Back in 2008 we performed a comparison of the solvers in the SSm GO toolbox with other optimization methods considering 3 problems of parameter estimation in nonlinear dynamic biological systems.

SSm GO outperformed all the other optimization methods available in the Systems Biology Toolbox 2, including simulated annealing, SRES, Differential Evolution, several implementations of particle swarm optimization, CMA-ES, etc.

This comparison was done in collaboration with Henning Schmidt (the author of the Systems Biology Toolbox 2) and was presented at ICSB 2008:

EGEA JA, SCHMIDT H, BANGA JR. (2008) A new tool for parameter estimation in nonlinear dynamic biological systems using global optimization. 9TH INTERNATIONAL CONFERENCE ON SYSTEMS BIOLOGY, ICSB 2008, Göteborg (Sweden), 22-28 August 2008.

Click here to download:

Poster_ICSB2008_2.pdf (320 KB)

(download)

Click here to download:

Poster_ICSB2008_2.pdf (320 KB)

Please see the attached poster above for more details.

If you want to reproduce these results, or adapt our scripts to perform your own evaluations, you will need to:

- have Matlab 7.5 or later (note: 32 bits version) under a Windows operating system
- download and install SSmGO toolbox from http://www.iim.csic.es/~gingproc/ssmGO.html
- download and install the Systems Biology Toolbox 2 from www.sbtoolbox2.org, making sure it works

- and then email us ( gingproc@iim.csic.es ) requesting the comparison scripts

SSm GO is a Matlab toolbox with several global optimization algorithms for nonlinear optimization problems (NLPs and MINLPs) based on Scatter Search.

The solvers in this toolbox attempt to solve problems of the form:

      min F(x)  subject to:  ceq(x) = 0 (equality constraints)
       x                     c_L <= c(x) <= c_U (inequality constraints)
                             x_L <= x <= x_U (bounds on the decision variables)

This package requieres Windows and Matlab and is available at no cost to ACADEMIC USERS.

To download it, please go to http://www.iim.csic.es/~gingproc/ssmGO.html

The method is described in these publications (please cite them if you publish results obtained with this toolbox):

Egea, J.A., Martí, R., & Banga, J.R. (2010). An evolutionary method for complex-process optimization. Computers and Operations Research 37(2):315-324.

Egea, J.A., M. Rodriguez-Fernandez, J. R. Banga and R. Martí (2007) Scatter Search for chemical and bioprocess optimization. Journal of Global Optimization 37(3):481-503.

Rodriguez-Fernandez, M., J. A. Egea and J. R. Banga (2006) Novel Metaheuristic for Parameter Estimation in Nonlinear Dynamic Biological Systems. BMC Bioinformatics 7:483. (PDF) (abstract)

License:

 
SSm GO toolbox by IIM-CSIC is licensed under a Creative Commons Attribution-Non-Commercial-No Derivative Works 2.5 Spain License.