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

doi:10.1371/journal.pcbi.1013462

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

Fig 6

Reconstructed trajectories of the RPA-DNA binding model.

A. Sample ground truth and reconstructed trajectories evaluated at , where we use the convention that . B. Sample ground truth and reconstructed parameters evaluated at . C. Temporally decoupled squared W₂ distances (see Eq (8)) between the ground truth and reconstructed trajectories evaluated at different values. In A and B, blue and red trajectories represent the filling fractions of DNA by 20nt-mode and 30nt-mode RPA, respectively. The dashed lines represent the predicted trajectories, and the solid lines represent the ground truth. Throughout the figure, the data are generated by a single neural SDE model that accepts the conversion rate k₂ as a parameter and outputs the trajectories.

doi: https://doi.org/10.1371/journal.pcbi.1013462.g006