Difference between revisions of "Optimization examples"
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Revision as of 12:10, 15 March 2019
Here we give some examples of the BioUML usage for solving the problem of parameter estimation applied to the models of biochemical pathways. For details about creation your oun optimization document in BioUML, see the chapter Optimization document. Some information about the optimization methods implemented in BioUML is done in the chapter Optimization problem.
Testing the convergence rate of the optimization methods
- Optimization document: data > Examples > Optimization > Data > Documents > test_case_1A
- Model: data > Examples > Optimization > Data > Diagrams > diagram_1A
- Experimental data: data > Examples > Optimization > Data > Experiments > exp_data_1
To analyze a convergence rate of the optimization methods implemented in BioUML [1], we considered a reaction chain extracted from the model by Neumann et al. [2] and representing activation of caspase-8 triggered by the receptor CD95 (APO-1/Fas).
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We performed estimation of parameters using the search space defined as:
where upper bounds were chosen based on the order of magnitude of parameter values proposed in [2].
Estimation was based on the experimental data obtained by Neumann et al. [2] for procaspase-8 and its cleaved products p43/p41 and caspase-8.
| Time (min-1) | p43/p41 (nM) | pro-8 (nM) | casp-8 (nM) |
| 0.0 | 0.058 | 59.963 | 0.000 |
| 10.0 | 0.268 | 57.565 | 0.041 |
| 20.0 | 4.760 | 58.590 | 0.316 |
| 30.0 | 8.252 | 59.422 | 1.397 |
| 45.0 | 16.144 | 48.190 | 3.520 |
| 60.0 | 17.021 | 38.950 | 3.947 |
| 90.0 | 15.269 | 23.502 | 4.871 |
| 120.0 | 12.530 | 13.127 | 4.878 |
| 150.0 | 10.335 | 10.703 | 4.228 |
We reviewed solutions obtained by all optimization methods for 100 runs. Each run was based on the generation of 107 different guesses. The best result was obtained by the particle swarm optimization (PSO) and the cellular genetic algorithm (MOCell). Methods SRES, MOCell and PSO found similar solutions. Methods ASA and glbSolve found other values for parameters k1 and k2 showing lower efficiency.
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The best guesses obtained by optimization methods for 100 runs
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References
- Kutumova E., Ryabova A., Valeev T., Kolpakov F. BioUML plug-in for nonlinear parameter estimation using multiple experimental data. Virtual biology. 2013. 1:47-58.
- Neumann L., Pforr C., Beaudouin J., Pappa A., Fricker N., Krammer P.H., Lavrik I.N., Eils R. Dynamics within the CD95 death-inducing signaling complex decide life and death of cells. Molecular Systems Biology. 2010. 6:352.
