Metabolic network visualization eliminating node redundance and preserving metabolic pathways
 Romain Bourqui^{1},
 Ludovic Cottret^{2},
 Vincent Lacroix^{2},
 David Auber^{1},
 Patrick Mary^{1},
 MarieFrance Sagot^{2} and
 Fabien Jourdan^{3}Email author
https://doi.org/10.1186/17520509129
© Bourqui et al; licensee BioMed Central Ltd. 2007
Received: 17 January 2007
Accepted: 03 July 2007
Published: 03 July 2007
Abstract
Background
The tools that are available to draw and to manipulate the representations of metabolism are usually restricted to metabolic pathways. This limitation becomes problematic when studying processes that span several pathways. The various attempts that have been made to draw genomescale metabolic networks are confronted with two shortcomings: 1 they do not use contextual information which leads to dense, hard to interpret drawings, 2 they impose to fit to very constrained standards, which implies, in particular, duplicating nodes making topological analysis considerably more difficult.
Results
We propose a method, called MetaViz, which enables to draw a genomescale metabolic network and that also takes into account its structuration into pathways. This method consists in two steps: a clustering step which addresses the pathway overlapping problem and a drawing step which consists in drawing the clustered graph and each cluster.
Conclusion
The method we propose is original and addresses new drawing issues arising from the noduplication constraint. We do not propose a single drawing but rather several alternative ways of presenting metabolism depending on the pathway on which one wishes to focus. We believe that this provides a valuable tool to explore the pathway structure of metabolism.
Background
Metabolism visualization for systems biology studies
The scale of metabolic studies varies according to the data and to the biological questions. For instance, toxicologists often follow the degradation of a given molecule; in that case they focus only on a very small number of reactions. At a larger scale, biologists studying glycolysis will focus on this particular metabolic pathway. Most of the work on metabolism visualization has been done at this level of detail [1–12]. However, in order to investigate an organism's metabolic response to stress, it is relevant to study all the pathways simultaneously. For instance, this will be useful for treating the results of high throughput experiments such as transcriptomic data where relevant gene products are identified in many pathways. Visualization is a suitable and obvious solution to achieve this kind of study, for instance by representing all the metabolic pathways in one drawing and by coloring relevant enzymes and metabolites [13–15]. In [16], the authors use this approach to analyze simultaneously transcriptomic and metabolomic data (they used Biocyc omics viewer [14]). Based on this representation, they managed to identify at once perturbations in the Calvin cycle, glycolysis and TCA cycle. Such kinds of studies emphasize the necessity to develop methods that allow to visualize the entire metabolic network in a single drawing.
Highlighting pathways according to experimental data provides some clues on metabolic processes. However, to integrate these conclusions in a systems biology approach, it is necessary to understand how these pathways are linked and how processes span over them.
The issue of analyzing biological processes spanning several metabolic pathways appears in many contexts. As we already mentioned, it appears when analyzing metabolomic or transcriptomic experiments, which are generally not pathwayfocused. This issue also arises for topological analyses based on motif detection [17]. A motif (defined as a set of reaction types) may occur in different parts of the network (which illustrates the need to visualize the whole network in a single picture), and each occurrence may be composed of reactions belonging to different pathways (which examplifies the need to explicitly visualize the links between the pathways).
Therefore, pathway visualization is not suitable for such tasks but neither is network visualization without pathway information. Indeed, to be useful for mapping experiments, it is necessary to represent the entire network structure while keeping the contextual information provided by its division into metabolic pathways. Note that this is one of the requirements for biological network visualization proposed in [18]. Recently, in addition to the studies that use the network as a background, great efforts have been devoted to the analysis of the topological properties of metabolic networks [19, 20]. Indeed topology could, for instance, give clues on the evolution of the organisms they are related to. More generally, topological features like shortest path, connectivity, node degrees and node/edge metrics have become common investigation tools. To visually retrieve topological information, it is necessary that the drawing provides a faithful image of the network structure. This is a challenging problem which has not been addressed by current metabolic network visualization tools [13, 14] which choose to allow node duplication and therefore do not face this issue.
