BIOINFORMATICS APPLICATIONS NOTE Vol. 25 no. 6 2009, pages 834–835doi:10.1093/bioinformatics/btp061

Systems biology

Systems biology metabolic modeling assistant: anontology-based tool for the integration of metabolic data inkinetic modelingArmando Reyes-Palomares1,†, Raul Montañez1,†, Alejando Real-Chicharro1,Othmane Chniber2, Amine Kerzazi2, Ismael Navas-Delgado2, Miguel Ángel Medina1,José F. Aldana-Montes2 and Francisca Sánchez-Jiménez1,∗1Department of Molecular Biology and Biochemistry and unit 741 of CIBER ‘de Enfermedades Raras’ and2Department of Computer Languages and Computational Sciences, Campus de Teatinos, Universityof Malaga, 29071, Spain

Received and revised on December 27, 2008; accepted on January 24, 2009

Advance Access publication February 2, 2009

Associate Editor: Trey Ideker

ABSTRACT

Summary: We present Systems Biology Metabolic ModelingAssistant (SBMM Assistant), a tool built using an ontology-basedmediator, and designed to facilitate metabolic modeling through theintegration of data from repositories that contain valuable metabolicinformation. This software can be used for the visualization, designand management of metabolic networks; selection, integration andstorage of metabolic information; and as an assistant for kineticmodeling.Availability: SBMM Assistant for academic use is freely available athttp://www.sbmm.uma.es.Contact: kika@uma.es

1 INTRODUCTIONThe ability to perform dynamic analysis of biochemical reactionsnetworks is essential for understanding the intrinsic complexityof biological systems. Systems biology platforms now use astructured standard of biological information (http://www.sbml.org)and software for the analysis of biological networks, e.g.Cytoscape (http://www.cytoscape.org), and the dynamic behaviorof biochemical reactions e.g. COPASI (http://www.copasi.org)and CellDesigner (http://www.celldesigner.org). These advancescorrelate with the increased rate of growth in available biologicalinformation and the reorganization of data service architectures.

For metabolic modeling, diverse information is required, from thecharacteristics of enzymes, metabolites and modulators to the globalstructure and dynamics of networks. One of the acknowledgedbarriers to efficient kinetic modeling is the lack of availabilityof kinetic parameters. To alleviate this problem, repositoriesfor kinetic data have been built e.g. BRENDA (http://www.brenda.uni-koeln.de); and SABIO-RK (http://sabio.villa-bosch.de)

∗To whom correspondence should be addressed.†The authors wish it to be known that, in their opinion, the first two authorsshould be regarded as joint First Authors.

(Schomburg et al., 2004; Wittig et al., 2006). On the other hand,semantic web technologies can address the shortcomings in theworkflows used for metabolic modeling (Lee et al., 2008; Oinn etal., 2004).

In the present note, we describe Systems Biology MetabolicModeling Assistant (SBMM Assistant), an ontology-basedapplication designed to facilitate the process of metabolic modeling.SBMM Assistant is an SBML-compatible (Hucka et al.,2003) anduser-friendly tool that gives both the novice or experienced userthe ability to capture, enrich, generate and visualize biologicalnetworks, to make basic queries about enzymatic kinetics andregulation, and to annotate this information using MIRIAM (LeNovère et al. 2005) specifications. Furthermore, SBMM Assistantfacilitates friendly cross-talk among different resources and tools.These features provide SBMM Assistant with capabilities notpresent in other, previously reported applications.

2 DESCRIPTIONSBMM Assistant uses an ontology-based mediator developedto integrate data from KEGG (Kanehisa and Goto 2000),ChEBI (http://www.ebi.ac.uk/chebi) (Brooksbank et al., 2005),BRENDA and SABIO-RK. KEGG provides information aboutpathways, enzymes, reactions and compounds. SABIO-RK hasdata on reactions and enzymes, including kinetic parameters andequations. BRENDA contains information about kinetic parametersand modulators. ChEBI has the general chemical properties ofmetabolites and modulators, including molecular composition andmolecular weight. The mediator architecture is composed of threemain components: the Controller (which receives user requests andcoordinates the mediator components), the Query Planner (whichelaborates one or several query plans to retrieve data for the userfrom different data sources) and the Evaluator/Integrator (whichanalyzes the query plan and performs the necessary calls to thedata services involved in the sub-queries of the query plan). Forfurther information about the computational work and architecture ofSBMM Assistant, refer to http://sbmm.uma.es/ and previous works(Navas-Delgado et al., 2008).

