Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species

PLoS Comput Biol. 2014 Feb 6;10(2):e1003465. doi: 10.1371/journal.pcbi.1003465. eCollection 2014 Feb.


We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Biomass
  • Biotechnology
  • Computational Biology
  • Evolution, Molecular
  • Fungi / classification
  • Fungi / genetics*
  • Fungi / metabolism*
  • Gene Knockout Techniques
  • Genome, Fungal*
  • Industrial Microbiology
  • Metabolic Networks and Pathways* / genetics
  • Models, Biological
  • Models, Genetic
  • Models, Statistical
  • Phylogeny
  • Saccharomyces cerevisiae / genetics
  • Saccharomyces cerevisiae / growth & development
  • Saccharomyces cerevisiae / metabolism
  • Species Specificity

Grants and funding

This work was financially supported by Academy of Finland ( postdoctoral researcher's fellowships (grant no 127715 for MA and 140380 for PJ), and supported in part by the EU FP7 ( grants BIOLEDGE (FP7-KBBE-289126) and PASCAL2 ( (ICT-2007-216886), Ministry of Employment ( and the Economy KYT programme (GEOBIOINFO, grant 26/2011/KYT), Academy of Finland grant 118653 (ALGODAN), and Finnish Centre of Excellence in White Biotechnology - Green Chemistry, Project No. 118573 ( The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.