In the case where nodes are not duplicated, pathways which share reactions and compounds cannot all be drawn equally well (a welldrawn pathway being a pathway having all its nodes drawn next to each other). Therefore, choices have to be made on which pathways will be drawn well in priority. We propose both an automatic way of making this choice and possibilities for the user to define his own priorities. This last option adds an interesting feature to the tool: depending on the choices made, the backbone of metabolism (the set of welldrawn pathways) can be adjusted to the pathways one is interested in. This backbone can either include the glycolysis and the TCA cycle as it is traditionnally the case in most drawings or, alternatively, it can include pathways that share compounds or reactions with glycolysis and the TCA cycle and which would, if not chosen, be drawn in the background. Playing around with this option enables to get a grip on the interdependence of the pathways.
The aim of this paper is to propose an algorithm to draw the entire metabolic network. The produced representation will have to follow textbook drawing conventions (see the following section), display information on the metabolic pathways and keep the topology of the network by avoiding node duplication.
Metabolic network drawing and visualization
Drawing metabolic pathways
A metabolic pathway (also called a metabolic map) is a subnetwork of the metabolic network. The decomposition of the entire network into metabolic pathways is generally done according to biological functions: molecule degradation (catabolism), molecule synthesis (anabolism) or energy transfer [21]. Until recently, these pathways have been manually drawn, for instance for teaching purposes, or to exchange results [22, 23]. Then, numerical versions of these manual drawings were proposed and used on web servers such as KEGG [3, 24].
In the last few years, automatic drawing algorithms have been designed, mainly for two reasons. First the number of organisms for which a metabolic network is described is increasing quickly. Indeed, in silico methods have been designed to reconstruct metabolic pathways from annotated genomes [25] which are more and more numerous. Second, these putative networks follow a regular curating process implying many changes in their structures. In this section, we describe the algorithms that have been proposed for drawing metabolic pathways since they could be extended to the entire network.
Because biologists are used to textbook representations, most of the automatic methods consist in following the drawing habits of these representations [22]. Even if there is no standard for these conventions, it is possible to identify the most commonly used ones. Some of the aesthetic criteria are also used in graph drawing [26–28]: lowering the number of edge crossings and lowering the number of bends on edges. Moreover, the biological nature of pathways implies some conventions. The notion of reaction cascade is central since generally metabolic pathways describe the transformation of input metabolites into output ones. Most automatic drawing algorithms have been designed to emphasize this structure. The algorithm proposed in [5] and implemented in Biominer uses a hierarchical drawing algorithm which embeds nodes on regular horizontal layers [29]. Others propose adapted versions of classical hierarchical drawing algorithms, like in [6] (implemented in BIOPATH [30]) or in [9] (implemented in Wilmascope).
Scaling to the whole metabolic network
In the Graph Drawing community, efficient drawing algorithms have been designed to draw large networks. Among them, forcebased layouts [31, 32] are commonly used. Such layouts mimic physical systems, that is, nodes are considered as masses (or particles) and edges behave as springs (or magnetic forces). This system evolves from a random embedding to one corresponding to an equilibrium, providing a suitable layout. These algorithms generate quite good drawings since they generally emphasize dense subgraphs and spread low degree nodes on the screen space. They are used in Cytoscape [33] or in the online SBML viewer [34] for instance. However, as mentioned in [18], such drawings are not satisfying for biologists. The first reason is that they do not follow textbook drawing conventions, and the second is that they emphasize topological clusters which generally do not correspond to a metabolic pathway decomposition. To overcome this last problem, forcebased methods could be used in a compound graph layout as it is done in [8] (implemented in PatikaWeb [12]). However, this tool is not dedicated to metabolic pathway visualization and thus does not follow all textbook drawing conventions.
The two main efforts for automatically drawing metabolic networks while keeping metabolic pathway information and respecting drawing conventions are: Reactome [13] and the Pathway Tools cellular overview diagram [14]. As it was mentioned before, in both tools nodes are duplicated thus the only drawing problem is to embed metabolic maps. Both achieve it by grouping maps according to their common functions. The latter assumes that a hierarchy on the pathways is given as input to the algorithm and is then used to display pathways close to each other when they are close to each other in the hierarchy. This functionality is not included in the current implementation of our algorithm. Nevertheless, it is still possible to circumvent this problem by redefining coarsegrained pathways (corresponding to groups of pathways of common functions) in the input data.
In the following sections, we first describe our metabolic network drawing algorithm. Then we discuss our approach and compare it to other published methods using the metabolic network of Esherichia coli (E. coli) as benchmark.
