Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
J Proteomics. 2010 Oct 10;73(11):2277-89. doi: 10.1016/j.jprot.2010.07.005. Epub 2010 Jul 15.

It's the machine that matters: Predicting gene function and phenotype from protein networks.

Author information

  • 1Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712-1064, USA. peggywang@mail.utexas.edu

Abstract

Increasing knowledge about the organization of proteins into complexes, systems, and pathways has led to a flowering of theoretical approaches for exploiting this knowledge in order to better learn the functions of proteins and their roles underlying phenotypic traits and diseases. Much of this body of theory has been developed and tested in model organisms, relying on their relative simplicity and genetic and biochemical tractability to accelerate the research. In this review, we discuss several of the major approaches for computationally integrating proteomics and genomics observations into integrated protein networks, then applying guilt-by-association in these networks in order to identify genes underlying traits. Recent trends in this field include a rising appreciation of the modular network organization of proteins underlying traits or mutational phenotypes, and how to exploit such protein modularity using computational approaches related to the internet search algorithm PageRank. Many protein network-based predictions have recently been experimentally confirmed in yeast, worms, plants, and mice, and several successful approaches in model organisms have been directly translated to analyze human disease, with notable recent applications to glioma and breast cancer prognosis.

Copyright © 2010 Elsevier B.V. All rights reserved.

PMID:
20637909
[PubMed - indexed for MEDLINE]
PMCID:
PMC2953423
Free PMC Article

Images from this publication.See all images (6)Free text

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science Icon for PubMed Central
    Loading ...
    Write to the Help Desk