Difference between revisions of "Parameter identifiability example"
From BioUML platform
| Line 10: | Line 10: | ||
==Parameter identifiability (optimization)== | ==Parameter identifiability (optimization)== | ||
| + | |||
| + | Definition of the method parameters can be found [[Parameter identifiability (optimization) (analysis)|here]]. | ||
| + | |||
| + | For our example, we used a test case optimization created for the MAP kinase cascade model of Kholodenko [3]. A brief description of this test case is done in the chapter [[Optimization examples]]. In the current identifiability analysis, we used the following settings: | ||
| + | |||
| + | <ul> | ||
| + | <li>Path to the optimization document in the BioUML repository:<br> | ||
| + | '''Optimization''' = ''data/Examples/Optimization/Data/Documents/test_case_2'' | ||
| + | |||
| + | <li>Path to the optimization results:<br> | ||
| + | '''Parameter values''' = ''data/Examples/Optimization/Data/Simulations/optimization_results_2/optimizationInfo'' | ||
| + | |||
| + | <li>The maximum number of steps performed by the analysis for each test variable in one direction:<br> | ||
| + | '''Maximum identifiability steps''' = 50 | ||
| + | |||
| + | <li>As the maximal deviation from the initial objective function value, we considered ten percent of the smallest objective function value (found by the optimization and corresponding to the results in the ''optimizationInfo'' table):<br> | ||
| + | '''Maximal deviation''' = 4.2 | ||
| + | |||
| + | <li>A possible path to save results of the analysis:<br> | ||
| + | '''Output path''' = ''data/Collaboration/Demo/Data/Temp/Identifiability results (for test_case_2)'' | ||
| + | </ul> | ||
<br>[[File:parameter_identifiability_example_01.png]]<br><br> | <br>[[File:parameter_identifiability_example_01.png]]<br><br> | ||
| Line 18: | Line 39: | ||
==Parameter identifiability (table)== | ==Parameter identifiability (table)== | ||
| + | |||
| + | Definition of the method parameters can be found [[Parameter identifiability (table) (analysis)|here]]. | ||
<br>[[File:parameter_identifiability_example_02.png]]<br><br> | <br>[[File:parameter_identifiability_example_02.png]]<br><br> | ||
| Line 32: | Line 55: | ||
# Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingmüller U, Timmer J (2009) Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics, 25(15):1923–1929. | # Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingmüller U, Timmer J (2009) Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics, 25(15):1923–1929. | ||
# Raue A, Becker V, Klingmüller U, Timmer J (2010) Identifiability and observability analysis for experimental design in nonlinear dynamical models. Chaos, 20(4):045105. | # Raue A, Becker V, Klingmüller U, Timmer J (2010) Identifiability and observability analysis for experimental design in nonlinear dynamical models. Chaos, 20(4):045105. | ||
| + | # Kholodenko BN (2000) Negative feedback and ultrasensitivity can bring about oscillations in the mitogenactivated protein kinase cascades. European Journal of Biochemistry, 267(6):1583–1588. | ||
</font> | </font> | ||
Revision as of 17:47, 16 March 2022
Identifiability analysis infers how well the model parameters are approximated by the amount and quality of experimental data [1,2].
Contents |
Reproducing a test case in BioUML
To reproduce a test case below in the BioUML workbench, go to the Analyses tab in the navigation pane and follow to analyses > Methods > Differential algebraic equations.
Identifiability analysis can be run in two ways:
- to use a pre-created optimization document, double click on Parameter identifiability (optimization);
- for auto-generation of an optimization document using the given settings, double click on Parameter identifiability (table).
Parameter identifiability (optimization)
Definition of the method parameters can be found here.
For our example, we used a test case optimization created for the MAP kinase cascade model of Kholodenko [3]. A brief description of this test case is done in the chapter Optimization examples. In the current identifiability analysis, we used the following settings:
- Path to the optimization document in the BioUML repository:
Optimization = data/Examples/Optimization/Data/Documents/test_case_2 - Path to the optimization results:
Parameter values = data/Examples/Optimization/Data/Simulations/optimization_results_2/optimizationInfo - The maximum number of steps performed by the analysis for each test variable in one direction:
Maximum identifiability steps = 50 - As the maximal deviation from the initial objective function value, we considered ten percent of the smallest objective function value (found by the optimization and corresponding to the results in the optimizationInfo table):
Maximal deviation = 4.2 - A possible path to save results of the analysis:
Output path = data/Collaboration/Demo/Data/Temp/Identifiability results (for test_case_2)
Ready analysis results can be found in the folder:
data/Examples/DAE models/Data/Parameter Identifiability
Parameter identifiability (table)
Definition of the method parameters can be found here.
Interpretation of results
Other possible profiles
References
- Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingmüller U, Timmer J (2009) Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics, 25(15):1923–1929.
- Raue A, Becker V, Klingmüller U, Timmer J (2010) Identifiability and observability analysis for experimental design in nonlinear dynamical models. Chaos, 20(4):045105.
- Kholodenko BN (2000) Negative feedback and ultrasensitivity can bring about oscillations in the mitogenactivated protein kinase cascades. European Journal of Biochemistry, 267(6):1583–1588.