Implementation
Using a mixed bipartite graph to model metabolic networks
A graph provides an intuitive way of organizing large amounts of relational data. The general definition of a graph G = (V, E) is simple. It consists of a set V of n vertices (V = n) and a set E of m edges, each of which corresponds to a pairwise relationship between two of the nodes (E ⊆ V × V). Modeling the metabolic network consists in choosing which biological objects are associated to nodes and edges. It is necessary to do this model description before introducing the graph drawing algorithm, since it will constrain the representation. For instance, a model may imply that some nodes have a high degree, thus complicating a planarization process.
Bipartite graph
Mixed graph
Metabolic reaction can be either reversible (i.e. it can occur in both directions) or irreversible (i.e. it can occur in only one direction). This orientation is defined according to the physiological properties of a reaction. SBML descriptions of reactions provide this kind of information. In order to model such a biological phenomenon, we use a mixed graph. In a mixed graph, the set E of edges is splitted in two subsets A and E', where A is the set of arcs (i.e. oriented edges), E' is a set of nonoriented edges and E = A ⊕ E'.
Thus, for modeling the whole network, we use a mixed bipartite graph G = (R, S, A, E').
Graph hierarchy
A metabolic pathway is a subnetwork of the metabolic network. Here, it corresponds to a graph G_{ p }= (V_{ p }, E_{ p }) where V_{ p }⊂ V and E_{ p }= {(u, v) ∈ Eu ∈ V_{ p }and v ∈ V_{ p }} ⊂ E (i.e. E_{ p }is the set of edges and arcs induced by V_{ p }on E). For a given metabolic network G, we note P_{ G }= {G_{ i } 1 ≤ i ≤ n_{ p }} its n_{ p }metabolic pathways. One can notice that for each G_{ i }, V_{ i }and E_{ i }can be decomposed in four subsets R_{ i }, S_{ i }, A_{ i }and ${{E}^{\prime}}_{i}$ (i.e. G_{ i }is a mixed bipartite graph).
Taking pathways into account leads to the following graph hierarchy : the graph G representing the whole network and n_{ p }induced subgraphs G_{ i }representing its n_{ p }metabolic pathways.
Drawing algorithm
The algorithm we propose has two main steps: first, a multiscale clustering is performed creating a quotient graph (strictly speaking, the quotient graph is built by considering isolated nodes as singletons), and second, clusters and quotient graph are drawn using three drawing algorithms. In the next section, we first explain our clustering algorithm and then, we present the drawing algorithms we use.
Multiscale clustering
First pass : computation of an independent set of pathways
First of all, the algorithm searches for a subset P_{ ind }= {p_{1}, ..., p_{ ind }}, ind ≥ 1, P_{ ind }⊆ P_{ G }such that 1. the pathways of P_{ ind }are independent and 2. ${\sum}_{i=1}^{i=ind}\left{p}_{i}\right$ is maximized. For instance, in Figure 3a, {p_{1}, p_{3}} is the independent set that maximizes this sum among all possible independent sets of pathways ({p_{1}}, {p_{2}},{p_{3}}, {p_{4}}, {p_{5}}, {p_{1}, p_{3}}, {p_{1}, p_{4}}, {p_{1}, p_{5}}, {p_{2}, p_{4}} and {p_{4}, p_{5}}).
The problem of finding a maximum independent set is known to be NPHard [36]. This problem can be reduced to a coloration problem (the graph is then the dependence graph, where each pathway corresponds to a node and there is an edge between two nodes when the pathways share nodes in the original graph). To find a solution, we use the Welsh and Powel heuristic [37]. Then, for each color class C, ${\sum}_{{p}_{i}\in C}\left{p}_{i}\right$ is computed, and a maximum one is chosen as our independent set.
Let P_{ Nind }= P_{ G }\P_{ ind }. Then, for all the pathways in P_{ Nind }, we exclude nodes that are shared with at least one other pathway in P_{ G }. We denote this reduced set by ${{P}^{\prime}}_{Nind}$.
Each element of P_{ ind }and ${{P}^{\prime}}_{Nind}$ is a set of nodes. These sets define a clustering on the original graph since there is no overlapping between them. This clustering is used by replacing each subgraph induced by an element of P_{ ind }or ${{P}^{\prime}}_{Nind}$ by a metanode representing it (see Figure 3b). We call this first clustered graph G_{clust 1}.
