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Open Access Research article

Microarray data can predict diurnal changes of starch content in the picoalga Ostreococcus

Oksana Sorokina1*, Florence Corellou23, David Dauvillée4, Anatoly Sorokin57, Igor Goryanin5, Steven Ball4, François-Yves Bouget23 and Andrew J Millar16

Author Affiliations

1 School of Biological Sciences, The University of Edinburgh King's Buildings, Mayfield Road, Edinburgh EH9 3JH, UK

2 UPMC Univ Paris 06, UMR7621 LOMIC. Observatoire Océanologique, F-66651, Banyuls/mer, France

3 CNRS, UMR7621 LOMIC. Observatoire Océanologique, F-66651, Banyuls/mer, France

4 UGSF, UMR 8576 CNRS, USTL, Univ Lille Nord de France, F-59650 Villeneuve d'Ascq, France

5 School of Informatics, Informatics Forum, The University of Edinburgh, 10 Crichton Street, Edinburgh, EH8 9AB, UK

6 Centre for Systems Biology at Edinburgh, C.H. Waddington Building, King's Buildings, Mayfield Road, Edinburgh EH9 3JD, UK

7 Institute of Cell Biophysics RAS, Pushchino, Moscow region, 142290, Russia

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BMC Systems Biology 2011, 5:36  doi:10.1186/1752-0509-5-36

Published: 26 February 2011

Abstract

Background

The storage of photosynthetic carbohydrate products such as starch is subject to complex regulation, effected at both transcriptional and post-translational levels. The relevant genes in plants show pronounced daily regulation. Their temporal RNA expression profiles, however, do not predict the dynamics of metabolite levels, due to the divergence of enzyme activity from the RNA profiles.

Unicellular phytoplankton retains the complexity of plant carbohydrate metabolism, and recent transcriptomic profiling suggests a major input of transcriptional regulation.

Results

We used a quasi-steady-state, constraint-based modelling approach to infer the dynamics of starch content during the 12 h light/12 h dark cycle in the model alga Ostreococcus tauri. Measured RNA expression datasets from microarray analysis were integrated with a detailed stoichiometric reconstruction of starch metabolism in O. tauri in order to predict the optimal flux distribution and the dynamics of the starch content in the light/dark cycle. The predicted starch profile was validated by experimental data over the 24 h cycle. The main genetic regulatory targets within the pathway were predicted by in silico analysis.

Conclusions

A single-reaction description of starch production is not able to account for the observed variability of diurnal activity profiles of starch-related enzymes. We developed a detailed reaction model of starch metabolism, which, to our knowledge, is the first attempt to describe this polysaccharide polymerization while preserving the mass balance relationships. Our model and method demonstrate the utility of a quasi-steady-state approach for inferring dynamic metabolic information in O. tauri directly from time-series gene expression data.