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. 2014 Nov 17;42(22):14060–14069. doi: 10.1093/nar/gku964

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© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Figure 5. — Transfer function (A) and basal expression (B) obtained from fitting our mathematical model including leakiness with real data. It explains the effect of receptor in amplitude signal and ligand affinity. Arrows show the times of variation from the ratio of G_m/G₀ from fitted model equation. Model was manually fitted to data using the following parameter values: γk_m0[P_T] = 85 M, K₂r′ = 5, K₃r′ = 500, ε_R = 10, ε_L = 10 and M. Pearson test was applied to the different constructs giving the following correlation coefficients: 0.996, 0.987 and 0.987 for weak, medium and strong, respectively. Comparison of model and real data leakiness in the panel (B) gave a correlation coefficient of 0.984.

Inline graphic — Transfer function (A) and basal expression (B) obtained from fitting our mathematical model including leakiness with real data. It explains the effect of receptor in amplitude signal and ligand affinity. Arrows show the times of variation from the ratio of G_m/G₀ from fitted model equation. Model was manually fitted to data using the following parameter values: γk_m0[P_T] = 85 M, K₂r′ = 5, K₃r′ = 500, ε_R = 10, ε_L = 10 and M. Pearson test was applied to the different constructs giving the following correlation coefficients: 0.996, 0.987 and 0.987 for weak, medium and strong, respectively. Comparison of model and real data leakiness in the panel (B) gave a correlation coefficient of 0.984.