERK, PI3K
