Abstract
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.
Highlights
OUR CONTRIBUTION In this paper, we focus on the logical approach to learn gene regulatory networks with delays from an existing knowledge that is Frontiers in Bioengineering and Biotechnology | Bioinformatics and Computational Biology expressed through a set of state transitions
Li et al tackle the inference of gene regulatory networks from temporal gene expression data
SUMMARY OF THE CONTRIBUTION To understand the memory effect involved in some interactions between biological components, it is necessary to include delayed influences in the model
Summary
LEARNING BOOLEAN NETWORKS WITH DELAYED INFLUENCES Various approaches have been recently designed to tackle the reverse engineering of gene regulatory networks from expression data This has led to the emergence of the so-called executable biology, whose goal is to provide formal methods to automatically synthesize models from experiments (Koksal et al, 2013). One common problem of discrete approaches taking expression data as input lies in the determination of a relevant threshold to define the inactive and active states of gene expression To position this hypothesis in the context of existing approaches to process raw biological data, let us cite the works of some authors, like Soinov et al (2003), who proposed an alternative methodology that considers not a concentration level, but the way the concentration is changed in the presence/absence of one regulator. This means that we are able to capture delayed influences in the inductive logic programing methodology
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