MEIGO is a global optimization toolbox that includes a number of metaheuristic methods as well as (currently in the R version only) a Bayesian inference method for parameter estimation. They can be used to solve the following problem classes:
Examples of these problem classes in bioinformatics and systems biology are:
In order to facilitate its use and interfacing with other software packages, we provide:
Furthermore, we also provide parallel implementations of these solvers based on a cooperative strategy :
 Egea, J.A., Martí, R. and Banga, J.R. (2010) An evolutionary method for complex-process optimization. Computers and Operations Research, 37(2), 315-324.
 Hansen, P.. Mladenović, N. and Moreno Pérez, J.A. (2010) Variable neighborhood search: methods and applications. Annals of Operations Research, 175, 367–407.
 Villaverde, A.F., Egea, J.A. and Banga, J.R. (2012) A cooperative strategy for param-eter estimation in large scale systems biology models. BMC Systems Biology, 6:75.
 Egea JA, Henriques D, Cokelaer T, Villaverde AF, MacNamara A, Danciu DP, Banga JR and Saez-Rodriguez J. (2014) MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics. BMC Bioinformatics 15:136.
 Eydgahi, H., Chen, W. W., Muhlich, J. L., Vitkup, D., Tsitsiklis, J. N. and Sorger, P. K. (2013). Properties of cell death models calibrated and compared using Bayesian approaches. Molecular Systems Biology 9(1).