  Optimization is key to solve many problems in computational biology. Global
optimization methods provide a robust methodology, and metaheuristics in
particular have proven to be the most efficient methods for many applications.
Despite their utility, there is limited availability of metaheuristic tools. 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:
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. Both 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
\url{http://www.iim.csic.es/~gingproc/meigo.html}. 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, outperforming
other state-of-the-art methods. 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.
