MetNet: Software to Build and Model the Biogenetic Lattice of
 Arabidopsis

doi:10.1002/cfg.285

Comp Funct Genomics. 2003 April; 4(2): 239–245.

doi: 10.1002/cfg.285.

PMCID: PMC2447407

MetNet: Software to Build and Model the Biogenetic Lattice of Arabidopsis

Eve Syrkin Wurtele,¹ Jie Li,^1,² Lixia Diao,^1,³ Hailong Zhang,² Carol M. Foster,¹ Beth Fatland,¹ Julie Dickerson,^2,⁴ Andrew Brown,⁴ Zach Cox,⁴ Dianne Cook,³ Eun-Kyung Lee,³ and Heike Hofmann³

¹ Department of Genetics, Cellular and Developmental Biology, Iowa State University, Ames, IA, 50011, USA,

² Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, 50011, USA,

³ Department of Statistics, Iowa State University, Ames, IA, 50011, USA,

⁴ Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, 50011, USA,

Eve Syrkin Wurtele, Email: mash/at/iastate.edu.

Corresponding author.

Received February 4, 2003; Revised February 7, 2003; Accepted February 10, 2003.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article has been cited by other articles in PMC.

Abstract

MetNet (http://www.botany.iastate.edu/~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.

Full Text

The Full Text of this article is available as a PDF (397K).

Selected References

These references are in PubMed. This may not be the complete list of references from this article.

Brown, MP; Grundy, WN; Lin, D; Cristianini, N; Sugnet, CW; Furey, TS; Ares, M, Jr; Haussler, D. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A. 2000 Jan 4;97(1):262–267. [PubMed]
Ding, J; Berleant, D; Nettleton, D; Wurtele, E. Mining MEDLINE: abstracts, sentences, or phrases? Pac Symp Biocomput. 2002:326–337. [PubMed]
Dougherty, Edward R; Barrera, Junior; Brun, Marcel; Kim, Seungchan; Cesar, Roberto M; Chen, Yidong; Bittner, Michael; Trent, Jeffrey M. Inference from clustering with application to gene-expression microarrays. J Comput Biol. 2002;9(1):105–126. [PubMed]
Fatland, Beth L; Ke, Jinshan; Anderson, Marc D; Mentzen, Wieslawa I; Cui, Li Wei; Allred, C Christy; Johnston, Jerry L; Nikolau, Basil J; Wurtele, Eve Syrkin. Molecular characterization of a heteromeric ATP-citrate lyase that generates cytosolic acetyl-coenzyme A in Arabidopsis. Plant Physiol. 2002 Oct;130(2):740–756. [PubMed]
Kanehisa, M; Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000 Jan 1;28(1):27–30. [PubMed]
Karp, Peter D; Riley, Monica; Paley, Suzanne M; Pellegrini-Toole, Alida. The MetaCyc Database. Nucleic Acids Res. 2002 Jan 1;30(1):59–61. [PubMed]
Karp, Peter D; Riley, Monica; Saier, Milton; Paulsen, Ian T; Collado-Vides, Julio; Paley, Suzanne M; Pellegrini-Toole, Alida; Bonavides, César; Gama-Castro, Socorro. The EcoCyc Database. Nucleic Acids Res. 2002 Jan 1;30(1):56–58. [PubMed]
Oliver, David J; Nikolau, Basil; Wurtele, Eve Syrkin. Functional genomics: high-throughput mRNA, protein, and metabolite analyses. Metab Eng. 2002 Jan;4(1):98–106. [PubMed]
Overbeek, R; Larsen, N; Pusch, GD; D'Souza, M; Selkov, E, Jr; Kyrpides, N; Fonstein, M; Maltsev, N; Selkov, E. WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction. Nucleic Acids Res. 2000 Jan 1;28(1):123–125. [PubMed]
Tomita, M. Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol. 2001 Jun;19(6):205–210. [PubMed]
Yao, Toru. Bioinformatics for the genomic sciences and towards systems biology. Japanese activities in the post-genome era. Prog Biophys Mol Biol. 2002 80(1-2):23–42.Jul–Aug; [PubMed]