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Edgetic Perturbation Models


    A key challenge in molecular biology is to explore molecular mechanisms of human inherited diseases, how a genetic mutation causes an undesirable phenotype (Botstein et al, 2003). Modern high throughput techniques allow us to systematically investigate these mechanisms using biological network models in which nodes and edges are used to represent biological components (i.e. genes, RNAs, proteins) and their interactions, respectively (Nurse and Hayles, 2011). This modeling methodology leads to the prosperity of Graph Theory in biology studies (Seebacher and Gavin, 2011). Classical models of genotype-to-phenotype relationships for human genetic diseases generally assume that a mutation causes complete loss of a gene product ,i.e. RNAs (Hwang et al, 2009) or proteins (Yue et al, 2005). Intuitively, this gene-loss model can be viewed as node removal in a biological network.

    However, not all the mutations cause the entire losses of gene products, as some mutations result in single functional loss of proteins, not all of them. To this end, Edgetic (edge-specific genetic perturbation) model, a new invented concept (Zhong et al, 2009), revolutionizes the way in network perturbation from node removal to edge removal. This new methodology of network modeling offers an explanatory power to complex relationships between genotypes and phenotypes, i.e. why different mutations from the same gene lead to different phenotypes. Furthermore, Dr. Marc Vidal and his colleagues not only tested this model bioinformatically (Zhong et al, 2009) but also experimentally (Dreze et al, 2009).

    Is it the right time for us to switch our attentions from disease-associated genes to disease-affected interactions (Wang et al, 2011)?


    • Zhong Q, Vidal M. et al. Edgetic perturbation models of human inherited disorders. Mol Syst Biol. 5:321, Nov 2009. PMID: 19888216, PDF.


    • Botstein D and Risch N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet Rev. 33, Mar 2003, PDF.
    • Nurse P and Hayles J. The Cell in an Era of Systems Biology. Cell. 144, Mar 2011, PDF.
    • Seebacher J and Gavin AC. SnapShot: Protein-Protein Interaction Networks. Cell. 144, Mar 2011. PDF.
    • Hwang D, Hood LE et alA systems approach to prion disease. Mol Syst Biol. 5:252, Mar 2009, PDF.
    • Yue P. et al. Loss of Protein Structure Stability as a Major Causative Factor in Monogenic Disease. J Mol Biol. 353:2, Oct 2005, PDF.
    • Dreze M, Vidal M. et al. Edgetic perturbation of a C. elegans BCL2 ortholog. Nat Methods. 6, Oct 2009. PDF.
    • Wang X. et al. Network-based methods for human disease gene prediction. Briefings in Functional Genomics, 10:5, Jul 2011, PDF.

    Technical References

    • Mann-Whitney U-test, PDF.
    • Fisher's exact test, PDF.
    • Finn RD et al, Pfam: clans, web tools and services. Nucleic Acids Res. 34, Jan 2006, PDF, link.
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