title: Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data.
abstract: BACKGROUND
  Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., iMM904) remains significantly less predictive than the latest E. coli model (i.e., iAF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model.
  
  
  RESULTS
  In this paper we make use of the automated GrowMatch procedure for restoring consistency with single gene deletion experiments in yeast and extend the procedure to make use of synthetic lethality data using the genome-scale model iMM904 as a basis. We identified and vetted using literature sources 120 distinct model modifications including various regulatory constraints for minimal and YP media. The incorporation of the suggested modifications led to a substantial increase in the fraction of correctly predicted lethal knockouts (i.e., specificity) from 38.84% (87 out of 224) to 53.57% (120 out of 224) for the minimal medium and from 24.73% (45 out of 182) to 40.11% (73 out of 182) for the YP medium. Synthetic lethality predictions improved from 12.03% (16 out of 133) to 23.31% (31 out of 133) for the minimal medium and from 6.96% (8 out of 115) to 13.04% (15 out of 115) for the YP medium.
  
  
  CONCLUSIONS
  Overall, this study provides a roadmap for the computationally driven correction of multi-compartment genome-scale metabolic models and demonstrates the value of synthetic lethals as curation agents.
authors.affiliation: Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: Ali R
authors.initials: AR
authors.lastname: Zomorrodi
authors.forename: Costas D
authors.initials: CD
authors.lastname: Maranas
mesh_terms: D020043:Genes, Essential / Q000235:genetics*; D005800:Genes, Fungal / Q000235:genetics; D005804:Genes, Lethal / Q000235:genetics*; D008954:Models, Biological; D009154:Mutation; D009930:Organic Chemicals / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics* / Q000378:metabolism*
chemical_list: D009930:Organic Chemicals