From Data to Dynamics: Uncovering Cell Signaling Networks with Physics-Informed Machine Learning

NathanSmyers

University of North Carolina at Chapel Hill

"From Data to Dynamics: Uncovering Cell Signaling Networks with Physics-Informed Machine Learning"

Cell signaling is governed by complex networks of biochemical interactions. These networks are critical for a wide range of cellular functions, including detecting environmental changes and cellular motility. Modeling these processes with reaction-diffusion equations (RDEs) requires prior knowledge of protein-protein interactions for constructing the underlying network. The complex nature of signaling pathways means many relevant interactions may be unknown. To address this challenge, we developed a deep learning-based method to infer reaction networks from data. By integrating a physics-informed neural network (PINN) with a neural network for symbolic regression, this method learns interpretable RDE models from spatiotemporal data, effectively learning the biochemical reactions driving dynamics. To develop and validate our approach, we applied it to data generated from a model of cell polarity establishment. This approach has the potential to overcome limitations from incomplete knowledge of protein-protein interactions, serving as a powerful tool for uncovering how cells regulate complex behaviors.
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