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Systems biology metabolic modeling assistant

3 APPLICATIONS

3.1 Metabolic network visualization and managementSBMM Assistant uses an interactive graphic interface to facilitatequeries and capture information. Biochemical reaction networkscan be visualized following the Systems Biology GraphicalNotation (http://www.sbgn.org) with CellDesigner labels. Therefore,reactions loaded as SBML files, or queried online, can be edited ina friendly, intuitive and standardized environment.

3.2 Selection, integration and storage of the metabolicinformation

The use of a mediator enables the user to only access informationrelated to the structures and kinetic properties of the reactions beingstudied. All of this information is integrated by the mediator and canbe edited by the user, as mentioned in the previous section.

The integrated information is annotated in a SBML file, accordingto MIRIAM (Le Novère et al. 2005). Because SBMM Assistant wasdesigned to promote cross-talk with other metabolic modeling tools,every compound is identified by both its KEGG compound numberand its ChEBI identifier, and every enzyme/reaction is identified byits EC code as well as its KEGG reaction number.

3.3 Design of metabolic networksSBMM Assistant makes it possible to design metabolic networksusing data from a KEGG pathway, a SBML file or a list of ECnumbers. KEGG pathways can be referred to by ID (e.g. hsa00010)or name (e.g. glycolysis). SBML files can be obtained fromdatabases such as BIOMODELS (http://www.ebi.ac.uk/biomodels-main/static-pages.do?page=home) or can be uploaded by the user(as long as each reaction and compound is identified accordingto MIRIAM specifications). The application can also generatemetabolic networks by integrating information from a list of ECnumbers. Once the new metabolic structure is built, it can bemanually modified to obtain the final desired configuration for thenetwork.

3.4 Assistance with kinetic modelingAfter a series of easy steps, a user can construct a formalimplementation of the kinetics of a given reaction using SBMMAssistant. First, the user must define the reaction stoichiometryand the regulatory elements. Next, the kinetic law describing thereaction can be found by choosing the relevant kinetics equationsfrom SABIO-RK, or alternatively, defined by user. The followingstep allows user to query BRENDA or SABIO-RK to find the valuesof the kinetic parameters of the enzymes involved in the reaction.It is possible to assign each parameter either as a constant or as atime-dependent condition, and to describe each parameter as globalor local. The user can also read the abstract of references associatedwith each parameter in the repositories to evaluate its accuracy underthe desired simulation conditions.

4 FUTURE AND SCOPEThe aim of SBMM Assistant is to help users overcome the majorproblems encountered in metabolic modeling, in a standardizedenvironment. It is a semantic, web-based tool that will provideintegrated, up-to-date metabolic information that the user can addto, as well as consult in a friendly and interactive way. Thus,it is useful for both experienced metabolic modelers and novicebiochemists. This tool could be potentially used to establish thevariation in each kinetic parameter in a reaction, with availableparameter optimization methods. It is fully compatible with themajor systems biology standards, and so it can be integrated into thecurrent platforms that are used by the preexisting biocomputationalsystems biology infrastructure. Finally, it is worth noting that thepresent architecture will allow us to easily integrate data from morerepositories, to enrich subsequent versions of the application and thatthe use of an ontology-based mediator could allow us to improvethe integration process, as well as to infer new knowledge.

ACKNOWLEDGEMENTSThis work has been carried out by the unit 741 of the CIBER‘de Enfermedades Raras’ (Rare Diseases). The CIBER ‘deEnfermedades Raras’ is an initiative of the ISCIII (Spanish Ministryof Health).

Funding: Plan Andaluz de Investigación (BIO-267 and Grantsof Excellence 2999); The Ramón Areces Foundation; Ministryof Sciences and Innovation, Spain (SAF2008-02522); SpanishMinistry of Education and Science (TIN2005-09098-C05-01); Juntade Andalucía project P07-TIC-02978; CIBER (Grant 741.1).

Conflict of Interest: none declared.

REFERENCESBrooksbank,C. et al. (2005) The European Bioinformatics Institute’s data resources:

towards systems biology. Nucleic Acids Res., 33, D46–D53.Hucka,M.et al. (2003) The systems biology markup language (SBML): a medium for

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Kanehisa,M. and Goto,S. (2000) KEGG: kyoto encyclopedia of genes and genomes.Nucleic Acids Res., 28, 27–30.

Lee,D.-Y. et al. (2008) Web-based applications for building, managing and analysingkinetic models of biological systems. Brief. Bioinform. [Epub ahead of print,doi:10.1093/bib/bbn039, September 19, 2008].

Le Novère,N. et al. (2005) Minimum information requested in the annotation ofbiochemical models (MIRIAM). Nat. Biotechnol., 23, 1509–1515.

Navas-Delgado,I. et al. (2008) AMMO-Prot: amine system project 3D-model finder.BMC Bioinformatics, 9(Suppl 4), S5.

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