Use of Logic Relationships to Decipher Protein Network Organization

doi:10.1126/science.1103330

Account Information

Guest Alerts | Access Rights | My Account | Sign In

Site Navigation

Article Views

Article Tools

Help & Feedback

My Science Signaling

Logo for

Science 306 (5705): 2246-2249

Use of Logic Relationships to Decipher Protein Network Organization

Peter M. Bowers,¹^,2 Shawn J. Cokus,³ David Eisenberg,¹^,2 Todd O. Yeates²^,4^*

Abstract: A major focus of genome research is to decipher the networksof molecular interactions that underlie cellular function. Wedescribe a computational approach for identifying detailed relationshipsbetween proteins on the basis of genomic data. Logic analysisof phylogenetic profiles identifies triplets of proteins whosepresence or absence obey certain logic relationships. For example,protein C may be present in a genome only if proteins A andB are both present. The method reveals many previously unidentifiedhigher order relationships. These relationships illustrate thecomplexities that arise in cellular networks because of branchingand alternate pathways, and they also facilitate assignmentof cellular functions to uncharacterized proteins.

¹ Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.
² Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
³ Department of Mathematics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁴ Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA.

^* To whom correspondence should be addressed. E-mail: yeates{at}mbi.ucla.edu

The editors suggest the following Related Resources on Science sites:

In Science Signaling

EDITORS' CHOICE
NETWORK ANALYSIS
The Proteins Came in Three by Three: (4 January 2005)
Sci. STKE 2005 (265), tw11. [DOI: 10.1126/stke.2652005tw11]
| Abstract »

THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES:

Logical Identification of an Allantoinase Analog (puuE) Recruited from Polysaccharide Deacetylases.: I. Ramazzina, L. Cendron, C. Folli, R. Berni, D. Monteverdi, G. Zanotti, and R. Percudani (2008)
J. Biol. Chem. 283, 23295-23304
| Abstract » | Full Text » | PDF »

Microbial genotype-phenotype mapping by class association rule mining.: M. Tamura and P. D'haeseleer (2008)
Bioinformatics 24, 1523-1529
| Abstract » | Full Text » | PDF »

Assigning functional linkages to proteins using phylogenetic profiles and continuous phenotypes.: O. Gonzalez and R. Zimmer (2008)
Bioinformatics 24, 1257-1263
| Abstract » | Full Text » | PDF »

Accurate prediction of enzyme mutant activity based on a multibody statistical potential.: M. Masso and I. I. Vaisman (2007)
Bioinformatics 23, 3155-3161
| Abstract » | Full Text » | PDF »

Extracting three-way gene interactions from microarray data.: J. Zhang, Y. Ji, and L. Zhang (2007)
Bioinformatics 23, 2903-2909
| Abstract » | Full Text » | PDF »

Detecting the Coevolution of Biosequences An Example of RNA Interaction Prediction.: C.-H. Yeang, J. F. J. Darot, H. F. Noller, and D. Haussler (2007)
Mol. Biol. Evol. 24, 2119-2131
| Abstract » | Full Text » | PDF »

Information-based clustering.: N. Slonim, G. S. Atwal, G. Tkacik, and W. Bialek (2005)
PNAS 102, 18297-18302
| Abstract » | Full Text » | PDF »

Reconstructing the pathways of a cellular system from genome-scale signals by using matrix and tensor computations.: O. Alter and G. H. Golub (2005)
PNAS 102, 17559-17564
| Abstract » | Full Text » | PDF »

Sulfatases and sulfatase modifying factors: an exclusive and promiscuous relationship.: M. Sardiello, I. Annunziata, G. Roma, and A. Ballabio (2005)
Hum. Mol. Genet. 14, 3203-3217
| Abstract » | Full Text » | PDF »

Lasting impressions: Motifs in protein-protein maps may provide footprints of evolutionary events.: J. J. Rice, A. Kershenbaum, and G. Stolovitzky (2005)
PNAS 102, 3173-3174
| Full Text » | PDF »