(a) Example of kinetics with reactions with rates . (b) Illustration of the DGA’s approximations: replacing the non-differentiable Heaviside and Kronecker delta functions with smooth sigmoid …
The process begins by initializing the parameters . Simulations are then run using the DGA to obtain statistics like moments. These statistics are used to compute the loss , and the gradient …
(a) Schematic of gene regulatory circuit for transcriptional repression. RNA polymerase (RNAP) binds to the promoter region to initiate transcription at a rate , leading to the synthesis of mRNA …
Comparison between the DGA and exact simulations for (a) steady-state mRNA distribution, (b) moments of the steady-state mRNA distribution, and (d) the probability for the promoter to be in the ‘ON’ …
Parameters are fixed at 1, with and for a simulation time of 10. (a) Scatter plot of true versus inferred parameters ( and ) with constant. Error bars are 95% confidence intervals …
Parameters are fixed at 1, with and for a simulation time of 10. (a) Scatter plot of true versus inferred parameters (, , and ). Error bars are 95% confidence intervals (CIs). Panel (b…
(a) Comparison between theoretical predictions from the DGA (solid curves) and experimental values of mean and the Fano factor for the steady-state mRNA levels are represented by square markers, …
(a) Schematic of four-state promoter model. (b) Target input–output relationships (solid curves) and learned input–output relationships (blue dots) between activator concentration and average …
In panel (a), ; in panel (b), . The simulation time is set to 10. In panels (c) and (d), for these same values, we show the gradient of the loss function with respect to the parameter …