Difference between revisions of "Sensitivity analysis example"

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(Reproducing the method in BioUML)
(Reproducing the method in BioUML)
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''data/Collaboration/Demo/Data/Sensitivity Analysis Results''
 
''data/Collaboration/Demo/Data/Sensitivity Analysis Results''
  
Than your click to '''run''' button and calculations are completed, you can see the results of the analysis represented by two tables including scaled and unscaled sensitivities.
+
Than your click to the ''run'' button and calculations are completed, you can see the results of the analysis represented by two tables including scaled and unscaled sensitivities.
  
 
==References==
 
==References==
 
# Geva-Zatorsky N., Rosenfeld N., Itzkovitz S., Milo R., Sigal A., Dekel E., Yarnitzky T., Liron Y., Polak P., Lahav G., Alon U. Oscillations and variability in the p53 system. ''Molecular Systems Biology''. 2006. V. 2, № 2006.0033.
 
# Geva-Zatorsky N., Rosenfeld N., Itzkovitz S., Milo R., Sigal A., Dekel E., Yarnitzky T., Liron Y., Polak P., Lahav G., Alon U. Oscillations and variability in the p53 system. ''Molecular Systems Biology''. 2006. V. 2, № 2006.0033.

Revision as of 21:20, 13 March 2019

The method description could be found in the section Sensitivity Analysis. Here we give an example of the method application and using in BioUML.

Consider the model of p53 and Mdm2 proteins regulation described by Geva-Zatorsky et al. [1]. The model includes the negative feedback loop in which p53 transcriptionally activates an Mdm2 precursor (pMdm2) representing, for example Mdm2 mRNA, and produces subsequent synthesis of Mdm2. Active Mdm2 increases the degradation rate of p53. The list of the model reactions is done in the table below, where x, y and y0 denote concentrations of p53, Mdm2 and pMdm2 respectively.

The model of p53 and Mdm2 interactions
    
ID Reactions Reaction rates
r1 → p53 βx
r2 -p53 → pMdm2 x
r3 pMdm2 → Mdm2 α0 · y0
r4 p53 -Mdm2 → αxy · x · y
r5 Mdm2 → αy · y

Assume βx = 0.3, βy = 0.4, α0 = αy = 0.1 and αxy = 3.2, and solve the algebraic system:

Sa formula 1.png

As a result we obtain the following steady state of the model:

Sa formula 2.png

Exploring the unscaled sensitivity of this state to perturbations of parameters βx, βy, α0, αxy and αy, we can find the matrix of partial derivatives:

Sa formula 3.png

Substituting the values of the investigated parameters, we get:

Sa formula 4.png

Scaled sensitivities could be found by the following way:

Sa formula 5.png

Reproducing the method in BioUML

To reproduce this example in BioUML workbench you first need to go to the Analysis tab in navigation pane and then follow to analyses > Methods > DAE models.

After double click on Sensitivity Analysis, a new tab with analysis settings opens.

You can select a path to the input diagram used in the example above:

data/Examples/DAE models/Data/Diagrams/Geva_Zatorsky_2006_Model_I

and select a path to save results of analysis:

data/Collaboration/Demo/Data/Sensitivity Analysis Results

Than your click to the run button and calculations are completed, you can see the results of the analysis represented by two tables including scaled and unscaled sensitivities.

References

  1. Geva-Zatorsky N., Rosenfeld N., Itzkovitz S., Milo R., Sigal A., Dekel E., Yarnitzky T., Liron Y., Polak P., Lahav G., Alon U. Oscillations and variability in the p53 system. Molecular Systems Biology. 2006. V. 2, № 2006.0033.
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