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    Predicting cellular growth from gene expression signatures

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    Predicting cellular growth from ...
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    Genre
    Journal Article
    Date
    2009-01-01
    Author
    Airoldi, EM
    Huttenhower, C
    Gresham, D
    Lu, C
    Caudy, AA
    Dunham, MJ
    Broach, JR
    Botstein, D
    Troyanskaya, OG
    Subject
    Algorithms
    Cell Proliferation
    Computer Simulation
    Gene Expression Profiling
    Models, Biological
    Proteome
    Saccharomyces cerevisiae
    Saccharomyces cerevisiae Proteins
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/5592
    
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    DOI
    10.1371/journal.pcbi.1000257
    Abstract
    Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate. © 2009 Airoldi et al.
    Citation to related work
    Public Library of Science (PLoS)
    Has part
    PLoS Computational Biology
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    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/5574
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