Optimization examples - Revision history

Elena.kutumova@biouml.org at 05:07, 16 March 2022

2022-03-16T05:07:08Z

2022-03-16T05:05:46Z

2019-03-15T08:31:53Z

‎Testing computational speed of the optimization methods

2019-03-15T08:14:14Z

‎Testing computational speed of the optimization methods

2019-03-15T07:16:49Z

‎Testing computation speed of the methods for models with the different number of parameters

2019-03-15T07:06:18Z

‎Testing computation speed of the methods for models with the different number of parameters

2019-03-15T07:04:22Z

‎Testing the methods convergence rate for models with the different number of parameters

2019-03-15T07:00:30Z

‎Testing the methods convergence rate for models with the different number of parameters

2019-03-15T06:59:48Z

‎Testing the methods convergence rate for models with the different number of parameters

2019-03-15T06:37:45Z

‎Testing the methods convergence rate for models with the different number of parameters

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 The other methods showed about the same speed for running in one core, wherease, for running in several cores, the computational speed of PSO, SRES and MOCell was evidently higher compared to the simulated annealing.
 <gallery>
-File:optimization_examples_model_1.png|Test case 1A: 7 species and 5 reaction rate parameters.
+File:optimization_examples_model_1.png|'''Test case 1A''': 7 species and 5 reaction rate parameters.
-File:optimization_examples_model_2.png|Test case 1B: 13 species and 10 reaction rate parameters.
+File:optimization_examples_model_2.png|'''Test case 1B''': 13 species and 10 reaction rate parameters.
-File:optimization_examples_model_3.png|Test case 1C: 18 species and 12 reaction rate parameters.
+File:optimization_examples_model_3.png|'''Test case 1C''': 18 species and 12 reaction rate parameters.
-File:optimization_examples_model_4.png|Test case 2: 8 species and 22 reaction rate parameters.
+File:optimization_examples_model_4.png|'''Test case 2''': 8 species and 22 reaction rate parameters.
-File:optimization_examples_model_5.png|Test case 3: 32 species and 57 reaction rate parameters.
+File:optimization_examples_model_5.png|'''Test case 3''': 32 species and 57 reaction rate parameters.
 File:optimization_examples_figure_3.png|The number of objective function evaluations per second for the different test cases in BioUML.
 </gallery>
 ==References==

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 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]]. All information about the optimization methods implemented in BioUML is done in the chapter [[Optimization problem]].
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 # Kholodenko B.N. Negative feedback and ultrasensitivity can bring about oscillations in the mitogenactivated protein kinase cascades. ''European Journal of Biochemistry''. 2000. 267(6):1583–1588.
 # Bagci E.Z., Vodovotz Y., Billiar T.R., Ermentrout G.B., Bahar I. Bistability in apoptosis: roles of bax, bcl-2, and mitochondrial permeability transition pores. ''Biophysical Journal''. 2006. 90(5):1546–1559.

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 </table>
-==Testing computational speed of the optimization methods==
+==Testing the computational speed of the optimization methods==
 <ul>

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 Secondly, we took the test case proposed by Mendes et al. [3] for the MAP kinase cascade model developed by Kholodenko et al. [4] (the test case 2).
 Finally, we tested the model by Bagci et al. [5] representing the mitochondria-depended apoptosis (the test case 3).
 <gallery>

@@ Line 239: / Line 239: @@
 </table>
-==Testing computation speed of the methods for models with the different number of parameters==
+==Testing computational speed of the optimization methods==
 <ul>
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-To test a computation speed of the optimization methods in BioUML for models with the different number of parameters and species, we considered the following test cases.
+We tested a computational speed of such optimization methods in BioUML as particle swarm optimization, adaptive simulated annealing, stochastic ranking evolution strategy (SRES), and cellular genetic algorithm.
 Firstly, we derived three models of CD95-induced activation of caspase-8 from the model by Neumann et al. [2] with varying degrees of detail (the test cases 1A, 1B and 1C).
 Secondly, we took the test case proposed by Mendes et al. [3] for the MAP kinase cascade model developed by Kholodenko et al. [4] (the test case 2).

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 <li>'''Experimental data''': ''data'' > ''Examples'' > ''Optimization'' > ''Data'' > ''Experiments'' <div></div> (''exp_data_1'' for the test cases 1A, 1B, 1C; ''exp_data_2'' for the test case 2; ''exp_data_3'' for the test case 3) </li>
 </ul>
 To test the convergence rate of the optimization methods in BioUML for models with the different number of parameters and species, we considered the following test cases.