Constructing signaling pathways from RNAI data using genetic algorithms

RNAi system allows us to see the phenotypes when some genes are removed from living cells. By observing these phenotypes, we can build signaling pathways without dealing with the chemistry inside the cell. However it is costly in terms of time and space complexity. Furthermore, there are some interactions RNAi data cannot distinguish that results in many different signaling pathways all of which are consistent with the RNAi data. In this paper, we combine genetic algorithms with some greedy approaches to find most of the networks that fits the RNAi experiments. Our algorithm works much faster than previous algorithms and finds many results in a small amount of time. The resulting topologies have equal priority which would be used as inputs of classification algorithms.