Available via license: CC BY-NC 2.5
Content may be subject to copyright.
MetExplore: a web server to link metabolomic
experiments and genome-scale metabolic networks
Ludovic Cottret
1,
*, David Wildridge
2
, Florence Vinson
1
, Michael P. Barrett
2
,
Hubert Charles
3,4
, Marie-France Sagot
3,5
and Fabien Jourdan
1
1
INRA, UMR1089, Xe
´nobiotiques, F-31000 Toulouse, France,
2
Division of Infection and Immunity, Glasgow
Biomedical Research Centre, University of Glasgow, Glasgow, UK,
3
Bamboo Team, INRIA Grenoble-Rho
ˆne-Alpes,
38330 Montbonnot Saint-Martin,
4
UMR203 Biologie Fonctionnelle Insectes et Interactions (BF2I), INRA, INSA-Lyon,
Universite
´de Lyon, F-69621 Villeurbanne and
5
Universite
´de Lyon, F-69000, Lyon; Universite
´Lyon 1; CNRS,
UMR5558, Laboratoire de Biome
´trie et Biologie Evolutive, F-69622, Villeurbanne, France
Received January 29, 2010; Revised March 30, 2010; Accepted April 17, 2010
ABSTRACT
High-throughput metabolomic experiments aim at
identifying and ultimately quantifying all metabolites
present in biological systems. The metabolites are
interconnected through metabolic reactions, gener-
ally grouped into metabolic pathways. Classical
metabolic maps provide a relational context to help
interpret metabolomics experiments and a wide
range of tools have been developed to help place
metabolites within metabolic pathways. However,
the representation of metabolites within separate
disconnected pathways overlooks most of the con-
nectivity of the metabolome. By definition, reference
pathways cannot integrate novel pathways nor show
relationships between metabolites that may be
linked by common neighbours without being con-
sidered as joint members of a classical biochemical
pathway. MetExplore is a web server that offers the
possibility to link metabolites identified in
untargeted metabolomics experiments within the
context of genome-scale reconstructed metabolic
networks. The analysis pipeline comprises
mapping metabolomics data onto the specific meta-
bolic network of an organism, then applying
graph-based methods and advanced visual-
ization tools to enhance data analysis. The
MetExplore web server is freely accessible at
http://metexplore.toulouse.inra.fr.
INTRODUCTION
Metabolomics aims at identifying the metabolome, i.e. the
full set of metabolites present in a biological system (1).
Cellular metabolite concentrations are ultimately a reflec-
tion of cell function (i.e. gene expression regulation and
protein interactions). They are modulated by genetic or
environmental perturbations and thus can be considered
as central to the phenotype of an organism. These metab-
olites are the inputs and outputs of biochemical reactions
organized into the complex system commonly termed the
metabolic network. To date, techniques that allow the
quantitative measurement of all metabolites within a
given system are not available and this confounds
systems level analysis of output from metabolomics ex-
periments. Methods that permit meaningful connections
to be inferred between metabolites thus offer the potential
to enhance metabolite analysis from the network
perspective.
The availability of complete genome sequences has
allowed the construction of predicted metabolic
networks for many organisms by using information on
the presence of enzymes inferred from the presence of
the genes that encode them and reference to known bio-
chemical pathways whose structure was determined
through the methods of classical biochemistry (2,3). The
main metabolic databases such as KEGG (4) or BioCyc
(5) are built on this pathway-oriented model. In
metabolomics, these databases are used for the analysis
of metabolites in the context of global metabolism. The
MassTRIX web server (6), for example, shows candidate
metabolites as coloured objects on the KEGG pathway
maps (7). If the positive identification of metabolites has
already been made, tools such as the Omics Viewer of
BioCyc (8) or the pathway projector (9) allow metabolites
to be highlighted on a collection of organism-specific
metabolic maps.
However, in such models, the same metabolite is
duplicated if it is involved in multiple metabolic
pathways. Moreover, some paths linking identified
*To whom correspondence should be addressed. Tel: +33 561285720; Fax: +33 561285244; Email: ludovic.cottret@toulouse.inra.fr
W132–W137 Nucleic Acids Research, 2010, Vol. 38, Web Server issue Published online 5 May 2010
doi:10.1093/nar/gkq312
ßThe Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
metabolites can span several pathways and thus are diffi-
cult to detect. Finally, the list of metabolic pathways
supposed to be present in the metabolic databases is
often predicted in an automatic way and can contain
many errors and omissions (10). Furthermore, novel,
organism-specific pathways cannot be integrated into
such networks since construction is dependent upon com-
parison to reference metabolic pathways.
