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:

- nonlinear programming (NLP) and mixed-integer nonlinear programming (MINLP) using the enhanced scatter search metaheuristic (eSS [1]).
- combinatorial (binary and integer programming) optimization problems using the variable neighbourhood metaheuristic (VNS [2]).
- continuous parameter estimation using the Bayesian inference method BayesFit (R version only), based in ref [5].

Examples of these problem classes in bioinformatics and systems biology are:

- parameter estimation in static and dynamic biology models
- network inference
- optimal experimental design
- metabolic engineering
- optimal design of synthetic biological networks
- sequence alignment
- genome rearrangement
- protein structure prediction

In order to facilitate its use and interfacing with other software packages, we provide:

- Matlab implementations (eSS, VNS)
- R implementations (eSSR, VNSR, BayesFit)
- Python interface to the R implementations

Furthermore, we also provide parallel implementations of these solvers based on a cooperative strategy [3]:

- cooperative parallel eSS (CeSS, in Matlab, and CeSSR, in R)
- cooperative parallel VNS (CVNS, in Matlab, and CVNSR, in R)

Related
publications

[1]
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.

[2] Hansen, P.. Mladenović, N. and Moreno Pérez,
J.A. (2010) Variable neighborhood search: methods and applications.
Annals of Operations Research, 175, 367–407.

[3] 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.

[4] 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.

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[5] 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).