Searching for Limited Connectivity in Genetic Network Models

The inference of regulatory interactions between genes from time-course micro-array data is one of the most challenging tasks in the field of functional genomics. The multitude of genes that can now be measured using micro-array technology requires analysis tools that can easily scale-up with respect to the number of genes. This scalability is especially important when inferring genetic interactions, because this task is complicated by the combinatorial nature of gene interaction and because the high cost of micro-array measurements still severely limits the number of measured timepoints. Because of this limitation of the data, it is essential to incorporate as much additional information as possible. This can be achieved by applying constraints based on general biological knowledge and by including specific knowledge about known interactions. In this paper we employ the fact that genetic networks are believed to exhibit limited connectivity. We propose a general approach in which we separate the task of finding the structure of the networks from the task of finding the best parameters of the model, given the structure. The second task can be solved efficiently for most models, but the first task amounts to a search problem which requires the choice of a suitable evaluation function and search strategy. Experimental investigations determined that the best evaluation function is simply the mean squared error on the training data. Through further extensive experimental investigation of several search strategies, it was found that the best search strategy is based on an approach of greedily increasing the number of connections. The strength of the proposed approach lies in the fact that it can be employed to all genetic network models and allows genetic network models to scale up to a large number of genes.