To restore realistic connectivity between metabolites
and to overcome the problems of metabolic pathway iden-
tification, the metabolism of an organism is best inter-
preted as a network gathering all pathways into a single
structure. The MetExplore web server allows the mapping
of data from metabolomic experiments onto genome-scale
metabolic networks. Beyond the mapping functionalities,
the network can also be analysed using graph-based
methods. A graph is a mathematical object made of
nodes and edges connecting them. In MetExplore,
graphs are used to offer several advanced mining
features including choke point analysis (11), computation
of biosynthetic capacity (12) and potential precursor de-
termination for any set of identified metabolites.
MetExplore provides novel filters to restrict metabolism
to a particular set of pathways and to discard cellular
macromolecules such as proteins, RNA and DNA.
Filters were also created to remove ubiquitous compounds
or generic reactions. Finally, filtered metabolic networks
can be exported into SBML files and metabolic graph files.
MetExplore provides a unique framework to link
metabolomics experiments, metabolic network visualiza-
tion and modelling.
The fact that many metabolites contribute to multiple
pathways means that a network approach can offer ad-
vantages to interpretation of metabolomic experiments. A
network approach can, for example, be used to show how
two metabolites that may each be transformed directly to,
or from, a common metabolite, but as members of
separate classical pathways, are separated by only a
single chemical species within the metabolic network.
OVERVIEW
MetExplore follows the workflow given in Figure 1. It
consists in building tailor-made filtered networks in
order to place experimental data into the context of the
known, or predicted, metabolism of a selected organism.
For each functionality, various outputs are proposed.
The first building block of MetExplore is a relational
database (freely available on request) containing the meta-
bolic networks of about 50 organisms. Metabolic informa-
tion currently comes from BioCyc-like databases
(5,13–17). The list of organisms and their source is avail-
able in the online documentation of MetExplore. The
latter also contains two databases containing metabolic
information from multiple organisms: PlantCyc that de-
scribes metabolic pathways present in 250 plant species
(http://www.plantcyc.org) and MetaCyc that contains
1400 pathways from more than 1800 organisms (5).
Analysing the networks as stored in the databases can
lead to misinterpretations. For instance, ubiquitous com-
pounds like water or ATP contribute to many reactions
and can overload networks in spite of their not represent-
ing formal transitional steps in classical biochemical
pathways (18). To overcome this limitation, MetExplore
proposes various filters that can be applied to the stored
networks.
Once a relevant network is built for a given organism,
two kinds of function can be used to perform metabolite
analysis. First, MetExplore can map to the genome-
inferred metabolic networks the list of metabolites
generated from metabolomics experiments (using metab-
olite masses or metabolite identifiers). Second,
MetExplore provides computational functions that allow
investigation of the network features of a set of metabol-
ites, allowing, for example, searches for potential drug
targets.
Each MetExplore function returns a web browsable
table and allows visualization of the results through the
graph visualization tool, Cytoscape (19). Each filtered
Figure 1. MetExplore work flow.
Nucleic Acids Research, 2010, Vol. 38, Web Server issue W133
metabolic network can be exported as an SBML file (20)
or as a graph file to allow further studies using other
modelling tools as described.
METABOLIC NETWORK FILTERING
The filters available in MetExplore have three functions.
First, their implementation can allow investigation of
selected subparts of metabolism. Second, they can be
used to avoid sources of misinterpretation in metabolic
graph analyses and finally, they can aid in providing
clearer visualizations of the network, by restricting
analyses to only small molecule metabolites excluding
cellular macromolecules (proteins, nucleic acids,
glycoconjugates, etc.).
A common problem during metabolic network
modelling is to deal with the so-called ubiquitous com-
pounds. These compounds, involved in many reactions,
can cause artefacts when considered in the same way as
components of a linear transformation series as metabol-
ites of classical biochemical pathways. These molecules,
e.g. ATP, which provides phosphate to hundreds of reac-
tions, short-circuit the network and confound network
analysis (18).
