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.
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