The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
OptFlux: an open-source software platform for in silico metabolic engineering
Open accessAbstract
BACKGROUND: Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications. RESULTS: OptFlux is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes. OptFlux also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms. The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. OptFlux has a visualization module that allows the analysis of the model structure that is compatible with the layout information of Cell Designer, allowing the superimposition of simulation results with the model graph. CONCLUSIONS: The OptFlux software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community. Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models.
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Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
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Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0
Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.308 · Cited by 1,835
Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth
Ibarra, R. U., Edwards, J. S., & Palsson, B. O. (2002) · Nature · doi:10.1038/nature01149 · Cited by 857 · cited 1×
“…flux over the reaction that represents biomass formation is the one being maximized, since this has proven to be a good representation of the natural behaviour of microorganisms in many circum-stances [31], but it is possible to perform simulations by maximizing or minimizing any flux of the model.”
Using process diagrams for the graphical representation of biological networks
Kitano, H., Funahashi, A., Matsuoka, Y., & Oda, K. (2005) · Nature Biotechnology · doi:10.1038/nbt1111 · Cited by 483
LibSBML: an API Library for SBML
Bornstein, B. J., Keating, S. M., Jouraku, A., & Hucka, M. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn051 · Cited by 373
Metabolic modelling of microbes: the flux‐balance approach
Edwards, J. S., Covert, M., & Palsson, B. (2002) · Environmental Microbiology · doi:10.1046/j.1462-2920.2002.00282.x · Cited by 365
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MEMOTE for standardized genome-scale metabolic model testing
Lieven, C., Beber, M. E., Olivier, B. G., Bergmann, F. T., Ataman, M., Babaei, P., Bartell, J. A., Blank, L. M., Chauhan, S., Correia, K., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0446-y · Cited by 509
Self-citationAdaptive laboratory evolution — harnessing the power of biology for metabolic engineering
Portnoy, V. A., Bezdan, D., & Zengler, K. (2011) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2011.03.007 · Cited by 309
Critical assessment of genome-scale metabolic networks: the need for a unified standard
Ravikrishnan, A., & Raman, K. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv003 · Cited by 87
Saini, D. K., Rai, A., Devi, A., Pabbi, S., Chhabra, D., Chang, J.-S., & Shukla, P. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.124908 · Cited by 75
Emerging Species and Genome Editing Tools: Future Prospects in Cyanobacterial Synthetic Biology
Gale, G. A. R., Schiavon Osorio, A. A., Mills, L. A., Wang, B., Lea-Smith, D. J., & McCormick, A. J. (2019) · Microorganisms · doi:10.3390/microorganisms7100409 · Cited by 61
Metadata
- Authors
- Rocha, I., Maia, P., Evangelista, P., Vilaça, P., Soares, S., Pinto, J. P., Nielsen, J., Patil, K. R., Ferreira, E. C., & Rocha, M.
- Venue
- BMC Systems Biology
- Published
- 2010-04-19
- Volume
- 4
- Issue
- 1
- Pages
- 45-45
- ISSN
- 1752-0509 (ISSN-L)
- OpenAlex
- W2156409537
- Cited by
- 388
- References
- 6
- FWCI
- 13.00
- Type
- journal article
- Language
- en
- Open access
- gold
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- Rocha, I., Maia, P., Evangelista, P., Vilaça, P., Soares, S., Pinto, J. P., Nielsen, J., Patil, K. R., Ferreira, E. C., & Rocha, M. (2010). OptFlux: an open-source software platform for in silico metabolic engineering. BMC Systems Biology. https://doi.org/10.1186/1752-0509-4-45
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