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Updated: Jun 12, 2026

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Published on: August 10, 2017

Detecting separate time scales in genetic expression data.

David A Orlando¹, Siobhan M Brady, Thomas M A Fink

¹Department of Biology and IGSP Center for Systems Biology, Duke University, Durham, NC, USA.

|June 23, 2010

Summary

This summary is machine-generated.

This study introduces a new method to analyze gene expression data, successfully identifying biological processes occurring at different time scales in yeast and plants. This approach helps uncover complex biological functions from system-wide measurements.

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Last Updated: Jun 12, 2026

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Published on: August 10, 2017

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Area of Science:

Systems Biology
Genomics
Developmental Biology

Background:

Biological processes operate across diverse and concurrent time scales.
System-wide gene expression data captures simultaneous biological events.
Distinguishing these processes and their time scales from data is a significant challenge, especially when unknown.

Purpose of the Study:

To develop a statistically rigorous method for detecting multiple time scales in time-series gene expression data.
To identify temporally shifted expression patterns between replicate datasets.

Main Methods:

Developed a flexible statistical method to detect time scales in gene expression data.
Applied the method to Saccharomyces cerevisiae cell-cycle and Arabidopsis thaliana root developmental datasets.

Main Results:

The method successfully detected processes operating on several different time scales in both datasets.
Identified associations between specific time scales and particular biological functions.
Detected spatiotemporal modules indicating multiple biological processes in Arabidopsis root and yeast.

Conclusions:

The developed method can identify biological processes acting at distinct time scales.
This approach enables the identification of multi-time scale biological processes in various organisms using large-scale expression datasets.