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Genome Informatics 14: 372–373 (2003)
Crosstalk between Metabolic and Regulatory Pathways Min Kyung Kim1
Hyun Seok Park2
[email protected]
[email protected]
Seong Joon Yoo3
[email protected] 1 2 3
Center of Engineering Research, Ewha Womans University, Seoul, Korea Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea School of Computer Engineering, Sejong University, Seoul, Korea
Keywords: pathway, database, crosstalk, network, BiopathDB
1
Introduction
To enhance the understanding of cells’ response in a specific condition, one requires information that shows the interconnected view of biological process. Broadly speaking, biological pathways fall into two functional categories: metabolic and regulatory pathways. Existing pathway databases are categorizing specific pathway types according to their own definition about their own subject. They contain protein and/or interaction data through clickable image pathway maps. Currently, the crosstalk among different pathways is restricted in metabolic pathways. Metabolic pathway information has a hierarchical structure as we can see in KEGG [3]. In the finest resolution, entities are compounds and reactions between them. In the upper level, a pathway itself will be a node and the interconnected nature of cells’ metabolism is represented as edges. Therefore, metabolisms are represented as many layers of different elements in their hierarchy. However, the available information of the regulatory pathway has neither a hierarchical concept nor a whole proteome scale view of crosstalk between the pathways. As a result, we have trouble to understand interconnection between metabolic and regulatory pathways because of the inherent difference of two kinds of pathways. For this reason, we integrated different kinds of pathway database: KEGG/BIND and protein information supplement database SWISS-PROT [1, 2]. In this paper, we describe several issues for database construction and crosstalk between different kinds of pathways. Because these kinds of pathways are interconnected for the same stimuli, the integrated understanding of all kinds of pathway will be a framework for the systems biology.
2 2.1
Method and Results BiopathDB: a Datawarehouse for Biological Pathway Integration
We parsed the raw data from heterogeneous data sources such as KEGG, BIND, and SWISS-PROT and convert them according to BiopathDB ER data model (Figure 1). Pathways are usually represented as a graph consisting of nodes and edges. BiopathDB contains several distinctive features from existing pathway database. First, any molecule will be nodes; DNA, RNA, protein, compound and complex are representative elements. Second, any relations will be edges such as interaction, reaction, catalysis and expression. A special feature of relations in BiopathDB is that the objects have not only nodes, but also edges. For example, catalysis is a relationship between protein(node) and reaction(edge). Third, a set of relationship is used for
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Crosstalk between Metabolic and Regulatory Pathways description of pathways. Metabolic pathways are represented as a series of reaction. Reaction is a kind of interaction mediated by catalytic enzymes(proteins) which sometimes participate in regulatory pathways. Regulatory pathways are represented as a set of binary interactions between the entities. Fourth, The interconnected pathways are described as a pathway network in BiopathDB. This generality makes it possible to represent all kinds of pathway in a single schema and find the crosstalk between pathways [4].
2.2 Interconnected Pathways between Metabolic and Regulatory Pathway Figure 1: $
From the integration of pathway database, we find two kinds of interconnected pathways: one is Raf protein and the other is PI3 kinase p110 alpha chain involved network. For example, Raf takes part in several kinds of metabolic pathway such as spingolipid, inositol phosphate, starch/sucrose, nicotinate/nicotinamide metabolism, benzoate degradation pathway and EGF receptor signaling pathway.
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The data model of BiopathDB. The arrow shows the relationship between nodes or a node and an edge. Proteins involved in metabolic and regulatory patyway could be represented by different kinds of interactions.
Discussion
High throughput experiment to detect protein-protein interaction is applied to yeast, worm and mouse. From these kinds of efforts, we know that the high connectivity of interaction has resulted in large cluster of protein-protein interaction network. Can pathway network be consistent with proteinprotein interaction network? All known pathways are accepted as preferred paths among all kinds of alternative paths or oversimplication of protein interaction network [5]. To answer the above question, we need more regulatory pathway data described as a set of interactions. BiopathDB contains limited information of regulatory pathway data as compared with interaction data. The current available systems of nomenclature for each pathway and definition for the elements are involved in each pathway remain divergent even if the biologists appreciate. For this reason, MINT database do not plan to predefine pathways (for instance the ‘EGF pathway’) [6]. How can we recognize biological paths in a specific condition among all of the alternatives? BiopathDB data model, which offer an interconnected view of interaction and pathway, could contribute to answer this question.
References [1] Bader, G.D., Betel, D., and Hogue, C.W., BIND: the biomolecular interaction network database, Nucleic Acids Res., 31:248–250, 2003. [2] Boeckmann, B., Bairoch, A., Apweiler, R., Blatter MC., Estreicher, A., Gasteiger, E., Martin, M.J., Michoud, K., O’Donovan, C., Phan, I., Pilbout, S., and Schneider, M., The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003, Nucleic Acids Res., 31:365–370, 2003. [3] Kanehisa, M., Goto, S., Kawashima, S., and Nakaya, A., The KEGG databases at GenomeNet, Nucleic Acids Res., 30:42–46. 2002. [4] Kim, M.K. and Parkm, H.S., Generalized representation of metabolic and regulatory pathways, Genome Informatics, 13:351–352, 2002. [5] Marcotte, E.M., The path not taken, Nature Biotech., 19:626–627, 2001. [6] Zanzoni, A., Montecchi-Palazzi, L., Quondam, M., Ausiello, G., Helmer-Citterich, M., and Cesareni, G., MINT: a molecular interaction database, FEBS Lett., 513:135–140. 2002.
