OptFlux. A software for metabolic engineering

OptFlux is an open-source, user-friendly and modular software aimed at being the reference computational tool for metabolic engineering applications.
It allows the use of stoichiometric models of microbial metabolism for simulation and optimization purposes.

Metabolic engineering deals with designing organisms with enhanced capabilities regarding the productivities of desired compounds1. This field has received increasing attention within the last few years due to the extraordinary growth in the adoption of white or industrial biotechnological
processes for the production of bulk chemicals, pharmaceuticals, food ingredients and enzymes, among other products.

Many different approaches have been used to aid in metabolic engineering efforts that take available models of metabolism together with mathematical tools and/or experimental data to identify metabolic bottlenecks or targets for genetic engineering. However, the rational design of microbial strains has been limited to the developers of the computational or mathematical techniques, since a platform that provides a user friendly interface to perform such tasks was not yet available.

Towards the purpose of changing this scenario, we introduce the OptFlux, whose main features are the following:
- Open-source:- it allows all users to use the tool freely and invites the contribution of other researchers;
- User-friendly:- facilitates its use by users with no/little background in modelling/informatics;
- Modular :- facilitates the addition of specific features by computer scientists
- Compatible with standards:- compatibility with the Systems Biology Markup Language (SBML)2 and the layout information of CellDesigner3.
At the present version, the main methods implemented in OptFlux for the simulation of both wild-type and mutant organisms are Flux Balance Analysis1, Minimization of Metabolic Adjustment (MOMA)4, and Regulatory on/off Minimization of Metabolic flux changes (ROOM)5. The optimization tasks, i. e., the identification of metabolic engineering targets can be performed with Evolutionary Algorithms, Simulated Annealing and Local Search methods 6,7.

Other features like the calculation of Elementary Flux Modes8 and Metabolic Flux Analysis1 will be added to the present platform in the near future as new modules

The software is made available, together with other resources, in the home page: http://www.optflux.org

Isabel Rocha1, Paulo Maia1,2, José P. Pinto2, Pedro Evangelista1,2, Jens Nielsen4, Kiran Raosaheb Patil3, Eugénio C. Ferreira1, Miguel Rocha2
1 IBB-Institute for Biotechnology and Bioengineering / Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
2 Department of Informatics / CCTC, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
3 Center for Microbial Biotechnology, BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
4 Systems Biology, Dept. Chemical and Biological Engineering Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden

1 Stephanopoulos,G. et al. (1998 ) Metabolic engineering, Academic Press
2 Hucka,M. et al. (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524-531
3 Kitano,H. et al. (2005) Using process diagrams for the graphical representation of biological networks. Nature Biotechnology 23, 961-9664 Segre,D. et al. (2002) Analysis of optimality in natural and perturbed metabolic networks. PNAS 99, 15112-15117
5 Shlomi,T. et al. (2005) Regulatory on/off minimization of metabolic flux changes after genetic perturbations. PNAS 102, 7695-7700
6 Patil,K.R. et al. (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics6
7 Rocha,M. et al. (2008 ) Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinformatics 9, 499
8 Schuster,S.et al (2000) A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nature Biotechnology, 18, 326-332.
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