Meigo Software biology and bioinformatics global optimization in systems
Meigo in Software:
MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics. BMC Bioinformatics, 15: 136.
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-136
https://pubmed.ncbi.nlm.nih.gov/24885957/
https://scholar.google.com/scholar_lookup?title=MEIGO
https://link.springer.com/article/10.1186/1471-2105-15-136
https://link.springer.com/content/pdf/10.1186/1471-2105-15-136.pdf
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. 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.
"..Mathematically, this is encoded as an hyper graph, where edges with
two or more inputs (hyperedges) represent a logical disjunction (AND
gate). OR gates are encoded implicitly, by means of edges with only one
input arriving at a node. See [38] for details.
To calibrate such models, the authors formulated the inference problem as a binary multi-objective problem, where the first objective corresponded to how well the model described the experimental data and the second consisted of a complexity penalty to avoid over-fitting:
where
and such that Bk,l,t(P)∈{0,1} is the value (0 or 1) as predicted by computation of the model’s logical steady state [42] and is the data value for readout l at time t under the kth experimental condition. θ f (P) is the mean squared error and α·θ s (P) is the product between a tunable parameter α and a function denoting the model complexity (each hyper edge receives a penalty proportional to the number of inputs. E.g. an AND gates with 3 inputs is penalised 3 times as a single edge. OR gates arise implicitly from the combination of single input edges.) .. "
Project name: Metaheuristics for global optimization in systems biology and bioinformatics (MEIGO)
Project home page:http://www.iim.csic.es/~gingproc/meigo.html Operating system(s): Windows, Linux, Mac OS X Programming language: Matlab 7.5 or higher and R 2.15 or higher Licence: GPLv3
https://cdnsciencepub.com/doi/10.1139/cjce-2018-0137
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