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CADLIVE for Constructing a Large-scale Biochemical Network Based on a Simulation-directed Notation and Its Application to Yeast Cell Cycle

Overview
Specialty Biochemistry
Date 2003 Jul 11
PMID 12853624
Citations 30
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Abstract

The further understanding of the mechanisms of gene regulatory networks requires comprehensive tools for both the representation of complicated signal transduction pathways and the in silico identification of genomic signals that govern the regulation of gene expression. Consequently, sophisticated notation must be developed to represent the signal transduction pathways in a form that can be readily processed by both computers and humans. We propose the regulator-reaction equations combined with detailed attributes including the associated cellular component, molecular function, and biological process and present the simulation-directed graphical notation that is derived from modification of Kohn's method. We have developed the software suite, CADLIVE (Computer-Aided Design of LIVing systEms), which features a graphical user interface (GUI) to edit large-scale maps of complicated signal transduction pathways using a conventional XML-based representation. The regulator-reaction equations represent not only mechanistic reactions, but also semantic models containing ambiguous and incomplete processes. In order to demonstrate the feasibility of CADLIVE, we constructed a detailed map of the budding yeast cell cycle, which consists of 184 molecules and 152 reactions, in a really compact space. CADLIVE enables one to look at the whole view of a large-scale map, to integrate postgenomic data into the map, and to computationally simulate the signal transduction pathways, which greatly facilitates exploring novel or unexpected interactions.

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References
1.
Abbott A . Alliance of US labs plans to build map of cell signalling pathways. Nature. 1999; 402(6759):219-20. DOI: 10.1038/46111. View

2.
Hwang L, Lau L, Smith D, Mistrot C, Hardwick K, Hwang E . Budding yeast Cdc20: a target of the spindle checkpoint. Science. 1998; 279(5353):1041-4. DOI: 10.1126/science.279.5353.1041. View

3.
Burke D . Complexity in the spindle checkpoint. Curr Opin Genet Dev. 2000; 10(1):26-31. DOI: 10.1016/s0959-437x(99)00040-4. View

4.
Pirson I, Fortemaison N, Jacobs C, Dremier S, Dumont J, Maenhaut C . The visual display of regulatory information and networks. Trends Cell Biol. 2000; 10(10):404-8. DOI: 10.1016/s0962-8924(00)01817-1. View

5.
Wingender E, Chen X, Fricke E, Geffers R, Hehl R, Liebich I . The TRANSFAC system on gene expression regulation. Nucleic Acids Res. 2000; 29(1):281-3. PMC: 29801. DOI: 10.1093/nar/29.1.281. View