Gene Regulatory Network Inference with Latent Force Models

TL;DR

This paper introduces a novel Bayesian latent force model that incorporates translation delays in gene regulatory network inference, enhancing biological interpretability and modeling non-linear dynamics from RNA-sequencing data.

Contribution

It combines mechanistic equations with Gaussian processes to explicitly model delays, providing a new approach for more accurate and interpretable gene regulatory network inference.

Findings

Improved modeling of translation delays in GRN inference.

Enhanced biological interpretability of inferred networks.

Ability to capture non-linear gene interactions.

Abstract

Delays in protein synthesis cause a confounding effect when constructing Gene Regulatory Networks (GRNs) from RNA-sequencing time-series data. Accurate GRNs can be very insightful when modelling development, disease pathways, and drug side-effects. We present a model which incorporates translation delays by combining mechanistic equations and Bayesian approaches to fit to experimental data. This enables greater biological interpretability, and the use of Gaussian processes enables non-linear expressivity through kernels as well as naturally accounting for biological variation.