Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2007 Jan;35(Database issue):D533-7.
doi: 10.1093/nar/gkl823.

SYSTOMONAS--an Integrated Database for Systems Biology Analysis of Pseudomonas

Affiliations
Free PMC article

SYSTOMONAS--an Integrated Database for Systems Biology Analysis of Pseudomonas

Claudia Choi et al. Nucleic Acids Res. .
Free PMC article

Abstract

To provide an integrated bioinformatics platform for a systems biology approach to the biology of pseudomonads in infection and biotechnology the database SYSTOMONAS (SYSTems biology of pseudOMONAS) was established. Besides our own experimental metabolome, proteome and transcriptome data, various additional predictions of cellular processes, such as gene-regulatory networks were stored. Reconstruction of metabolic networks in SYSTOMONAS was achieved via comparative genomics. Broad data integration is realized using SOAP interfaces for the well established databases BRENDA, KEGG and PRODORIC. Several tools for the analysis of stored data and for the visualization of the corresponding results are provided, enabling a quick understanding of metabolic pathways, genomic arrangements or promoter structures of interest. The focus of SYSTOMONAS is on pseudomonads and in particular Pseudomonas aeruginosa, an opportunistic human pathogen. With this database we would like to encourage the Pseudomonas community to elucidate cellular processes of interest using an integrated systems biology strategy. The database is accessible at http://www.systomonas.de.

Figures

Figure 1
Figure 1
The visualization of metabolic pathways from KEGG in SYSTOMONAS is based on GraphViz using the dot layout. All known metabolic reactions are depicted here for the ‘Urea cycle and metabolism of amino groups’ pathway. Rectangles depict metabolic reactions, ellipses represent metabolites whose names are abbreviated with an asterisk * when the length exceeds 10 letters. Both types of nodes are clickable. Different colours for rectangles specify distinct Pseudomonas species, which catalyse the corresponding reaction. These pathways can be obtained from metabolic pathway entries. An abbreviation code for the species is provided with the visualization output (AO1 = P.aeruginosa PAO1, A14 = P.aeruginosa PA14, P = P.putida KT2440, Pf-5 = P.fluorescens F5, F01 = P.fluorescens PfO-1, ST = P.syringae pv tomato, SP = P.syringae pv phaseolicola, SS = P.syringae pv syringae)
Figure 2
Figure 2
Semi-quantitative scatter plot for the comparison of metabolic profiles measured for P.aeruginosa PAO1 grown under aerobic conditions. Metabolites were analysed by GC/MS. Mean peak areas and standard deviations for the metabolites were calculated and plotted on a logarithmic scale using gnuplot (). Metabolites measured from samples of exponentially growing cells under aerobic conditions are plotted along the x-axis against metabolites from samples of resting cells along the y-axis. The metabolite name for every data point is shown as tooltip while moving the mouse over the point (e.g. for the data point ‘Lactate’) and linked back to the corresponding database entry. If the metabolic profile during one experimental condition is similar to the condition compared, data points will arrange closely to the diagonal line.
Figure 3
Figure 3
SYSTOMONAS architecture: combining the data warehouse concept and web services to provide a quick and dynamically updated data integration.

Similar articles

See all similar articles

Cited by 13 articles

See all "Cited by" articles

References

    1. Pinney J.W., Shirley M.W., McConkey G.A., Westhead D.R. metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella. Nucleic Acids Res. 2005;33:1399–1409. - PMC - PubMed
    1. Karp P.D., Paley S., Romero P. The Pathway Tools software. Bioinformatics. 2002;18:S225–S232. - PubMed
    1. Goesmann A., Haubrock M., Meyer F., Kalinowski J., Giegerich R. PathFinder: reconstruction and dynamic visualization of metabolic pathways. Bioinformatics. 2002;18:124–129. - PubMed
    1. Münch R., Hiller K., Grote A., Scheer M., Klein J., Schobert M., Jahn D. Virtual Footprint and PRODORIC: an integrative framework for regulon prediction in prokaryotes. Bioinformatics. 2005;21:4187–4189. - PubMed
    1. Karp P.D., Ouzounis C.A., Moore-Kochlacs C., Goldovsky L., Kaipa P., Ahrén D., Tsoka S., Darzentas N., Kunin V., López-Bigas N. Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res. 2005;33:6083–6089. - PMC - PubMed

Publication types

Substances

Feedback