Learning Delayed Influences of Biological Systems

Abstract

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Boolean networks are a widely used model to represent gene interactions and global dynamical behavior of genetic 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 on 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.

Keywords

• Gene Regulatory Networks
• machine learning
• Time delay
• Boolean network
• state transitions
• logic programming