  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.