For all the pathways in P_{ ind }and in ${{P}^{\prime}}_{Nind}$, we search for the longest independent mixed cycles (Cycles C_{1} and C_{2} are independent if C_{1} and C_{2} do not share any node). A mixed cycle is a sequence of nodes v_{1}, v_{2}, ..., v_{ l }, l ≥ 3 such that ∀ 1 <i ≤ l, (v_{i1}, v_{ i }) ∈ E' ∪ A and (v_{ l }, v_{1}) ∈ E' ∪ A.
Moreover, ∀ 1 <i <l, if v_{ i }represents a reaction and v_{i1}a substrate consumed in (resp. produced by) this reaction, then v_{i+1}is produced by (resp. consumed in) v_{ i }. This problem is also NPComplete even if A = ∅ [36]. To "solve" it, we use an exact maximum length cycle algorithm and bound the computation time with a threshold. If the threshold is reached, we stop the algorithm and consider that the longest mixed cycle we have already found is a longest one. This allows to have an exact result in the best case and an approximation of a longest mixed cycle otherwise. The technique computes all mixed paths using a mixed breadthfirst search (BFS). In Figure 3c, one can see the longest independent cycles of each element of P_{ ind }and ${{P}^{\prime}}_{Nind}$ highlighted in red. These cycles are clustered into metanodes yielding a multiscale graph called G_{clust 2}. For all the metabolic networks on which we tested our algorithm, the threshold was not reached (i.e. we found an exact solution).
Second pass : detection of cycles and paths
The next step of the algorithm consists in computing the longest independent mixed cycles in G_{clust 2}, excluding metanodes. At each iteration, we cluster a longest cycle into a metanode and exclude it for the next search. We then compute the longest mixed paths, i.e. the longest sequences of nodes of degree less or equal to two v_{1}, v_{2}, ..., v_{ l }, l ≥ 2, where ∀1 <i ≤ l, (v_{i1}, v_{ i }) ∈ E' ∪ A.
In figure 3d, one can see the two new metanodes, the left one is a path and the other one is a cycle. The result of this clustering is the quotient graph that will be the input of the drawing algorithm.
Drawing algorithm
To draw the metabolic network, we use three drawing algorithms: one for the quotient graph and two for the metanodes.
Drawing metanodes
Drawing the quotient graph
We want a drawing that optimizes the angular resolution and the number of bends to obtain a better visibility. The MixedModel algorithm of C. Gutwenger and P. Mutzel [39] is a tradeoff between all these aesthetic criteria. Moreover, drawings produced by this algorithm are similar to manually drawn metabolic networks.
To use the MixedModel algorithm, we need to make modifications on the quotient graph. Indeed, it can only be applied to planar graphs; therefore, we have to planarize (i.e. make it planar) the quotient graph. This problem is wellknown and is NPHard [40]. Many techniques exist that do it either by augmentation or by deletion of edges (or nodes). For a survey on this topic, one can refer to [41]. The drawback of an augmentation based technique is that it may add up to V^{4} nodes, thus the drawing becomes difficult to understand. That is why we use our own heuristic: vertices of higher degree are removed one by one until the graph becomes planar. All removed nodes are then reinserted. Removed edges are readded one by one as long as the graph is planar.
The reinsertion of edges for each node is done with no prior order, using a greedy approach. The edges that have been removed and not reinserted during the planarization step will be reinserted after the planar subgraph is drawn.
The obtained planar subgraph of the quotient graph is drawn by the MixedModel algorithm [39]. To summarize, this algorithm has two steps :

The first step builds an ordered partition of the set of nodes. This partition is called shelling ordering. The principle is to remove successively nodes that are on the external face of the graph.

The second one is the "recomposition" of the graph according to the shelling ordering. To guarantee that there is neither edgeedge crossing nor nodeedge overlapping, the ordering is traversed in reverse order.
As described in the background section, if a vertex is in a pathway, it has to be drawn close to the other vertices of the pathway. Taking into account such a constraint in the MixedModel algorithm can be done during the decomposition phase. Let SO = {V_{1}, V_{2}, ..., V_{ r }} be the shelling ordering. When a vertex n is added to a set V_{ i }, 1 ≤ i < r, we add in priority vertices which have a constraint with n into the next V_{ j }, j > i. Those nodes will be more likely to be drawn next to each other.
The last step of our drawing algorithm is to draw edges removed during the planarization step. These edges are routed on the external face, using an orthogonal drawing with three bends per edge. Figure 4 shows the drawing obtained by our algorithm on the metabolic network of E. coli. This is an organism which has been widely studied, its metabolism is composed of 198 pathways, 1140 substrates and reactions (i.e. nodes) and 1321 links (i.e. edges) between them.
