  The evolution, regulation and sustenance of biological complexity is
determined by protein-protein interaction network that is filled with dynamic
events. Recent experimental evidences point out that clustering of proteins has
a vital role in many cellular processes. Upsurge in fluorescence imaging
methods has given a new spin to our ability to probe protein interactions in
cellular and sub-cellular compartments. Despite the increasing detection
sensitivity, quantitative information that can be obtained from these imaging
methods is limited. This is primarily due to (i) the difficulty in tracking the
problem analytically and (ii) limitations in spatio-temporal resolution that
can be achieved in interrogating living cells in real time. A novel point of
view based on diffusion-driven percolative clustering is proposed here that can
plausibly shed more light on the complex issues of protein-protein
interactions. Since this model is open to computational analysis, it is
quantitative in its premise. Besides being able to analyze the phenomenon, the
power of any model is gauged by its ability to predict interesting and novel
features of the phenomenon itself, which can subsequently be tested by
additional experiments. To this end, an experimental assay based on
fluorescence lifetime imaging is proposed to verify the validity of the
percolation model.
