Tracing the molecular basis of transcriptional dynamics in noisy data by using an experiment-based mathematical model
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.
|THEME||Life Sciences (theme 5)|
|Journal||Nucleic acids research|
|Project||From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications|
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