Parameter: focus pathways
The algorithm allows to focus on several pathways, i.e. one can choose pathways to be entirely clustered. Users constrain the independent set algorithm by giving an ordered list of pathways that are clustered if possible. Indeed, such a list may not be represented by an independent set in the dependence graph (i.e. one or more nodes are shared by pathways of the list). In this case, the order of the list gives the priority associated to each pathway and helps to extract an independent set of pathways from the list. Nodes representing those pathways and their neighbors are removed from the dependence graph. An independent set is then computed in the resulting dependence graph. The final independent set is obtained by adding this independent set and those computed in the list.
Results
Data
To test and validate the algorithm, we used data from the version 10.0 of the EcoCyc database. We developed perl scripts using the pathway tools software [42–44] to obtain information on the reactions, compounds and metabolic pathways involved in the metabolism of the K12 strain of Escherichia coli. We chose this organism because it is perhaps the most curated one and we thus avoid most of the data artifacts caused by automatic reconstructions of metabolism.
Several filters are applied on the original data to build our test data. The first one is to withdraw reactions involving large molecules such as proteins. Next, we remove reactions that are involved in no identified metabolic pathway. The last filter has for objective to avoid ubiquitous compounds. Indeed, cofactors such as ATP and NADH participate in many reactions and form hubs in the network which lead to a very fuzzy drawing. One traditional way around this problem is to eliminate the most connected compounds but this implies that metabolic pathways that have these compounds as final products or as precursors become meaningless. We therefore prefer another solution which consists in eliminating the connection between a compound and a reaction if the compound is annotated in EcoCyc as "secondary" in each metabolic pathway that contains the reaction. A compound is defined as "primary" in a BioCyc metabolic pathway when it is a direct chemical intermediate between the start substrate(s) and the end product(s) and is defined as "secondary" when it is a subproduct or a secondary substrates (e.g cofactors) of the metabolic pathway.
It is important to note that this filter leads to a clearer drawing but any kind of compound filter could be applied. In the same way, the classification of the reactions in the EcoCycdefined metabolic pathways was an easy way to test our algorithm but other classifications could be used, for instance a decomposition into elementary modes [45] or extreme pathways [46]. A metabolic pathway, as defined in BioCyc, can be either a linear chain of reactions, a branched pathway, a cycle: this topological diversity is interesting for testing our drawing algorithm.
The data is stored in a SBML file [47] and computed by MetaViz. The information about the belonging of each reaction is directly included in the SMBL file as shown below in the entry of one reaction which belongs to three different metabolic pathways:
...
<reaction id="DIHYDROFOLATEREDUCT__45__RXN" name="DIHYDROFOLATEREDUCTRXN" reversible="true">
<notes>
<html:p>SUBSYSTEM: tetrahydrofolate biosynthesis</html:p>
<html:p>SUBSYSTEM: superpathway of chorismate</html:p>
<html:p>SUBSYSTEM: formylTHF biosynthesis I</html:p>
</notes>
<listOfReactants>
<speciesReference species="THF" stoichiometry="1"/>
</listOfReactants>
<listOfProducts>
<speciesReference species="DIHYDROFOLATE" stoichiometry="1"/>
</listOfProducts>
</reaction>
...
After the filtering, the SBML file contains :

553 compounds and 597 reactions (the nodes of the network represented in Metaviz)

198 metabolic pathways of which 30 are superpathways, i.e. pathways which contain other pathways.
Validation
The protocol we adopted for the validation is the following: we systematically compared the behavior of MetaViz to Cytoscape and to the Pathway Tools cellular overview diagram whenever possible. This comparison was carried out for the following tasks:

Visualization of the whole network;

Visualization of individual metabolic pathways;

Visualization of a metabolic pathway in its context.
Visualization of the whole network
Drawing of the TCA cycle
We do not compare the results with Cytoscape of which the purpose is not to draw metabolic pathways but only to draw a whole network.
In the data from BioCyc, the TCA cycle is included in the super pathway of "glycolysis, pyruvate dehydrogenase, TCA, and glyoxylate bypass". Because of its great number of nodes, this pathway was chosen by the algorithm to be particularly well drawn: all the nodes (compounds and reactions) involved in this super pathway are grouped together into a same metanode (Figure 7a). The drawing obtained by MetaViz is very similar to the one obtained by the pathway viewer of BioCyc (Figure 7c). The differences between the two drawings are mostly due to the differences in the types of graph used to model the network: a simple graph in the case of BioCyc, and a bipartite graph in the case of MetaViz.
