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What can we learn when fitting a simple telegraph model to a complex gene expression model?
Fig 3
Linking effective parameters to their real values in complex models.
A: For each complex model, the absolute values of relative errors of the three effective parameters 
, 
, and 
are computed under 625 parameter sets, along with their sample means, sample variances, and the sample frequencies of relative errors being greater than 0.2. The effective parameter 
is closed to the synthesis rate ρ for the three-state, cross-talk pathway, and positive feedback models, while the effective parameter 
is closed to the gene activation rate λ for the negative feedback model. B: Accuracy of the three effective parameters 
, 
, and 
for each complex model. For the three-state model, all effective parameters are over-estimated; for the cross-talk pathway and positive feedback models, all effective parameters are under-estimated; for the negative feedback model, 
is over-estimated, while 
and 
are under-estimated. C: For each complex model, 150 parameter sets are randomly generated such that 1/〈Toff〉 and 1/〈Ton〉 are between 0 and 2.5d (grey diamonds). For the three-state model, the scatter plot of 
escapes from the potential bimodal region of 
; for the cross-talk pathway and positive feedback models, the scatter plot of 
moves towards the potential bimodal region; for the negative feedback model, the scatter plot of 
neither escapes from nor moves towards the potential bimodal region. The yellow (orange) bar shows the proportion of parameter sets that give rise to a unimodal (bimodal) distribution.
doi: https://doi.org/10.1371/journal.pcbi.1012118.g003