CADLIVE for constructing a yeast cell cycle networkHiroyuki Kurata1
firstname.lastname@example.org, Kyushu Institute of Technology
With the increase in the number of gene regulatory reactions, there is a great need for comprehensive tools for the in silico identification of genomic signals that govern gene regulation events and the representation of the detailed maps for signal transduction pathways. Thus, sophisticated notation must be proposed to structurally represent a large-scale map of gene regulatory networks, requiring two features: one is the diagram-based representation that describes the complicated networks that can be readily understood by humans, and the other text-based representation that can be automatically processed by computers.
We developed the software suit, CADLIVE (Computer-Aided Design of a LIVing systEm), which has been renamed from BIOCAD, with GUI for editing a large-scale map of complicated signal transduction pathways based on modified Kohn's notation, and demonstrated that the regulator-reaction equations with various attribute tags using an XML-based common representation can be readily employed by computers and humans. The notation is able to represent not only known interactions, but also ambiguous reactions in the form of diagram and database.
In order to demonstrate the feasibility of CADLIVE, we constructed a biochemical map of the budding yeast cell cycle, which consists of 184 molecules and 152 reactions, and mapped experimental data regarding DNA microarray and proteomics on the cell cycle network. CADLIVE enhanced the design of the yeast cell cycle greatly, and enabled computers to simulate the signal transduction pathways. CADLIVE is able to draw a detailed network map in really compact space, which enables us to look at the whole view of a large-scale map and to integrate postgenomic data into it. This is a great advantage to find novel or unexpected interactions among the components that are located at distant processes. In the postgenomic era, biological predictions would be facilitated by exploring the interaction among the components that are located at distant processes on a map rather than by focusing on local signal transduction pathways intensively.