MetNet: Software to Build and Model the Biogenetic Lattice of Arabidopsis.
MetNet (http://www.botany.iastate.edu/ approximately mash/metnetex/metabolicnetex.html) is publicly available software in development for analysis of genome-wide RNA, protein and metabolite profiling data. The software is designed to enable the biologist to visualize, statistically analyse and model a metabolic and regulatory network map of Arabidopsis, combined with gene expression profiling data. It contains a JAVA interface to an interactions database (MetNetDB) containing information on regulatory and metabolic interactions derived from a combination of web databases (TAIR, KEGG, BRENDA) and input from biologists in their area of expertise. FCModeler captures input from MetNetDB in a graphical form. Sub-networks can be identified and interpreted using simple fuzzy cognitive maps. FCModeler is intended to develop and evaluate hypotheses, and provide a modelling framework for assessing the large amounts of data captured by high-throughput gene expression experiments. FCModeler and MetNetDB are currently being extended to three-dimensional virtual reality display. The MetNet map, together with gene expression data, can be viewed using multivariate graphics tools in GGobi linked with the data analytic tools in R. Users can highlight different parts of the metabolic network and see the relevant expression data highlighted in other data plots. Multi-dimensional expression data can be rotated through different dimensions. Statistical analysis can be computed alongside the visual. MetNet is designed to provide a framework for the formulation of testable hypotheses regarding the function of specific genes, and in the long term provide the basis for identification of metabolic and regulatory networks that control plant composition and development.
Wurtele
Eve Syrkin
ES
Department of Genetics Cellular and Developmental Biology Iowa State University Ames IA 50011 USA.
Li
Jie
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Diao
Lixia
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Zhang
Hailong
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Foster
Carol M
CM
Fatland
Beth
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Dickerson
Julie
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Brown
Andrew
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Cox
Zach
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Cook
Dianne
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Lee
Eun-Kyung
EK
Hofmann
Heike
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