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Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations

Fig 7

Reconstruction of NFB signaling dynamics.

A. Sample trajectories of nuclear NFB concentration as a function of time with , . B. Sample trajectories of nuclear NFB concentration as a function of time with , . C. Reconstructed nuclear NFB trajectories generated by the trained neural SDE versus the ground truth nuclear NFB trajectories under noise intensities , in Eq (10). D. Reconstructed nuclear NFB trajectories generated by the trained neural SDE versus the ground truth nuclear NFB trajectories under noise intensities , . E. The squared W₂ distance between the distributions of the predicted trajectories and ground truth trajectories on the training set under different noise strengths . For training, we randomly selected 50% sample trajectories in 80 combinations of noise strengths as the training dataset. Blank cells indicate that the corresponding parameter set is not included in the training set. F. Validation of the trained model by evaluating the squared W₂ distance between the distributions of predicted trajectories and ground truth trajectories on the validation set.

doi: https://doi.org/10.1371/journal.pcbi.1013462.g007