title: Scope and limitations of yeast as a model organism for studying human tissue-specific pathways.
abstract: BACKGROUND
  Budding yeast, S. cerevisiae, has been used extensively as a model organism for studying cellular processes in evolutionarily distant species, including humans. However, different human tissues, while inheriting a similar genetic code, exhibit distinct anatomical and physiological properties. Specific biochemical processes and associated biomolecules that differentiate various tissues are not completely understood, neither is the extent to which a unicellular organism, such as yeast, can be used to model these processes within each tissue.
  
  
  RESULTS
  We present a novel framework to systematically quantify the suitability of yeast as a model organism for different human tissues. To this end, we develop a computational method for dissecting the global human interactome into tissue-specific cellular networks. By individually aligning these networks with the yeast interactome, we simultaneously partition the functional space of human genes, and their corresponding pathways, based on their conservation both across species and among different tissues. Finally, we couple our framework with a novel statistical model to assess the conservation of tissue-specific pathways and infer the overall similarity of each tissue with yeast. We further study each of these subspaces in detail, and shed light on their unique biological roles in the human tissues.
  
  
  CONCLUSIONS
  Our framework provides a novel tool that can be used to assess the suitability of the yeast model for studying tissue-specific physiology and pathophysiology in humans. Many complex disorders are driven by a coupling of housekeeping (universally expressed in all tissues) and tissue-selective (expressed only in specific tissues) dysregulated pathways. While tissue-selective genes are significantly associated with the onset and development of a number of tissue-specific pathologies, we show that the human-specific subset has even higher association. Consequently, they provide excellent candidates as drug targets for therapeutic interventions.
authors.affiliation: Department of Computer Sciences, Purdue University, West Lafayette, 47907, USA. mohammadi@purdue.edu.
authors.forename: Shahin
authors.initials: S
authors.lastname: Mohammadi
authors.affiliation: Department of Computer Sciences, Purdue University, West Lafayette, 47907, USA. bsaberid@purdue.edu.
authors.forename: Baharak
authors.initials: B
authors.lastname: Saberidokht
authors.affiliation: Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. shankar@ucsd.edu.
authors.forename: Shankar
authors.initials: S
authors.lastname: Subramaniam
authors.affiliation: Department of Computer Sciences, Purdue University, West Lafayette, 47907, USA. ayg@cs.purdue.edu.
authors.forename: Ananth
authors.initials: A
authors.lastname: Grama
mesh_terms: D019295:Computational Biology / Q000379:methods; D053263:Gene Regulatory Networks; D006801:Humans; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D060066:Protein Interaction Maps; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism*; D013045:Species Specificity; D049490:Systems Biology / Q000379:methods