an integrated framework to model cellular phenotype as a component of biochemical networks

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ID: 135895
2011
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Abstract
Identification of regulatory molecules in signaling pathways is critical for understanding cellular behavior. Given the complexity of the transcriptional gene network, the relationship between molecular expression and phenotype is difficult to determine using reductionist experimental methods. Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. Integrating gene expression data with phenotypic data in transcriptional network models enables systematic identification of critical molecules in a biological network. We developed an approach based on fuzzy logic to model cell budding in Saccharomyces cerevisiae using time series expression microarray data of the cell cycle. Cell budding is a phenotype of viable cells undergoing division. Predicted interactions between gene expression and phenotype reflected known biological relationships. Dynamic simulation analysis reproduced the behavior of the yeast cell cycle and accurately identified genes and interactions which are essential for cell viability.
Reference Key
gormley2011advancesan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Michael Gormley;Viswanadha U. Akella;Judy N. Quong;Andrew A. Quong
Journal journal of hepatocellular carcinoma
Year 2011
DOI
10.1155/2011/608295
URL
http://dx.doi.org/10.1155/2011/608295
Keywords Keywords not found

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