Two ways are proposed in MetExplore to filter out
these compounds. First, we can distinguish between
main compounds and side compounds in the metabolic
pathways stored in the BioCyc-like databases. The main
compounds are involved in the backbone of the metabolic
pathway, while the side compounds are molecules such as
common cofactors that contribute to reactions as chemical
donors or recipients without being part of the transform-
ation chain of the metabolic pathway. The MetExplore
filter removes metabolites that are annotated as a side
compound for given reactions regardless of the metabolic
pathway considered. The second MetExplore method to
deal with ubiquitous compounds uses a list of 62 cofactor
transformations (available in the online documentation).
The filter removes these compounds from each reaction in
which they participate together. For instance, ATP, ADP
and phosphate are removed in each reaction where the
transformation ‘ATP = ADP + Phosphate’ appears.
Unlike the previous filter, this function does not use any
pathway information and deals with reactions not classi-
fied in any metabolic pathway. Filtering ubiquitous com-
pounds also simplifies the visual representation of the
metabolic network by removing highly connected nodes.
Finally, a MetExplore user can choose to keep or
to remove all of the reactions involved in the pathways
identified in the selected organism, but for which
no enzymes are assigned [classically called pathway
holes (21)].
METABOLOME MAPPING
User input
After choosing an organism and tuning the network
filters, the required input data for metabolome mapping
is a tabulated file. Depending on the selected mode, the
first column of the input file corresponds either to
measured masses, database metabolite identifiers or me-
tabolite names. In the mass mapping mode, an error limit
in p.p.m. (parts per million) has to be defined. The iden-
tification of metabolites by their masses is not always
satisfactory since, even with high resolution mass spec-
trometry, the identification may be ambiguous (22), par-
ticularly with respect to isomers that by definition are of
identical mass. For this reason, MetExplore also uses me-
tabolite identifiers or user-defined names. The names can
be described by a regular expression when their syntax is
not exactly known. For instance, if the user does not know
if the metabolite coenzyme A is stored in the database as
co-A, coenzyme-A, or coA, he can use the regular expres-
sion co.*a that corresponds to any metabolite name that
starts by co, followed by any number of additional char-
acters and ends by a (the case is not sensitive). Additional
columns of the input file correspond to numerical values
that quantify, for instance, the retention time or the peak
intensities.
Each mass, name or identifier is compared to the infor-
mation stored in the MetExplore relational database for
the selected organism. As queries to external databases are
not required, the processing is quite fast: a mapping of 380
masses on the complete metabolic network of MetaCyc,
for example, takes 6 min.
Results
MetExplore is capable of mapping experimental data
uploaded by the user onto user-selected metabolic
networks. The output is a list of identified metabolites
which is enriched by metabolic network informa-
tion such as metabolic pathways involving these
metabolites.
Whatever the selected mode, a result table as described
in Figure 2A is displayed. The name of each identified
metabolite is a hyperlink to the source database. The
pathways in which the metabolite appears in the filtered
metabolic network are also indicated. Each numerical
value from the input file is reported and coloured depend-
ing on the quartile computed in the whole column to
which it belongs. A glyph corresponding to the local
topology around the metabolite in the filtered metabolic
network is also displayed. Simple visual inspection is thus
sufficient to indicate whether the metabolite is a source, an
output or a choke point (see definition below) in the
filtered metabolic network. To facilitate interpretation of
the results, it is possible to visualize identified metabolites
on the filtered metabolic network by launching Cytoscape
directly from MetExplore. The user does not have to
install Cytoscape since it is loaded via Java Web Start
(the only requirement is that the browser supports Java
1.5 or higher). A MetExplore visual style is automatically
applied and highlights the identified metabolites
(Figure 2B). Moreover, metabolic network attributes
including Enzyme Commission (EC) numbers, metabolic
pathways, masses and chemical formulae are loaded as
attributes in Cytoscape. All of these attributes and the
SBML file corresponding to the filtered metabolic
network are also downloadable from the MetExplore
interface.
W134 Nucleic Acids Research, 2010, Vol. 38, Web Server issue
The graphical representation of the entire metabolic
network of an organism can be very dense (i.e.with
many nodes and overlapping edges). Following reaction
paths connecting identified metabolites can be difficult
within such an output. To improve visualization in such
circumstances, we have developed a Cytoscape plug-in
(GapFiller) to compute subnetworks of identified metab-
olites (23) (Figure 2C). Finally, by creating a MetExplore
account, users can create a history of all analyses allowing
subsequent consultation without reprocessing.