Drawing of the valine biosynthesis pathway
This pathway is a fourstep chain which starts with pyruvate and ends with Lvaline.
Visualization of a metabolic pathway in its context
MetaViz represents explicitly the links between metabolic pathways. These links are ignored when metabolic pathways are separately drawn (as in BioCyc) or when no information about the belonging of the nodes to a metabolic pathway is displayed (as in Cytoscape). The Pathway Tools Cellular Overview diagram proposes to optionally draw these links in superposition to the main drawing. The limit of this approach is that, since these links are not incorporated in the original layout, the final drawing may become very dense and hard to read.
Conclusion
In this paper, we present an algorithm to compute the representation of a metabolic network. This method addresses a challenging problem which consists in representing simultaneously the topology and the metabolic pathway information. Indeed, metabolic pathways often share metabolites and reactions, thus to represent them in a single view, previous approaches duplicated these shared elements. However, duplication produces drawings where the depicted connectivity does not fit the real topology of the network. To overcome the problem of shared nodes, we propose a clustering step based both on topology and a metabolic pathway decomposition. During this step, we deal with pathway overlapping by detecting a largest set of independent pathways and subpathways. The resulting graph clustering shows the overall organization of the pathways. To follow common drawing conventions, it is drawn using a planar graph drawing algorithm. Finally, each pathway or subpathway is drawn using specific drawing algorithms (hierarchical and circular ones). In our collaboration with physiologists, we noticed that they often consider some pathways as being central in their global studies. To respect their habits, the physiologists can provide a set of focus pathways that will be considered as a parameter of the clustering step. Thus our algorithm will generate a drawing where these pathways are entirely and carefully drawn.
This global representation allows the visualization of processes that span over different metabolic pathways. For instance, this approach was successfully used to highlight metabolic processes, especially those traversing different metabolic pathways.
One of the future directions we would like to consider concerns the improvement of the global aspect of our drawing. The drawing conventions that we identified for metabolism are mostly local (emphasizing cycles and reaction cascades). Following them does not ensure to have a global picture that will look like the Boehringer map [23] which may be closer to what biochemists are used to. Indeed, the global picture that we obtain with our method can be puzzling at first glance, and it is only when navigating in the drawing that the user will find more familiar patterns. We believe that we can improve the aspect of the global drawing in considering alternative ways of drawing the quotient graph.
In this paper, we focused on the drawing part of metabolic network visualization. As it was mentioned, drawings are used as a background for high throughput data visualization. Since this algorithm is already implemented in a graph drawing software [38], we plan to develop an input module for omic data. Another issue will be to add more relational information such as signaling processes. We plan to use the third dimension to incorporate the additional edges.
Availability and requirements
Project name: MetaViz
Project home page: http://www.labri.fr/perso/bourqui/software.php
Operating system(s): Currently Linux and Windows. Mac OSX ports is possible.
Programming language: C++
Other requirements: Tulip [38], Qt from Trolltech.
License: GPL
Declarations
Acknowledgements
The work presented in this paper was funded in part by the ACI Nouvelles Interfaces des Mathématiques (project pvert) of the French Ministry of Research, by the ARC (project IBN) from the INRIA and by the ANR (project REGLIS).
