Model-based interpretation of high-throughput biological data to understand RNA degradation

Temporal omics data provide unprecedented views of cellular inner workings. They account for the molecular responses of living organisms to perturbations, resulting from regulatory mechanisms that ensure adaptation and survival. Statistical modelling tools are commonly used to analyse and interpret these data. However, achieving mechanistic insight requires moving from data-driven to mechanistic modelling approaches, by including prior knowledge of the relations between molecular responses and cell components. Mechanistic model parameters have a biological interpretation that provides insight into the molecular mechanisms involved. However, mechanistic modelling is a daunting task and examples of these models are scarce in the literature due to model non-linearities and large and sparse data sets.  I will discuss this problem in the context of the study of mRNA degradation using dynamic transcriptomics data sets and illustrate with concrete examples from the work of the BIOP group.