From uncertain protein interaction networks to signaling pathways through intensive color coding

Discovering signaling pathways in protein interaction networks is a key ingredient in understanding how proteins carry out cellular functions. These interactions however can be uncertain events that may or may not take place depending on many factors including the internal factors, such as the size and abundance of the proteins, or the external factors, such as mutations, disorders and drug intake. In this paper, we consider the problem of finding causal orderings of nodes in such protein interaction networks to discover signaling pathways. We adopt color coding technique to address this problem. Color coding method may fail with some probability. By allowing it to run for sufficient time, however, its confidence in the optimality of the result can converge close to 100%. Our key contribution in this paper is elimination of the key conservative assumptions made by the traditional color coding methods while computing its success probability. We do this by carefully establishing the relationship between node colors, network topology and success probability. As a result our method converges to any confidence value much faster than the traditional methods. Thus, it is scalable to larger protein interaction networks and longer signaling pathways than existing methods. We demonstrate, both theoretically and experimentally that our method outperforms existing methods.