title: Genome-level transcription data of Yersinia pestis analyzed with a new metabolic constraint-based approach.
abstract: BACKGROUND
  Constraint-based computational approaches, such as flux balance analysis (FBA), have proven successful in modeling genome-level metabolic behavior for conditions where a set of simple cellular objectives can be clearly articulated. Recently, the necessity to expand the current range of constraint-based methods to incorporate high-throughput experimental data has been acknowledged by the proposal of several methods. However, these methods have rarely been used to address cellular metabolic responses to some relevant perturbations such as antimicrobial or temperature-induced stress. Here, we present a new method for combining gene-expression data with FBA (GX-FBA) that allows modeling of genome-level metabolic response to a broad range of environmental perturbations within a constraint-based framework. The method uses mRNA expression data to guide hierarchical regulation of cellular metabolism subject to the interconnectivity of the metabolic network.
  
  
  RESULTS
  We applied GX-FBA to a genome-scale model of metabolism in the gram negative bacterium Yersinia pestis and analyzed its metabolic response to (i) variations in temperature known to induce virulence, and (ii) antibiotic stress. Without imposition of any a priori behavioral constraints, our results show strong agreement with reported phenotypes. Our analyses also lead to novel insights into how Y. pestis uses metabolic adjustments to counter different forms of stress.
  
  
  CONCLUSIONS
  Comparisons of GX-FBA predicted metabolic states with fluxomic measurements and different reported post-stress phenotypes suggest that mass conservation constraints and network connectivity can be an effective representative of metabolic flux regulation in constraint-based models. We believe that our approach will be of aid in the in silico evaluation of cellular goals under different conditions and can be used for a variety of analyses such as identification of potential drug targets and their action.
authors.affiliation: Biosciences & Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550-0808, USA. navid1@llnl.gov
authors.forename: Ali
authors.initials: A
authors.lastname: Navid
authors.forename: Eivind
authors.initials: E
authors.lastname: Almaas
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000494:pharmacology; D002118:Calcium / Q000378:metabolism; D002244:Carbon / Q000378:metabolism; D023281:Genomics / Q000379:methods*; D053858:Metabolic Networks and Pathways / Q000187:drug effects / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D013312:Stress, Physiological / Q000187:drug effects / Q000235:genetics; D013696:Temperature; D014158:Transcription, Genetic / Q000187:drug effects; D015010:Yersinia pestis / Q000187:drug effects / Q000235:genetics* / Q000378:metabolism* / Q000502:physiology
chemical_list: D000900:Anti-Bacterial Agents; D002244:Carbon; D002118:Calcium