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From the same journal

The genome-scale metabolic network of Ectocarpus siliculosus (EctoGEM): a resource to study brown algal physiology and beyond

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Published copy (DOI)


  • Sylvain Prigent
  • Guillaume Collet
  • Simon M Dittami
  • Ludovic Delage
  • Floriane Ethis de Corny
  • Olivier Dameron
  • Damien Eveillard
  • Sven Thiele
  • Jeanne Cambefort
  • Catherine Boyen
  • Anne Siegel
  • Thierry Tonon


Publication details

JournalThe Plant journal
DateE-pub ahead of print - 25 Jul 2014
DatePublished (current) - Oct 2014
Issue number2
Number of pages15
Pages (from-to)367-381
Early online date25/07/14
Original languageEnglish


Brown algae (stramenopiles) are key players in intertidal ecosystems, and represent a source of biomass with several industrial applications. Ectocarpus siliculosus is a model to study the biology of these organisms. Its genome has been sequenced and a number of post-genomic tools have been implemented. Based on this knowledge, we report the reconstruction and analysis of a genome-scale metabolic network for E. siliculosus, EctoGEM ( This atlas of metabolic pathways consists of 1866 reactions and 2020 metabolites, and its construction was performed by means of an integrative computational approach for identifying metabolic pathways, gap filling and manual refinement. The capability of the network to produce biomass was validated by flux balance analysis. EctoGEM enabled the reannotation of 56 genes within the E. siliculosus genome, and shed light on the evolution of metabolic processes. For example, E. siliculosus has the potential to produce phenylalanine and tyrosine from prephenate and arogenate, but does not possess a phenylalanine hydroxylase, as is found in other stramenopiles. It also possesses the complete eukaryote molybdenum co-factor biosynthesis pathway, as well as a second molybdopterin synthase that was most likely acquired via horizontal gene transfer from cyanobacteria by a common ancestor of stramenopiles. EctoGEM represents an evolving community resource to gain deeper understanding of the biology of brown algae and the diversification of physiological processes. The integrative computational method applied for its reconstruction will be valuable to set up similar approaches for other organisms distant from biological benchmark models.

    Research areas

  • Genome, Plant, Molecular Sequence Data, Phaeophyta/genetics

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