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In our previous publication, a framework for information flow in interactionnetworks based on random walks with damping was formulated with two fundamentalmodes: emitting and absorbing. While many other network analysis methods basedon random walks or equivalent notions have been developed before and after ourearlier work, one can show that they can all be mapped to one of the two modes.In addition to these two fundamental modes, a major strength of our earlierformalism was its accommodation of context-specific directed information flowthat yielded plausible and meaningful biological interpretation of proteinfunctions and pathways. However, the directed flow from origins to destinationswas induced via a potential function that was heuristic. Here, with atheoretically sound approach called the channel mode, we extend our earlierwork for directed information flow. This is achieved by constructing apotential function facilitating a purely probabilistic interpretation of thechannel mode. For each network node, the channel mode combines the solutions ofemitting and absorbing modes in the same context, producing what we call achannel tensor. The entries of the channel tensor at each node can beinterpreted as the amount of flow passing through that node from an origin to adestination. Similarly to our earlier model, the channel mode encompassesdamping as a free parameter that controls the locality of information flow.Through examples involving the yeast pheromone response pathway, we illustratethe versatility and stability of our new framework.

Information flow in interaction networks II: channels, path lengths and potentials