Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Software

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics

Jose A Egea1, David Henriques23, Thomas Cokelaer3, Alejandro F Villaverde2, Aidan MacNamara3, Diana-Patricia Danciu3, Julio R Banga2* and Julio Saez-Rodriguez3*

Author Affiliations

1 Department of Applied Mathematics and Statistics, Universidad Polit├ęcnica de Cartagena, 30202 Cartagena, Spain

2 (Bio)Process Engineering Group, Spanish National Research Council, IIM-CSIC, 36208 Vigo, Spain

3 European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK

For all author emails, please log on.

BMC Bioinformatics 2014, 15:136  doi:10.1186/1471-2105-15-136

Published: 10 May 2014

Abstract

Background

Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools.

Results

We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html webcite. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods.

Conclusions

MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.