Showing papers on "Interaction network published in 2000"

BIND--a data specification for storing and describing biomolecular interactions, molecular complexes and pathways.

Abstract: Motivation: Proteomics is gearing up towards highthroughput methods for identifying and characterizing all of the proteins, protein domains and protein interactions in a cell and will eventually create more recorded biological information than the Human Genome Project. Each protein expressed in a cell can interact with various other proteins and molecules in the course of its function. A standard data specification is required that can describe and store this information in all its detail and allow efficient cross-platform transfer of data. A complete specification must be the basis for any database or tool for managing and analysing this information. Results:We have defined a complete data specification in ASN.1 that can describe information about biomolecular interactions, complexes and pathways. Our group is using this data specification in our database, the Biomolecular Interaction Network Database (BIND). An interaction record is based on the interaction between two objects. An object can be a protein, DNA, RNA, ligand, molecular complex or an interaction. Interaction description encompasses cellular location, experimental conditions used to observe the interaction, conserved sequence, molecular location, chemical action, kinetics, thermodynamics, and chemical state. Molecular complexes are defined as collections of more than two interactions that form a complex, with extra descriptive information such as complex topology. Pathways are defined as collections of more than two interactions that form a pathway, with additional descriptive information such as cell cycle stage. A request for proposal of a human readable flat-file format that mirrors the BIND data specification is also tendered for interested parties.

Sequence comparison to graph comparison—a new generation of algorithms for network analysis of interacting molecules (abstract only)

Abstract: The sequence comparison has been the most fundamental method for understanding molecular functions encoded in the sequence data of proteins and nucleic acids. In general, however, the biological function results from an ordered network of interacting molecules in the cell; it cannot be attributed to just a single protein or a single gene. With the availability of complete genome sequences, it is now necessary to analyze multiple proteins or multiple genes at a time in order to understand higher level cellular functions, such as metabolism, signal transduction, cell cycle, apoptosis, and development.We have been computerizing current knowledge on cellular processes in KEGG (http://www.genome.ad.jp/kegg/) in terms of the network of interacting molecules. For simplicity of correlating with the genomic information, we abstract the interactions at the level of gene products- mostly proteins but including RNAs. Thus, the `generalized' protein-protein interactions include direct interactions, such as binding and phosphorylation in the signal transduction pathway, enzyme-enzyme relations in two successive reaction steps in the metabolic pathway, and the relations of transcription factors and transcribed gene products in the gene regulatory network. The latter two type of interactions are termed `indirect' protein-protein interactions.The generalized protein-protein interaction network is represented as a graph with gene products as nodes and interactions as edges. The genome is also represented as a graph where genes are nodes and they are considered to be linked one-dimensionally. Then, the functional prediction (reconstruction) from the genome is a process of mapping gene nodes in the genome to gene product nodes in the interaction network, which is a conversion of one graph to another graph. To help this mapping or conversion, additional graphs can also be considered, such clusters of coregulated genes by gene expression profile experiments or groups of orthologous and paralogous genes by sequence similarity searches. I will discuss graph comparison and path computation algorithms and their practical applications in functional reconstruction problems.