  In this paper, we study the large-scale protein interaction network of yeast
uti lizing a stochastic method based upon percolation of random graphs. In
order to find the global features of connectivities in the network, we
introduce numeric al measures that quantify (1) how strongly a protein ties
with the other parts o f the network and (2) how significantly an interaction
contributes to the integr ity of the network. Our study shows that the
distribution of essential proteins is distinct from the background in terms of
global connectivities. This observ ation highlights a fundamental difference
between the essential and the non-esse ntial proteins in the network.
Furthermore, we find that the interaction data o btained from different
experimental methods such as immunoprecipitation and two- hybrid techniques
possess different characteristics. We discuss the biological implications of
these observations.
