Changes in transcription factor levels, epigenetic status, splicing kinetics and mRNA degradation can each contribute to changes in the mRNA dynamics of a gene. We present a novel method to identify which of these processes is changed in cells in response to external signals or as a result of a diseased state. The method employs a mathematical model, for which the kinetics of gene regulation, splicing, elongation and mRNA degradation were estimated from experimental data of transcriptional dynamics. The time-dependent dynamics of several species of adipose differentiation-related protein (ADRP) mRNA were measured in response to ligand activation of the transcription factor peroxisome proliferator-activated receptor δ (PPARδ). We validated the method by monitoring the mRNA dynamics upon gene activation in the presence of a splicing inhibitor. Our mathematical model correctly identifies splicing as the inhibitor target, despite the noise in the data.
Additional Metadata
THEME Life Sciences (theme 5)
Publisher Oxford Open
Persistent URL dx.doi.org/10.1093/nar/gku1272
Journal Nucleic acids research
Project From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications
Citation
Rybakova, K.N, Tomaszewska, A, van Mourik, S, Blom, J.G, Westerhoff, H.V, Carlberg, C, & Bruggeman, F.J. (2014). Tracing the molecular basis of transcriptional dynamics in noisy data by using an experiment-based mathematical model . Nucleic acids research. doi:10.1093/nar/gku1272