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Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations
Fig 9
Inferring intrinsic noise intensities and reconstructing experimental data via END-nSDE.
A. Plots showing the mean (solid circles) and variance (error bars) of the relative error in the reconstructed noise intensities 
predicted by the parameter-inference NN for the testing dataset, as a function of the group size of input trajectories. B. Heatmaps showing the relative error in the reconstructed noise intensities for the training dataset. Colored cells represent results from the parameter-inference NN for the training dataset, while blank cells indicate noise strength values not included in the training set. C. Heatmaps showing the relative error in the diffusion function for the testing dataset. D. The inferred intensity of I
Bα transcription noise (
) and NF
B translocation noise (
) in different groups of experimental trajectories, plotted against the group’s ranking in decreasing similarity with the representative ODE trajectory. E-H. Groups of experimental and nSDE-reconstructed trajectories ranked by decreasing cosine similarity: #1 (E), #4 (F), #16 (G), #29 (H). The squared W2-distance between experimental and SDE-generated trajectories are 0.157 (E), 0.143 (F), 0.212 (G), 0.236 (H). The inferred noises are (10−0.49,10−0.81) (E), (10−0.47,10−0.78) (F), (10−0.46,10−0.74) (G), (10−0.44,10−0.71) (H). I. The temporally decoupled squared W2 distance between reconstructed trajectories generated by the trained END-nSDE and groups of experimental trajectories, ordered according to decreasing similarity with the representative ODE trajectory.
doi: https://doi.org/10.1371/journal.pcbi.1013462.g009