Identifying Genetic Network Using Experimental Time Series Data by Boolean Algorithm

Abstract

Recently, a variety of experimental techniques for biological field have been developed. These technologies have made it possible to observe the expression of many genes simultaneously and to accumulate a vast amount data. One of the challenging research areas is to extract the genetic networks from these large data. A lot of methods for this problem proposed; quantitative model [2], statistic model [3], hybrid model [7], and Boolean model [3]. However, a lot of these papers analyzed only artificial data [5] and deletion strain data [6]. In this paper, we applied Boolean algorithm for extraction genetic network from experimental time series data. Using binary data that was made from real data, our system was achieved to categorize genes to some equivalence classes and discover genetic interactions.