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On the way to understand biological complexity in plants: S-nutrition as a case study for systems biology

Cellular & Molecular Biology Letters volume 11, pages 37–56 (2006)Cite this article

Abstract

The establishment of technologies for high-throughput DNA sequencing (genomics), gene expression (transcriptomics), metabolite and ion analysis (metabolomics/ionomics) and protein analysis (proteomics) carries with it the challenge of processing and interpreting the accumulating data sets. Publicly accessible databases and newly development and adapted bioinformatic tools are employed to mine this data in order to filter relevant correlations and create models describing physiological states. These data allow the reconstruction of networks of interactions of the various cellular components as enzyme activities and complexes, gene expression, metabolite pools or pathway flux modes. Especially when merging information from transcriptomics, metabolomics and proteomics into consistent models, it will be possible to describe and predict the behaviour of biological systems, for example with respect to endogenous or environmental changes. However, to capture the interactions of network elements requires measurements under a variety of conditions to generate or refine existing models. The ultimate goal of systems biology is to understand the molecular principles governing plant responses and consistently explain plant physiology.

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  1. Max-Planck-Institute fuer Molekulare Pflanzenphysiologie, Am Muehlenberg 1, 14476, Golm, Germany

    Holger Hesse & Rainer Hoefgen

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  1. Holger Hesse
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  2. Rainer Hoefgen
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Correspondence to Holger Hesse.

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Hesse, H., Hoefgen, R. On the way to understand biological complexity in plants: S-nutrition as a case study for systems biology. Cell. Mol. Biol. Lett. 11, 37–56 (2006). https://doi.org/10.2478/s11658-006-0004-8

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  • DOI: https://doi.org/10.2478/s11658-006-0004-8

Key words

  • Sulfur metabolism
  • Systems biology
  • Metabolome
  • Ionome
  • Transcriptome
  • Proteome

Cellular & Molecular Biology Letters

ISSN: 1689-1392

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