Open Access Research article

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

Katerina Tashkova1*, Peter Korošec1, Jurij Šilc1, Ljupčo Todorovski2 and Sašo Džeroski3

Author Affiliations

1 Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia

2 Faculty of Administration, University of Ljubljana, Gosarjeva ulica 5, SI-1000 Ljubljana, Slovenia

3 Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia

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BMC Systems Biology 2011, 5:159  doi:10.1186/1752-0509-5-159

Published: 11 October 2011

Additional files

Additional file 1:

Supplemental information. This file contains Figures S1-S10 and Tables S1-S8 with results obtained from parameter estimation in the Rab-to-Rab7 conversion model. Experimental behavior vs. simulated behavior of the reconstructed output and reconstructed model dynamics with the best parameters estimated by DASA (Figure S1), PSO (Figure S2), DE (Figure S3), and A717 (Figure S4) using measured data. Relative errors of the best estimated parameters by DASA (Table S1), PSO (Table S2), DE (Table S3), and A717 (Table S4) using artificial data. Parameter values associated with the best solutions estimated using measured data (Table S5). Summary of results on the DE estimated parameters with the Monte Carlo-based approach using data with 20% noise in three observation scenarios: CO (Table S6), AO (Table S7), and TO (Table S8). Corresponding histograms of the DE estimated parameters with the Monte Carlo-based approach using data with 20% noise: CO (Figure S5), AO (Figure S6), and TO (Figure S7). Scatter plots of the Monte Carlo-based DE parameter estimates combined with contour plots of the objective function when considering data with 20% noise for the most correlated pairs of parameters in the CO (Figure S8), AO (Figure S9), and TO (Figure S10) observation scenarios.

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