Reference : An integrated network visualization framework towards metabolic engineering applications
Scientific journals : Article
Life sciences : Biotechnology
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/19356
An integrated network visualization framework towards metabolic engineering applications
English
Noronha, Alberto mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > > ; University of Minho > Department of Informatics]
Vilaça, Paulo [University of Minho > Department of Informatics > > ; Silico Life Lda, Braga, Portugal]
Rocha, Miguel mailto [University of Minho > Department of Informatics]
30-Dec-2014
BMC Bioinformatics
BioMed Central
15
Yes (verified by ORBilu)
1471-2105
[en] Metabolic network visualization ; Metabolic engineering ; Open-source software
[en] Background
Over the last years, several methods for the phenotype simulation of microorganisms, underspecified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential.
Results
In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks.
Conclusions
The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.
http://hdl.handle.net/10993/19356
10.1186/s12859-014-0420-0
http://www.biomedcentral.com/1471-2105/15/6594/abstract

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