Switched latent force models for reverse-engineering transcriptional regulation in gene expression data

TL;DR

This paper introduces a switched latent force model that accurately infers transcription factor activities from gene expression data, capturing sudden changes and non-linearities in transcriptional regulation.

Contribution

It proposes a novel dynamical model that switches between different TF activities and systems, improving reverse-engineering of gene regulation from expression data.

Findings

Successfully fits gene expression data from simulations and real experiments.

Infers continuous-time transcription factor profiles.

Captures discrete changes and non-linearities in transcription networks.

Abstract

To survive environmental conditions, cells transcribe their response activities into encoded mRNA sequences in order to produce certain amounts of protein concentrations. The external conditions are mapped into the cell through the activation of special proteins called transcription factors (TFs). Due to the difficult task to measure experimentally TF behaviours, and the challenges to capture their quick-time dynamics, different types of models based on differential equations have been proposed. However, those approaches usually incur in costly procedures, and they present problems to describe sudden changes in TF regulators. In this paper, we present a switched dynamical latent force model for reverse-engineering transcriptional regulation in gene expression data which allows the exact inference over latent TF activities driving some observed gene expressions through a linear differential equation. To deal with discontinuities in the dynamics, we introduce an approach that switches between different TF activities and different dynamical systems. This creates a versatile representation of transcription networks that can capture discrete changes and non-linearities We evaluate our model on both simulated data and real-data (e.g. microaerobic shift in E. coli, yeast respiration), concluding that our framework allows for the fitting of the expression data while being able to infer continuous-time TF profiles.