Authors’ Affiliations
References
 Karp PD, Paley SM: Automated Drawing of Metabolic Pathways. Third International Conference on Bioinformatics and Genome Research. 1994Google Scholar
 Salamonsen , Yee , Mok , Kolatkar : BioJAKE: a tool for the creation, visualization and manipulation of metabolic pathways. Pacific Symposium on Biocomputing. 1999, 4: 392400.Google Scholar
 Kanehisa M: Postgenome Informatics. 2000, Oxford University PressGoogle Scholar
 Becker M, Rojas I: A Graph Layout Algorithm for Drawing Metabolic Pathways. Bioinformatics. 2001, 17: 461467. 10.1093/bioinformatics/17.5.461PubMedView ArticleGoogle Scholar
 Seo J, Shneiderman B: Interactively Exploring Hierarchical Clustering Results. IEEE Computer. 2002, 35 (7): 8086.View ArticleGoogle Scholar
 Schreiber F: Comparison of metabolic pathways using constraint graph drawing. APBC 03: Proceedings of the First AsiaPacific bioinformatics conference on Bioinformatics. 2003, 105110. Australian Computer Society, IncGoogle Scholar
 Jourdan F, Melançon G: A Tool for Metabolic and Regulatory Pathways Visual Analysis. Visualization and Data Analysis, VDA. 2003, 4655. Santa Clara Convention Center: SPIE, Http://vw.indiana.edu/vda2003/Google Scholar
 Dogrusoz , Giral , Cetintas , Civril , Demir : A Compound Graph Layout Algorithm for Biological Pathways. GD 2004. Edited by: LNCS. 2004, 3383: 442447.Google Scholar
 Brandes U, Dwyer T, Schreiber F: Visualizing Related Metabolic Pathways in Two and Half Dimensions. LNCS. 2004, 2912: 11122.Google Scholar
 Wegner , Kummer : A new dynamical layout algoritmh for complex biochemical reaction networks. BMC Bioinformatics. 2005, 6: 212 10.1186/147121056212PubMed CentralPubMedView ArticleGoogle Scholar
 Gabouje SD, Zimányi : A New Compound Graph Layout Algorithm for Visualizing Biochemical Networks. Poster Proceedings Volume of the 4th International Workshop on Efficient and Experimental Algorithms, WEA 05.Google Scholar
 Dogrusoz , Erson , Giral , Demir , Babur , Cetintas , Colak : PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization. Bioinformatics. 2005, 22 (3): 374375. 10.1093/bioinformatics/bti776PubMedView ArticleGoogle Scholar
 Tope J, Gillespie , Vastrik , DEustachio , Schmidt , de Bono , Jassal , Gopinath , Wu , Matthews , Lewis , Birney , Stein : Reactome: a knowledgebase of biological pathways. Nucleic Acids Research. 2005, 33: D428D432. 10.1093/nar/gki072View ArticleGoogle Scholar
 Paley S, Karp P: The Pathway Tools cellular overview diagram and Omics Viewer. Nucleic Acids Research. 2006, 34 (13): 37713778. 10.1093/nar/gkl334PubMed CentralPubMedView ArticleGoogle Scholar
 Junker BH, Klukas C, Schreiber F: VANTED: A System for Advanced Data Analysis and Visualization in the Context of Biological Networks. BMC Bioinformatics. 2006, 7: 109EPub 10.1186/147121057109PubMed CentralPubMedView ArticleGoogle Scholar
 Nikiforova V, Kopka J, Tolstikov V, Fiehn O, Hopkins L, Hawkesford M, Hesse H, Hoefgen R: Systems Rebalancing of Metabolism in Response to Sulfur Deprivation, as Revealed by Metabolome Analysis of Arabidopsis Plants. Plant Physiology. 2005, 138: 304318. 10.1104/pp.104.053793PubMed CentralPubMedView ArticleGoogle Scholar
 Lacroix V, Fernandes CG, Sagot MF: Motif search in graphs: application to metabolic networks. IEEE/ACM Trans Comput Biol Bioinform. 2006, 3 (4): 360368. 10.1109/TCBB.2006.55PubMedView ArticleGoogle Scholar
 Saraiya P, North C, Duca K: Visualizing biological pathways: requirements analysis, systems evaluation and research agenda. Information Visualization. 2005, 4: 115. 10.1057/palgrave.ivs.9500102.View ArticleGoogle Scholar
 Jeong H, Tombor B, Albert R, Oltvai Z, Barabasi A: The LargeScale Organization of Metabolic Networks. Nature. 2000, 407: 651 10.1038/35036627PubMedView ArticleGoogle Scholar
 Pfeiffer T, Dandekar T, Moldenhauer F, Schuster S: Topological analysis of metabolic networks. Application to the metabolism of Mycoplasma pneumoniae. BTK2000: Animating the Cellular Map. 2000, 6: 229234.Google Scholar
 Salway JG: Metabolism at a Glance. 2003, Blackwell Science LtdGoogle Scholar
