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Inference of gene regulation functions from dynamic transcriptome data.

Patrick Hillenbrand1, Kerstin C Maier2, Patrick Cramer2

  • 1Lehrstuhl für Theorie komplexer Biosysteme, Physik-Department, Technische Universität München, Garching, Germany.

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|September 23, 2016
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Summary

Researchers inferred gene regulation functions (GRFs) from natural gene expression changes in yeast. This method reveals how transcription factors and cyclins drive cell cycle oscillations.

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S. cerevisiaecell cyclecomputational biologygene regulationnetwork inferencequantitative biologysystems biology

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Area of Science:

  • Molecular Biology
  • Systems Biology
  • Computational Biology

Background:

  • Quantifying gene regulation requires relating transcription factor binding to mRNA synthesis rates.
  • Direct measurement of gene regulation functions (GRFs) is experimentally challenging due to difficulties in titrating inputs and measuring outputs simultaneously.

Purpose of the Study:

  • To develop and validate a method for inferring GRFs from endogenous cellular gene expression data.
  • To investigate the network architecture underlying transcriptional regulation during the yeast cell cycle.

Main Methods:

  • Utilized time-series mRNA synthesis rate data from synchronized yeast (*Saccharomyces cerevisiae*) cells over three cell cycles.
  • Developed an inference approach to estimate the functional form of transcription factor inputs influencing mRNA output.
  • Derived GRFs for target genes within the *CLB2* gene cluster active during G2/M phase.

Main Results:

  • Successfully inferred GRFs from natural variations in gene expression data.
  • Identified a network architecture that explains transcriptional oscillations during the cell cycle.
  • Demonstrated that while transcription factor networks can generate oscillations, cyclin oscillations are crucial for native cell cycle behavior.

Conclusions:

  • Inference of GRFs from endogenous expression data is a viable alternative to direct experimental measurement.
  • The study provides insights into the regulatory network controlling the yeast cell cycle.
  • Highlights the interplay between transcription factors and cyclins in driving cell cycle oscillations.