Deducing topology of protein-protein interaction networks from experimentally measured sub-networks.
BACKGROUND
Protein-protein interaction networks are commonly sampled using yeast two hybrid approaches. However, whether topological information reaped from these experimentally-measured sub-networks can be extrapolated to complete protein-protein interaction networks is unclear.


RESULTS
By analyzing various experimental protein-protein interaction datasets, we found that they are not random samples of the parent networks. Based on the experimental bait-prey behaviors, our computer simulations show that these non-random sampling features may affect the topological information. We tested the hypothesis that a core sub-network exists within the experimentally sampled network that better maintains the topological characteristics of the parent protein-protein interaction network. We developed a method to filter the experimentally sampled network to result in a core sub-network that more accurately reflects the topology of the parent network. These findings have fundamental implications for large-scale protein interaction studies and for our understanding of the behavior of cellular networks.


CONCLUSIONS
The topological information from experimental measured networks network as is may not be the correct source for topological information about the parent protein-protein interaction network. We define a core sub-network that more accurately reflects the topology of the parent network.
Yang
Ling
L
Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California 90095, USA . lyang@mednet.ucla.edu
Vondriska
Thomas M
TM
Han
Zhangang
Z
Maclellan
W Robb
WR
Weiss
James N
JN
Qu
Zhilin
Z
D000818:Animals; D002468:Cell Physiological Phenomena; D016000:Cluster Analysis; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D016571:Neural Networks, Computer; D025941:Protein Interaction Mapping / Q000379:methods*; D040901:Proteomics / Q000379:methods; D018401:Sample Size; D013598:Systems Theory; D020798:Two-Hybrid System Techniques