Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein–protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein.

Once interactome network is mapped as an electrical circuit, Kirchhoff’s circuit laws (current entering at a node = current leaving that node) are implemented. Information flow through a protein node is calculated as an absolute sum of current flow through that node as algorithm iterate over all combinations of remaining node pairs, assigning one as a current source and second one as a sink node. Algorithm detect not only proteins those are central to the network but also those proteins which belong to slightly weaker alternative pathways, which would have otherwise remained undetected.

We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein’s information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy.

Overall the study demonstarted that information flow based approach can predict subtle gene properties such as presence and number of phenotypes.