Genome Informatics 13: 351–352 (2002)
351
Generalized Representation of Metabolic and
Regulatory Pathways
Min Kyung Kim
Hyun Seok Park
minkykim@sejong.ac.kr
hsp@sejong.ac.kr
Institute of Bioinformatics, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul
143-747, Korea
Keywords: visualization, pathway, interaction
1
Introduction
To enhance the exploration of protein function in a pathway context, one requires an application that
allows the visualization of the protein in a pathway map. Broadly speaking, protein interactions fall
into two functional categories; metatbolic pathway and regulatory pathway. The inherent difference of
two pathway features leads to the design of various data presentation to visualize pathway information.
Metabolic pathways are commonly represented by a graph-like structure in which node represent
reactants, products or EC numbers and edges represent reactions. However, the regulatory pathway
has neither a widely accepted taxonomy of biological functions as the EC classification system nor
a fully understand mechanism like metabolic pathway [2]. But this partial understanding is very
artificial. Cells respond in this interconnected fashion, involving several pathways. Therefore, if we
want to simulate the cell’s responses, we need integrated information and generalized representation.
In this poster, we will describe several issues the mapping convention that governs both the metabolic
and regulatory pathway.
2
2.1
Method and Results
Rules of Node, Edge and Node on the Edge
Metabolic and regulatory pathways are represented as graph with node, edge and ‘node on the edge’.
A major consideration in the design of the diagram conventions was to generalize all the known
interactions. For this purpose, we added the third type of symbols except nodes and edges: ‘node on
the edge’. Primarily, ‘nodes represent edges’ performer whereas ‘node on the edge’ inform additional
facts. The meaning of each case is summarized in Table 1.
Table 1: The type of nodes and edges.
Node
Edge
Node on the edge
Metabolic
Pathway
enzyme
reaction
reactant
substrate
product
Regulatory
Pathway
protein/DNA/RNA
interaction
type of interaction
bind(activation/inhibition)
release
modify
statechange
translocate
default(unknown)
352
2.2
Kim and Park
Graphical Representation of Pathways
For example, by applying the general rules
as summarized in Table 1, we can visualize the
pathways for glycolysis (metabolic) and apoptosis (regulatory) pathway as in Figure 1. Although, visualizing regulatory pathways focus on
binary interactions in many approaches of others,
a flow representation should be considered. EC
numbers are usually used in visualizing metabolic
pathways. But this approach has several serious
drawbacks: First, in many cases organisms have
more than one enzyme with the same EC number
[3]. Secondly, the nomenclature of the functional
category of enzymes in the primary literature is
Figure 1: Graphical representation of pathways.
rarely associated. Thirdly, proteins, which have
not an enzymatic function, are not assigned EC numbers. Therefore, another kind of concept was
needed and GO(Gene Ontology) might be one of the choice [1] Our approach is base on using the
enzyme name itself rather than EC numbers. For small molecules such as ATP, NADH, and NADPH
are involved in both pathways, additional symbols might be needed. This issue is a designe problem:
descriptive or instinctive.
3
Discussion
We discussed a couple of important design issues generalized representation of metabolic and
regulatory pathways. For this purpose, we need
more generalized concept and description, which
is possible to store full descriptions of interactions. In this paper, we proposed general rules
that govern both pathways: (1) the use of enzyme name instead of EC number. (2) the use
of ‘node on the edge’ for the reactant and the
additional information. For future versions, the
Figure 2: System overview of BIOPATH.
concept of quality(for example, time, condition,
and etc.) and quantity(for example, flux, kinetics, signaling cascade, oligomerization and etc.) will
be needed for simulation. We are currently doing a BIOPATH project, to develop an integrated
environment for analyzing pathway information. Figure 2 shows our on-going project [4].
References
[1] Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P.,
Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A.,
Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., and Sherlock, G., Gene
ontology: Tool for the unification of biology, Nat Genet, 25:25–29, 2000.
[2] Fukuda, K. and Takagi, T., Knowledge representation of signal transduction pathways, Bioinformatics, 17:829–837, 2001.
[3] Schomburg, I., Chang, A., and Schomburg, D., BRENDA, enzyme data and metabolic information, Nucleic Acids Res., 30:47–49, 1999.
[4] http://www.biopathway.or.kr/