  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.
