Switched latent force models for reverse-engineering transcriptional regulation in gene expression data
This work addresses the challenge of modeling transcription factor dynamics in gene regulation for computational biology, representing an incremental improvement over existing differential equation models by handling discontinuities.
The paper tackled the problem of reverse-engineering transcriptional regulation in gene expression data by proposing a switched dynamical latent force model, which allowed exact inference over latent transcription factor activities and captured discrete changes, resulting in the ability to fit expression data and infer continuous-time transcription factor profiles as demonstrated on simulated and real datasets like E. coli and yeast.
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.