Learning Delayed Influences of Biological Systems
Tony Ribeiro, Morgan Magnin, Katsumi Inoue and Chiaki Sakama
Frontiers in Bioengineering and Biotechnology, 2(81), 2015.
Boolean networks are widely used model to represent gene interactions and global dynamical
behavior of gene regulatory networks. To understand the memory effect involved in some interactions
between biological components, it is necessary to include delayed influences in the model.
In this paper, we present a logical method to learn such models from sequences of gene expression data.
This method analyzes each sequence one by one to iteratively construct a Boolean network that captures
the dynamics of these observations. To illustrate the merits of this approach, we apply it to learning
real data from bioinformatic literature. Using data from the yeast cell cycle, we give experimental
results and show the scalability of the method. We show empirically that using this method we can
handle millions of observations and successfully capture delayed influences of Boolean networks.