< Back to Article
What can we learn when fitting a simple telegraph model to a complex gene expression model?
Fig 5
Variation patterns of effective parameters under different induction conditions in all complex models.
A: Tuning a single parameter of a complex model can generate a series of steady-state gene product distributions, along with different mean expression levels. Fitting these distributions to the telegraph model leads to a series of effective parameters 
, 
, and 
. Plotting 
, 
, and 
as functions of the corresponding mean expression level reveals how the effective parameters vary when a single parameter of a complex model is tuned. B: Effective parameters changed when modulating a single parameter of a complex model. For example, for the positive feedback model, the effective parameter 
changes when tuning the parameter λ, while all effective parameters 
, 
, and 
change when tuning the parameter ρ.
doi: https://doi.org/10.1371/journal.pcbi.1012118.g005