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We used a two species approach for mRNA and Protein concentrations. x₁ and x₂ represent the mRNA and protein levels for the induced promoter and x₃ and x₄ the respective levels for the uninduced promoter.

As no analysis was done with several galactose concentrations hence the hill function that normally models the response to the concentration was replaced by a single factor to improve identifiability.

The best fit is in a-posterior sense where the logarithm of the a-posteriori was optimised using the matlab script lsqnonlin. The script uses a Levenberg-Marquardt algorithm to find a minimum.

All fitted parameters are within realistic bounds.

The half life time of eGFP is a bit lower than expected. The literature states GFP should have a half-life time (inverse of the degradation rate) of over 20 hours. But there are several possible explanations for this:

• Certain GFP mutants are destabilizedm and thus have a significantly lower half-life time.
• The cells were not stored in the dark, hence photobleaching might lower the effectively measured half-life time as we only measure the non-bleached proteins.
• The simulations show that the parameter set k₂, γ₂ and μ is not precisely identifiable. This means that the best fit values are not necessarily significant.

Figure 1. Experimental data versus simulation

The methods utilized rely on a value for standard deviation, but only one experimental measurement was performed. Hence a value was infered during the optimization process. Thus the error bars in the plot do not reflect values from actual measurements but merely show how well the data can be approximated by the model.

It is clearly visible that the simulated time course stays within the standard deviation and we can thus conclude, that the model is definitely able to reproduce the data.

Figure 2. Profile Likelihood without priors

For the profile likelihood analysis, the scripts provided by the Systems Biology group from TU Eindhovenhttp://bmi.bmt.tue.nl/sysbio/software/pua.html Vanlier and van Riel, 2012 were used and modified to our needs.

All profiles in figure 2 have a convex shape, which means that we have no structural non-identifiabilitieshttp://www.ncbi.nlm.nih.gov/pubmed/22355081 Vanlier et al., 2012 and all parameters are identifiable. Still all profiles except the one for γ₁ have a rather large, flat section in the middle. This means, as long as the prior does not significantly change anything, we will not be able to make very accurate predictions on the parameters.

Including priors in further investigating will increase the accuracy of our measurements. Unfortunately, as computing profile likelihoods can become quite time consuming, we were not able to compute a profile likelihood with priors included.

Joint Distribution

Figure 4. Smoothed 2D histogram of the joint distribution of in each case 2 parameters.

Figure 5. Scatter plot of the samples of joint distribution of in each case 2 parameters.

For the smoothed histogram plot smoothhist2d.mhttp://www.mathworks.de/matlabcentral/fileexchange/13352-smoothhist2d Perkins, 2009 was used. The color indicates the density of samples. Black to dark red reflects a low density, yellow to white indicates a high density. Grey dots indicate samples identified as outliers by the script.

On the diagonal of the scatter plot the kernel density estimation script kde.mhttp://www.mathworks.com/matlabcentral/fileexchange/14034-kernel-density-estimator Botev, 2011 was used to obtain a plot of the respective marginal densities. Off the diagonal is a again a plot of the joint distribution. Here the color indicates the a-posterior probability of the respective sample. Low probabilities are mapped by blue to green color, High probabilities are mapped by yellow to red color.

Mind that due to the nature of the the utilized scripts, the orientation of the Y-Axis is flipped when comparing the two plots.

Credibility Intervals

3D plot of resulting timecourse

Figure 6. Simulated time course of resulting probabilities of the fluorescence according to the generated samples

Using the generated samples we can simulate the time course for all of them and use the 2d kernel density estimation script kde2d.mhttp://www.mathworks.com/matlabcentral/fileexchange/17204-kernel-density-estimation Botev, 2009 to plot a wonderful distributed time-course of the simulated fluorescence.

Conclusion

Figure 7. Joint 2D density of α and k₁. Obtained with kde2d.m.

Figures 3-5 all show a strong positive correlation between k₁ and α. When looking at figure 7 we see that the joint density of the two parameters has a clear peak. Thus the two parameters should be identifiable.

Figure 8. Smoothed 2D histogram of the joint distribution of in each case 2 parameters.

For parameters μ₁ and k₂ figure 4 and 5 clearly show a strong negative correlation of the two parameters. The joint distribution in figure 8 suggests no single localized peak, hence those two parameters are most likely non-identifiable.

As the profile likelihood tells us that we should not expect any structural non-identifiabilities, it must be possible to remove this non-identifiability by inclusion of further priors.

This non-identifiability is also reflected in the huge size of the confidence intervals.

Sensitivity Analysis

Differential Equation

Result

Figure 9. Logarithmic plot of the absolute value of the absolute sensitivity for the fluorescence with an induced promoter

Figure 10. Logarithmic plot of the absolute value of the relative sensitivity for the fluorescence with an induced promoter

Figure 11. Logarithmic plot of the absolute value of the absolute sensitivity for the fluorescence with an uninduced promoter

Figure 12. Logarithmic plot of the absolute value of the relative sensitivity for the fluorescence with an uninduced promoter

A high sensitivity means that small changes in the parameter will cause large deviations in the resulting simulations. In figure 9 and 10 the magnitude of the curves for the sensitivity of the parameters inversely correlates with the size of the confidence intervals of the respective parameters. This perfectly matches our expectations.

Code

Reference

• http://www.mathworks.com/matlabcentral/fileexchange/17204-kernel-density-estimation Botev, 2009 Botev, Z. (2009). http://www.mathworks.com/matlabcentral/fileexchange/17204-kernel-density-estimation.
• http://www.mathworks.com/matlabcentral/fileexchange/14034-kernel-density-estimator Botev, 2011 Botev, Z. (2011). http://www.mathworks.com/matlabcentral/fileexchange/14034-kernel-density-estimator.
• http://www.mathworks.de/matlabcentral/fileexchange/13352-smoothhist2d Perkins, 2009 Perkins, P. (2009). http://www.mathworks.de/matlabcentral/fileexchange/13352-smoothhist2d.
• http://www.ncbi.nlm.nih.gov/pubmed/22355081 Vanlier et al., 2012 Vanlier, J., Tiemann, C. A., Hilbers, P. A. J., and van Riel, N. A. W. (2012). An integrated strategy for prediction uncertainty analysis. Bioinformatics, 28(8):1130–5.
• http://bmi.bmt.tue.nl/sysbio/software/pua.html Vanlier and van Riel, 2012 Vanlier, J. and van Riel, N. (2012). http://bmi.bmt.tue.nl/sysbio/software/pua.html.