Difference between revisions of "Metabolic control analysis example"

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The method description could be found in the section [[Metabolic Control Analysis]]. Here we give an example of the method application and using in BioUML.
 
The method description could be found in the section [[Metabolic Control Analysis]]. Here we give an example of the method application and using in BioUML.
  
==Reproducing a test case in BioUML==
+
==Reproducing a test case==
  
 
To reproduce a test case below in the [[BioUML]] workbench, go to the <b>Analysis</b> tab in the navigation pane and follow to ''analyses'' > ''Methods'' > ''Differential algebraic equations''.
 
To reproduce a test case below in the [[BioUML]] workbench, go to the <b>Analysis</b> tab in the navigation pane and follow to ''analyses'' > ''Methods'' > ''Differential algebraic equations''.

Revision as of 17:05, 11 March 2022

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

Reproducing a test case

To reproduce a test case below in the BioUML workbench, go to the Analysis tab in the navigation pane and follow to analyses > Methods > Differential algebraic equations.

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

Select a path to the input diagram:

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

and a path to save results of analysis, for example:

data/Collaboration/Demo/Data/Temp/Metabolic Control Analysis

After you click the Run button and the calculations are finished, you can see the results presented in six tables, including unscaled elastisities (the matrix ƊCssv in the description below), unscaled concentration controls (the matrix Γ), unscaled flux controls (the matrix Λ), and scaled versions of these matrices with elements ei,j defined as a product of the corresponding elements in the unscaled matrixes and values of ci,j / ri,j, where ci,j and ri,j denote steady state values of column and row elements, respectively.

Ready analysis results can be found here:

data/Examples/DAE models/Data/Metabolic Control Analysis

Description of the test case

The theory of metabolic control determines sensitivity of steady metabolic fluxes (reaction rates) vss and steady state Css of an ODE model under perturbation of its parameters K [1]. Unlike the Sensitivity Analysis, this method does not require the computation of values Css(p + Δp), pK, that can accelerate the model research. Instead, to analyze matrixes ƊKCss and ƊKvss of partial derivatives of Css and vss with respect to parameters K, the method considers a matrix ƊKv|C(t) = Css of partial derivatives of a vector v(t) with respect to K in a steady state of the model, and searches for control matrices Γ and Λ, such that

Mca formula 1.png

In accordance with [1], these matrices can be calculated by formulas

Mca formula 2.png

where NR is a matrix consisting of linearly independent rows of the model stoichiometric matrix N of n by m, L is a transition matrix such that: N = L · NR, and ƊCssv is a matrix of elasticity coefficients

Mca formula 3.png.

Consider the model of p53 and Mdm2 proteins regulation described in the example for Sensitivity Analysis. For this model, we get:

Mca formula 4.png

Next, we find the matrixes N and ƊCssv:

Mca formula 5.png

Since NR = N and L is the identity matrix of 3 by 3, we can calculate the matrix product N · ƊCssv:

Mca formula 6.png

Applying the cofactor method to the resulting matrix, we deduce:

Mca formula 7.png

Thus, we have:

Mca formula 8.png

Take into account the formulas for calculation of the model steady state derived in the Sensitivity Analysis example:

Sa formula 2.png

Using these formulas, we can convert matrix Γ to the form:

Mca formula 9.png

Multiplication of the calculated matrices Γ and ƊKv|C(t) = Css gives the same matrix ƊKCss, as in the example for Sensitivity Analysis:

Mca formula 10.png

References

  1. Reder C. Metabolic control theory: a structural approach. Journal of Theoretical Biology. 1988. V. 135. № 2. p. 175-201.
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