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Journal Article

Inference of gene regulation functions from dynamic transcriptome data.

MPS-Authors
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Maier,  K. C.
Department of Molecular Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Cramer,  P.
Department of Molecular Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Fulltext (public)

2347618.pdf
(Publisher version), 6MB

Supplementary Material (public)

2347618_Suppl.pdf
(Supplementary material), 15MB

Citation

Hillenbrand, P., Maier, K. C., Cramer, P., & Gerland, U. (2016). Inference of gene regulation functions from dynamic transcriptome data. eLife, 5: e12188. doi:10.7554/eLife.12188.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-74AC-5
Abstract
To quantify gene regulation, a function is required that relates transcription factor binding to DNA (input) to the rate of mRNA synthesis from a target gene (output). Such a 'gene regulation function' (GRF) generally cannot be measured because the experimental titration of inputs and simultaneous readout of outputs is difficult. Here we show that GRFs may instead be inferred from natural changes in cellular gene expression, as exemplified for the cell cycle in the yeast S. cerevisiae. We develop this inference approach based on a time series of mRNA synthesis rates from a synchronized population of cells observed over three cell cycles. We first estimate the functional form of how input transcription factors determine mRNA output and then derive GRFs for target genes in the clb2 gene cluster that are expressed during G2/M phase. Systematic analysis of additional GRFs suggests a network architecture that rationalizes transcriptional cell cycle oscillations. We find that a transcription factor network alone can produce oscillations in mRNA expression, but that additional input from cyclin oscillations is required to arrive at the native behaviour of the cell cycle oscillator.