Fig. 3
Parameter estimation using a constitutive gene expression circuit and in-silico data. a Diagram of a gene with constitutive expression. The mRNA is produced at constant rate θ 1 and is degraded at constant rate θ 2, the proteins are produced at constant rate θ 3 and are degraded with θ 4. b Comparison between in-silico data and model dynamics using the true parameters θ o=(5,0.03,0.1,0.03) and the estimated parameters \(\hat {\theta } = (5.086,0.03,0.098,0.03)\). A distribution with the in-silico data is given in grey. In red is given the model dynamics. c Comparison between a priori and a posteriori parameter distributions. The true parameters are represented by the intersection of the red lines. In the priori distribution it can be observed that 1000 parameters are randomly distributed in the parameter space. The posteriori distributions were calculated by running 1000 independent random searches with 1000 parameters each, and by plotting the best parameter value selected by the algorithm. In the posteriori distribution it is plotted only the parameters that passed the deterministic precondition, and it can be observed that those parameter estimates are in a region close to the true parameter values