METABOLIC GRAPH ANALYSES
In addition to offering the potential to store and visualize
data from metabolomics data sets within the context of a
virtual metabolome, MetExplore provides several func-
tions based on graph analysis that facilitate understanding
of the role of metabolites within the network. For
example, these methods can be used for drug target
identification (11) or to decipher the biosynthetic links
between metabolites (24,12).
Choke points
One successful and practical use of global metabolic
networks has been the introduction of choke point
analysis. Choke points are defined as reactions that
either uniquely consume a specific substrate or uniquely
produce a specific product (11). Choke point analysis,
such as used for the malaria parasite Plasmodium falcip-
arum has shown current drug targets are far more likely to
be choke points within the network than other reactions
(11). Furthermore, comparison between choke points in
parasites and their hosts identifies parasite-specific choke
points as being even better candidate targets.
Scope
The scope of metabolites allows the identification of the
potential biosynthetic capacity of an organism from a
Figure 2. Mapping results. (A) Table of results (B) Visualization of the identified metabolites in the metabolic network. (C) Extraction of the
subnetwork linking the identified metabolites.
Nucleic Acids Research, 2010, Vol. 38, Web Server issue W135
given set of metabolites (12). The scope of a set of metab-
olites (so-called seeds) is defined as the sum of all metab-
olites that the seeds are able to produce using the reactions
available in an organism. In contrast to the shortest paths
computed in simple graphs, the scope concept takes into
account the availability of all of the substrates used in a
reaction. The scope is computed in an iterative way
[referred to as the expansion process (12)]. A table is
generated to display information about the metabolites
produced during the process.
Precursors
Precursors are the set of metabolites from which a defined
set of target metabolites can be produced. Precursor sets
can be calculated by the inverse of the expansion process
described in the scope function, starting with a set of given
target metabolites, then moving backwards through the
dataset until a metabolite is reached that is not
produced by any reaction or metabolite already visited
during the process. Two outputs can be generated from
this computation: a table containing all the metabolites
visited during the process and the precursors or a table
containing only the precursors. For instance, these precur-
sors might then be considered essential components of any
defined culture medium required for growth of a microbial
organism or cell type.
When Cytoscape is launched via the scope and precur-
sor functions, the filtered metabolic network and the sub-
network visited during the process are automatically
loaded.
METABOLIC NETWORK EXPORT
While MetExplore, especially used in conjunction with
visualization environments such as Cytoscape, offers
multiple functions regarding presentation and analysis of
metabolomic data sets, clearly there are a multitude of
additional software options that may be useful in further
analysis of MetExplore file outputs. SBML is an XML-
based format dedicated to the description of systems
biology type data sets including metabolic networks (20).
Filtered networks from each MetExplore function
described above and the corresponding attributes can be
exported directly to SBML. Moreover, the extended
SBML that we created stores information about masses,
EC numbers, links between genes, reactions and pathways
not present in a classical SBML file. This format is
described in the online documentation.
From the filtered metabolic networks, MetExplore is
able to build three kinds of graphs: the compound
graph, the reaction graph and the bipartite graph [for def-
initions see (3)]. Each one is available in edge-list format
and can be visualized in Cytoscape or used to perform
other graph analyses.
DISCUSSION AND CONCLUSION
Here, we describe novel software to help visualize,
navigate, mine and draw biological inference from
metabolomics experiments set within the context of
global metabolic networks. MetExplore gathers a set of
original functionalities that can be easily and freely
accessed through its web server. The mapping function
enables identification of metabolites predicted within
the metabolism of a given organism, using either mass
information, database identifiers or their simple names.
Since metabolite names are frequently poorly formatted,
MetExplore allows the inclusion of regular expressions
to describe them. As MetExplore does not require access
to other web servers, the processing involved in mapping is
very fast as it queries only the MetExplore database.
Rather than mapping the identified metabolites to individ-
ual pathways or to a collection of pathways, MetExplore
maps them onto a single metabolic network. This yields
topological information about the identified metabolites.
Another advantage of MetExplore resides in its ability to
highlight identified metabolites within the metabolic
network by directly launching Cytoscape from the
MetExplore interface. It is thus possible to visualize
output from metabolomic experiments with the attributes
of the metabolic network also loaded. To generate a
clearer visualization or to study only a subsection of
the metabolic network, MetExplore contains various
filters to be applied to networks prior to mapping.
