title: Metabolic reconstruction of the archaeon methanogen Methanosarcina Acetivorans.
abstract: BACKGROUND
  Methanogens are ancient organisms that are key players in the carbon cycle accounting for about one billion tones of biological methane produced annually. Methanosarcina acetivorans, with a genome size of ~5.7 mb, is the largest sequenced archaeon methanogen and unique amongst the methanogens in its biochemical characteristics. By following a systematic workflow we reconstruct a genome-scale metabolic model for M. acetivorans. This process relies on previously developed computational tools developed in our group to correct growth prediction inconsistencies with in vivo data sets and rectify topological inconsistencies in the model.
  
  
  RESULTS
  The generated model iVS941 accounts for 941 genes, 705 reactions and 708 metabolites. The model achieves 93.3% prediction agreement with in vivo growth data across different substrates and multiple gene deletions. The model also correctly recapitulates metabolic pathway usage patterns of M. acetivorans such as the indispensability of flux through methanogenesis for growth on acetate and methanol and the unique biochemical characteristics under growth on carbon monoxide.
  
  
  CONCLUSIONS
  Based on the size of the genome-scale metabolic reconstruction and extent of validated predictions this model represents the most comprehensive up-to-date effort to catalogue methanogenic metabolism. The reconstructed model is available in spreadsheet and SBML formats to enable dissemination.
authors.affiliation: Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: Vinay
authors.initials: V
authors.lastname: Satish Kumar
authors.forename: James G
authors.initials: JG
authors.lastname: Ferry
authors.forename: Costas D
authors.initials: CD
authors.lastname: Maranas
mesh_terms: D019295:Computational Biology / Q000379:methods*; D016680:Genome, Bacterial; D053858:Metabolic Networks and Pathways; D017020:Methanosarcina / Q000235:genetics / Q000378:metabolism*; D008954:Models, Biological