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Nucleic Acids Res. 2015 Jan;43(1):153-61. doi: 10.1093/nar/gku1272. Epub 2014 Dec 3.

Tracing the molecular basis of transcriptional dynamics in noisy data by using an experiment-based mathematical model.

Author information

1
Molecular Cell Physiology, VU University Amsterdam, De Boelelaan 1087, NL-1081 HV Amsterdam, The Netherlands katja.rybakova@ucd.ie.
2
School of Medicine, Institute of Biomedicine, University of Eastern Finland, FI-70211 Kuopio, Finland.
3
Biometris, Plant Sciences Group, Wageningen University and Research Center, NL-6708 PB Wageningen, The Netherlands.
4
Life Sciences, Centre for Mathematics and Computer Science (CWI), NL-1098 XG Amsterdam, The Netherlands.
5
Molecular Cell Physiology, VU University Amsterdam, De Boelelaan 1087, NL-1081 HV Amsterdam, The Netherlands Manchester Centre for Integrative Systems Biology, CEAS, University of Manchester, Manchester M60 1QD, UK Synthetic Systems Biology, Swammerdam Institute for Life Sciences, University of Amsterdam, NL-1098 XH Amsterdam, The Netherlands.
6
Systems Bioinformatics, VU University Amsterdam, De Boelelaan 1087, NL-1081 HV Amsterdam, The Netherlands f.j.bruggeman@vu.nl.

Abstract

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.

PMID:
25477385
PMCID:
PMC4288170
DOI:
10.1093/nar/gku1272
[Indexed for MEDLINE]
Free PMC Article

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