Boston University Libraries OpenBU
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    •   OpenBU
    • College of Engineering
    • Biomedical Engineering
    • ENG: Biomedical Engineering: Scholarly Papers
    • View Item
    •   OpenBU
    • College of Engineering
    • Biomedical Engineering
    • ENG: Biomedical Engineering: Scholarly Papers
    • View Item

    Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production

    Thumbnail
    Date Issued
    2009-8-28
    Publisher Version
    10.1371/journal.pcbi.1000489
    Author(s)
    Colijn, Caroline
    Brandes, Aaron
    Zucker, Jeremy
    Lun, Desmond S.
    Weiner, Brian
    Farhat, Maha R.
    Cheng, Tan-Yun
    Moody, D. Branch
    Murray, Megan
    Galagan, James E.
    Share to FacebookShare to TwitterShare by Email
    Export Citation
    Download to BibTex
    Download to EndNote/RefMan (RIS)
    Metadata
    Show full item record
    Permanent Link
    https://hdl.handle.net/2144/3043
    Citation (published version)
    Colijn, Caroline, Aaron Brandes, Jeremy Zucker, Desmond S. Lun, Brian Weiner, Maha R. Farhat, Tan-Yun Cheng, D. Branch Moody, Megan Murray, James E. Galagan. "Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production" PLos Computational Biology 5(8): 1000489. (2009)
    Abstract
    Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data. Author Summary The ability of cells to survive and grow depends on their ability to metabolize nutrients and create products vital for cell function. This is done through a complex network of reactions controlled by many genes. Changes in cellular metabolism play a role in a wide variety of diseases. However, despite the availability of genome sequences and of genome-scale expression data, which give information about which genes are present and how active they are, our ability to use these data to understand changes in cellular metabolism has been limited. We present a new approach to this problem, linking gene expression data with models of cellular metabolism. We apply the method to predict the effects of drugs and agents on Mycobacterium tuberculosis (M. tb). Virulence, growth in human hosts, and drug resistance are all related to changes in M. tb's metabolism. We predict the effects of a variety of conditions on the production of mycolic acids, essential cell wall components. Our method successfully identifies seven of the eight known mycolic acid inhibitors in a compendium of 235 conditions, and identifies the top anti-TB drugs in this dataset. We anticipate that the method will have a range of applications in metabolic engineering, the characterization of disease states, and drug discovery.
    Rights
    Colijn et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    Collections
    • ENG: Biomedical Engineering: Scholarly Papers [294]
    • MED: Microbiology Papers [18]


    Boston University
    Contact Us | Send Feedback | Help
     

     

    Browse

    All of OpenBUCommunities & CollectionsIssue DateAuthorsTitlesSubjectsThis CollectionIssue DateAuthorsTitlesSubjects

    Deposit Materials

    LoginNon-BU Registration

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Boston University
    Contact Us | Send Feedback | Help