Inference of gene regulation functions from dynamic transcriptome data.
Patrick HillenbrandKerstin C MaierPatrick CramerUlrich GerlandPublished in: eLife (2016)
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.
Keyphrases
- cell cycle
- transcription factor
- genome wide identification
- single cell
- gene expression
- genome wide
- cell proliferation
- working memory
- rna seq
- dna methylation
- dna binding
- induced apoptosis
- binding protein
- cell cycle arrest
- single molecule
- circulating tumor
- cell therapy
- big data
- electronic health record
- endoplasmic reticulum stress
- oxidative stress
- cell free
- signaling pathway
- heat shock