Carol M. Foster

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

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.

Nodulation response of woody Papilionoid species after inoculation with rhizobia and soil from Hawaii, Asia and North America

Among subfamilies in the Fabaceae, the capacity to form root nodules is most common in the Papilionoideae. Yet nodules have never been observed on species of Cladrastis, and there are conflicting reports of the capacity of species in the genus Styphnolobium to nodulate. Our objectives were to evaluate Styphnolobium japonicum (formerly Sophora japonica) and Cladrastis kentukea for the capacity to nodulate and to characterize any isolated rhizobia. N-deficient plants were inoculated with rhizobia chosen for their low host specificity or for their symbiotic potential with indigenous and introduced trees and shrubs of Sophora species in Hawaii, Japan and China. Soil samples from the root zones of mature S. japonicum, C. kentukea and other woody legumes, introduced or indigenous to Hawaii, Japan, China and the continental USA, also were used as inocula. Inoculation did not elicit nodulation of C. kentukea or S. japonicum, despite that N concentrations of shoots of S. japonicum (1.6%) and C. kentukea (1.5%) fell below the highest shoot N percentage that previously was associated with well-nodulated plants of Maackia amurensis (1.8%). In addition to these analyses, rhizobia were isolated from nodules on the roots of a tree reported to us as S. japonicum. Nine of the 10 isolates selected as representatives of similarity groups were capable of nodulating M. amurensis, which led to the identification of the putative S. japonicum as Maackia floribunda. We also found that broad-range Bradyrhizobium USDA 6, USDA 3384 and USDA 3456 induce nodules on R. pseudoacacia and M. amurensis, which were used as control species during inoculation trials with S. japonicum and C. kentukea. Our conclusion that S. japonicum and C. kentukea lack the capacity to nodulate is based on the most thorough analysis of the nodulation capacity of these species to date. Previous reports of nodulation of S. japonicum may have been due to inaccurate plant or nodule identification.

Creating, Modeling, and Visualizing Metabolic Networks

Metabolic networks combine metabolism and regulation. These complex networks are difficult to understand and create due to the diverse types of information that need to be represented. This chapter describes a suite of interlinked tools for developing, displaying, and modeling metabolic networks. The metabolic network interactions database, MetNetDB, contains information on regulatory and metabolic interactions derived from a combination of web databases and input from biologists in their area of expertise. PathBinderA mines the biological “literaturome” by searching for new interactions or supporting evidence for existing interactions in metabolic networks. Sentences from abstracts are ranked in terms of the likelihood that an interaction is described and combined with evidence provided by other sentences. FCModeler, a publicly available software package, enables the biologist to visualize and model metabolic and regulatory network maps. FCModeler aids in the development and evaluation of hypotheses, and provides a modeling framework for assessing the large amounts of data captured by high-throughput gene expression experiments.