Gene Coexpression as Hebbian Learning in Prokaryotic Genomes

Abstract

Biological interaction networks represent a powerful tool for characterizing intracellular functional relationships, such as transcriptional regulation and protein interactions. Although artificial neural networks are routinely employed for a broad range of applications across computational biology, their underlying connectionist basis has not been extensively applied to modeling biological interaction networks. In particular, the Hopfield network offers nonlinear dynamics that represent the minimization of a system energy function through temporally distinct rewiring events. Here, a scaled energy minimization model is presented to test the feasibility of deriving a composite biological interaction network from multiple constituent data sets using the Hebbian learning principle. The performance of the scaled energy minimization model is compared against the standard Hopfield model using simulated data. Several networks are also derived from real data, compared to one another, and then combined to produce an aggregate network. The utility and limitations of the proposed model are discussed, along with possible implications for a genomic learning analogy where the fundamental Hebbian postulate is rendered into its genomic equivalent: Genes that function together junction together.