Moreover, the GapFiller plug-in installed in the
Cytoscape version loaded from MetExplore further facili-
tates analysis by computing subnetworks that link
identified metabolites.
MetExplore also provides additional high-level func-
tions that allow further inference from the mapping
results. By computing the choke points in a filtered meta-
bolic network, it is possible in MetExplore to detect weak
points in the metabolic network, which offer potential
drug targets. Furthermore, the scope function imple-
mented in MetExplore helps to decipher which metabol-
ites and reactions can be affected by the changes in
concentration of a set of metabolites. In a converse
function, since metabolomics generally measures the
outputs of the metabolism, MetExplore also computes
which metabolites are necessary to produce those
observed experimentally. The results of these functions
can also be directly visualized by launching Cytoscape.
Finally, the possibility to save the filtered metabolic
networks into SBML and several graph formats facilitates
the first steps of the metabolic network modelling.
We are currently working on further improvements for
MetExplore. The database currently contains only about
50 organism-specific data sets. We plan to increase this
number, especially by including data sets from other data-
bases such as KEGG (4) or HMDB (25). Furthermore, we
are working on the possibility of applying the MetExplore
functions to a metabolic network uploaded by the user.
Other graph analysis tools will be included in future
versions of MetExplore. For instance, some topological
graph measures including betweeness centrality (26)
should facilitate an appreciation of the importance of
identified metabolites in a metabolic network. Inclusion
of data from other sources (e.g. proteomics and
transcriptomics) is also under consideration for
enhanced analysis through MetExplore.
W136 Nucleic Acids Research, 2010, Vol. 38, Web Server issue
FUNDING
French projects (ANR REGLIS NT05-3 45205 and ANR
MIRI BLAN08-1335497); French-UK projects (ANR-
BBSRC MetNet4SysBio ANR-07-BSYS 003 02 and
ANR-BBSRC SysTryp). Funding for open access
charge: University of Glasgow.
Conflict of interest statement. None declared.
REFERENCES
1. Fiehn,O. (2001) Combining genomics, metabolome analysis, and
biochemical modelling to understand metabolic networks. Comp.
Funct. Genomics,2, 155–168.
2. Francke,C., Siezen,R.J. and Teusink,B. (2005) Reconstructing the
metabolic network of a bacterium from its genome. Trends
Microbiol.,13, 550–558.
3. Lacroix,V., Cottret,L., The
´bault,P. and Sagot,M.-F. (2008) An
introduction to metabolic networks and their structural analysis.
IEEE/ACM Trans. Comput. Biol. Bioinform.,5, 594–617.
4. Kanehisa,M., Goto,S., Furumichi,M., Tanabe,M. and
Hirakawa,M. (2010) KEGG for representation and analysis of
molecular networks involving diseases and drugs. Nucleic Acids
Res.,38, D355–D360.
5. Caspi,R., Altman,T., Dale,J.M., Dreher,K., Fulcher,C.A.,
Gilham,F., Kaipa,P., Karthikeyan,A.S., Kothari,A.,
Krummenacker,M. et al. (2010) The metacyc database of
metabolic pathways and enzymes and the biocyc collection of
pathway/genome databases. Nucleic Acids Res.,38, D473–D479.
6. Suhre,K. and Schmitt-Kopplin,P. (2008) MassTRIX: mass
translator into pathways. Nucleic Acids Res.,36, W481–W484.
7. Okuda,S., Yamada,T., Hamajima,M., Itoh,M., Katayama,T.,
Bork,P., Goto,S. and Kanehisa,M. (2008) KEGG Atlas mapping
for global analysis of metabolic pathways. Nucleic Acids Res.,36,
W423–W426.
8. Paley,S.M. and Karp,P.D. (2006) The pathway tools cellular
overview diagram and omics viewer. Nucleic Acids Res.,34,
3771–3778.
9. Kono,N., Arakawa,K., Ogawa,R., Kido,N., Oshita,K.,
Ikegami,K., Tamaki,S. and Tomita,M. (2009) Pathway projector:
web-based zoomable pathway browser using kegg atlas and
google maps api. PLoS ONE,4, e7710 [11 November 2009, Epub
ahead of print].
