A Bayesian approach for structure learning in oscillating regulatory networks

TL;DR

This paper introduces a Bayesian method for inferring the structure of oscillating transcriptional regulatory networks by leveraging their oscillatory signals and using a frequency domain approach to improve accuracy.

Contribution

It presents a novel Bayesian hierarchical model that uses Fourier transforms to reconstruct network interactions specific to oscillatory biological systems.

Findings

Improves network reconstruction accuracy when oscillatory assumptions hold.

Remains competitive even when oscillatory assumptions are violated.

Demonstrates effectiveness on real and simulated biological data.

Abstract

Oscillations lie at the core of many biological processes, from the cell cycle, to circadian oscillations and developmental processes. Time-keeping mechanisms are essential to enable organisms to adapt to varying conditions in environmental cycles, from day/night to seasonal. Transcriptional regulatory networks are one of the mechanisms behind these biological oscillations. However, while identifying cyclically expressed genes from time series measurements is relatively easy, determining the structure of the interaction network underpinning the oscillation is a far more challenging problem. Here, we explicitly leverage the oscillatory nature of the transcriptional signals and present a method for reconstructing network interactions tailored to this special but important class of genetic circuits. Our method is based on projecting the signal onto a set of oscillatory basis functions using a Discrete Fourier Transform. We build a Bayesian Hierarchical model within a frequency domain linear model in order to enforce sparsity and incorporate prior knowledge about the network structure. Experiments on real and simulated data show that the method can lead to substantial improvements over competing approaches if the oscillatory assumption is met, and remains competitive also in cases it is not.