 Michal G: On representation of metabolic pathways. BioSystems. 1998, 47: 17. 10.1016/S03032647(98)000197PubMedView ArticleGoogle Scholar
 Michal G: Biochemical Pathways (Poster). 1993, Boehringer MannheimGoogle Scholar
 BIOCARTA: Charting pathways of life. http://www.biocarta.com
 Romero P, Wagg J, Green M, Kaiser D, Krummenacker M, Karp P: Computational prediction of human metabolic pathways from the complete human genome. Genome Biology. 2004, 117.Google Scholar
 Purchase H, Cohen RF, James M: An Experimental Study of the Basis for Graph Drawing Algorithms. ACM Journal of Experimental Algorithmics. 1997, 2 (4): 189Google Scholar
 Battista GD, Eades P, Tamassia R, Tollis IG: Graph Drawing: Algorithms for the Visualization of Graphs. 1999, Prentice HallGoogle Scholar
 Kaufmann M, Wagner D: Drawing Graphs Springer. 2001View ArticleGoogle Scholar
 Sugiyama , Misue : Visualisation of structural information : Automatic drawing of compound digraphs. IEEE Transactions on Systems, Man, and Cybernetics. 1991, 21 (4): 876892. 10.1109/21.108304.View ArticleGoogle Scholar
 Brandenburg F, Forster M, Pick A, Raitner M, Schreiber F: Biopath. GD'01. Edited by: Verlag S. 2002Google Scholar
 Eades : A heuristic for graph drawing. Congressus Numerantium. 1984, 42: 149160.Google Scholar
 Frick , Ludwig , Mehldau : A fast adaptive layout algorithm for undirected graphs. Lecture Notes in Computer Science. 1994, 894: 388403.View ArticleGoogle Scholar
 Shannon P, Markiel A, Ozierand O, Baliga N, Wang J, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research. 2003, 13: 24982504. 10.1101/gr.1239303PubMed CentralPubMedView ArticleGoogle Scholar
 SBML viewer. http://sbw.kgi.edu/layout/
 van Helden J, Wernisch L, Gilbert D, Wodak S: Graphbased analysis of metabolic networks. Ernst Schering Research Foundation Workshop. 2002, 38: 245274.PubMedGoogle Scholar
 Garey MR, Johnson DS: Computers and Intractability: A Guide to the Theory of NPCompleteness. 1979, New York, NY, USA: W. H. Freeman & CoGoogle Scholar
 Welsh , Powell : An upper Bound to the chromatic number of a graph and its application to timetabling problems. The Computer journal. 1967, 10: 8586. 10.1093/comjnl/10.1.85.View ArticleGoogle Scholar
 Auber D: Graph Drawing Software. SpringerVerlag 2003 chap. Tulip A Huge Graph Visualization FrameworkGoogle Scholar
 Gutwenger C, Mutzel P: Planar Polyline Drawings with Good Angular Resolution. Graph Drawing '98 (Proc.). 1998, 1547: 167182. SpringerVerlag, Lecture Notes in Computer ScienceView ArticleGoogle Scholar
 Lui P, Geldmacher R: On the deletion of nonplanar edges of a graph. Proceeding on the 10th conf. on Comb., Graph Theory, and Comp. 1977, 727738.Google Scholar
 Liebers A: Planarizing Graphs – A Survey and Annotated Bibliography. Journal of Graph Algorithms and Applications. 2001, 5: 174.View ArticleGoogle Scholar
 Karp P, Riley M, Saier M, Paulsen I: The EcoCyc and MetaCyc databases. Nucleic Acids Research. 2000, 28: 5659. 10.1093/nar/28.1.56PubMed CentralPubMedView ArticleGoogle Scholar
 Karp PD, Paley S, Romero P: The Pathway Tools software. Bioinformatics. 2002, 18 (Suppl 1): S22532.PubMedView ArticleGoogle Scholar
 Krummenacker M, Paley S, Mueller L, Yan T, Karp PD: Querying and computing with BioCyc databases. Bioinformatics. 2005, 21 (16): 34543455. 10.1093/bioinformatics/bti546PubMed CentralPubMedView ArticleGoogle Scholar
 Schuster S, Hilgetag C, Woods JH, Fell DA: Reaction routes in biochemical reaction systems: algebraic properties, validated calculation procedure and example from nucleotide metabolism. J Math Biol. 2002, 45 (2): 153181. 10.1007/s002850200143PubMedView ArticleGoogle Scholar
 Schilling CH, Letscher D, Palsson BO: Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathwayoriented perspective. J Theor Biol. 2000, 203 (3): 229248. 10.1006/jtbi.2000.1073PubMedView ArticleGoogle Scholar
 Finney AHM: Systems biology markup language: Level 2 and beyond. Biochem Soc Trans. 2003, 14723.Google Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.