10. Ginsburg,H. (2009) Caveat emptor: limitations of the automated
reconstruction of metabolic pathways in Plasmodium. Trends
Parasitol.,25, 37–43.
11. Yeh,I., Hanekamp,T., Tsoka,S., Karp,P.D. and Altman,R.B.
(2004) Computational analysis of Plasmodium falciparum
metabolism: organizing genomic information to facilitate drug
discovery. Genome Res.,14, 917–924.
12. Handorf,T., Ebenho
¨h,O. and Heinrich,R. (2005) Expanding
metabolic networks: scopes of compounds, robustness, and
evolution. J. Mol. Evol.,61, 498–512.
13. Evsikov,A.V., Dolan,M.E., Genrich,M.P., Patek,E. and Bult,C.J.
(2009) Mousecyc: a curated biochemical pathways database for
the laboratory mouse. Genome Biol.,10, R84.
14. Keseler,I.M., Bonavides-Martı
`nez,C., Collado-Vides,J., Gama-
Castro,S., Gunsalus,R.P., Johnson,D.A., Krummenacker,M.,
Nolan,L.M., Paley,S., Paulsen,I.T. et al. (2009) Ecocyc: a
comprehensive view of Escherichia coli biology. Nucleic Acids
Res.,37, D464–D470.
15. Romero,P., Wagg,J., Green,M.L., Kaiser,D., Krummenacker,M.
and Karp,P.D. (2005) Computational prediction of human
metabolic pathways from the complete human genome.
Genome Biol.,6, R2.
16. Vallenet,D., Labarre,L., Rouy,Z., Barbe,V., Bocs,S., Cruveiller,S.,
Lajus,A., Pascal,G., Scarpelli,C. and Me
´digue,C. (2006) Mage: a
microbial genome annotation system supported by synteny results.
Nucleic Acids Res.,34, 53–65.
17. Zhang,P., Foerster,H., Tissier,C.P., Mueller,L., Paley,S.,
Karp,P.D. and Rhee,S.Y. (2005) Metacyc and aracyc. metabolic
pathway databases for plant research. Plant Physiol.,138, 27–37.
18. Arita,M. (2004) The metabolic world of Escherichia coli is not
small. Proc. Natl Acad. Sci. USA,101, 1543–1547.
19. Shannon,P., Markiel,A., Ozier,O., Baliga,N.S., Wang,J.T.,
Ramage,D., Amin,N., Schwikowski,B. and Ideker,T. (2003)
Cytoscape: a software environment for integrated models of
biomolecular interaction networks. Genome Res.,13, 2498–2504.
20. Finney,A. and Hucka,M. (2003) Systems biology markup
language: level 2 and beyond. Biochem. Soc. Trans.,31(Pt 6),
1472–1473.
21. Green,M.L. and Karp,P.D. (2004) A bayesian method for
identifying missing enzymes in predicted metabolic pathway
databases. BMC Bioinformatics,5, 76.
22. Kind,T. and Fiehn,O. (2006) Metabolomic database annotations
via query of elemental compositions: mass accuracy is insufficient
even at less than 1 ppm. BMC Bioinformatics,7, 234.
23. Jourdan,F., Cottret,L., Wildridge,D., Scheltema,R., Hillenweck,A.,
Barrett,M.P., Zalko,D., Watson,D.G. and Debrauwer,L. (2010)
Use of reconstituted metabolic networks to assist in metabolomic
data visualization and mining. Metabolomics, doi:10.1007/
s11306-009-0196-9.
24. Handorf,T., Christian,N., Ebenho
¨h,O. and Kahn,D. (2007) An
environmental perspective on metabolism. J. Theor. Biol.,252,
530–537.
25. Wishart,D.S., Knox,C., Guo,A., Eisner,R., Young,N., Gautam,B.,
Hau,D.D., Psychogios,N., Dong,E., Bouatra,S. et al. (2009)
Hmdb: a knowledgebase for the human metabolome.
Nucleic Acids Res.,37, D603–D610.
26. Liu,W.-C., Lin,W.-H., Davis,A.J., Jorda
`n,F., Yang,H.-T. and
Hwang,M.-J. (2007) A network perspective on the topological
importance of enzymes and their phylogenetic conservation.
BMC Bioinformatics,8, 121.
Nucleic Acids Research, 2010, Vol. 38, Web Server issue W137
