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by Bill Snyder | Thursday, Feb. 7, 2013, 10:02 AM
Budding yeast occupies a place of honor in the pantheon of human history. Saccharomyces cerevisiae has been used for baking and alcoholic fermentation for thousands of years.
Easily manipulated and cultured in the laboratory, this one-celled eukaryote (each cell has a nucleus) also has revealed much about fundamental cellular processes, including DNA replication, cell division and metabolism.
Today the lowly yeast is helping scientists develop methods for predicting how genes are turned on and off, or regulated, by signals from proteins and the environment. This information should aid efforts to better understand, and ultimately thwart, human diseases such as cancer.
In the Feb. 1 issue of Science magazine, Vanderbilt University’s Gregor Neuert, Ph.D., M.Eng., and colleagues report how they developed predictive models for complex gene expression and signaling pathways in S. cerevisiae by integrating single-cell experiments with stochastic analyses.
“This approach we propose here is very general,” said Neuert, assistant professor of Molecular Physiology and Biophysics. “It can be applied to any pathway in any cell.”
Stochastic, from the Greek word for aim or guess, recognizes that the natural world is to a great extent random. In biology, stochastic analyses account for the variability that time and environmental factors introduce into processes such as gene expression.
“In any cell type or organism, there are biologically significant differences from one cell to another even though they have exactly the same genome. These differences are missed entirely in cell population-based experiments,” Neuert said.
Genes will “turn on” if certain conditions are satisfied — if proteins called transcription factors that bind strongly enough to DNA are available in the right concentration at the right time, he explained.
Gene expression won’t occur in otherwise genetically identical cells that lack those conditions.
This may help explain why genetically identical cancer cells, even within the same patient, may behave differently and respond differently to treatment.
This study showed that detailed predictive models of gene expression are possible “if one has the ability to measure individual RNA molecules in single cells over time,” he said.
Neuert, a German-trained biophysicist who arrived at Vanderbilt last summer, led the study as a postdoctoral fellow at the Massachusetts Institute of Technology (MIT). He is co-first author with Brian Munsky, Ph.D., a postdoctoral fellow at the Los Alamos National Laboratory in New Mexico.
“Rigorously integrating dynamic single-molecule experiments in individual cells with single cell-based modeling approaches can lead to new insights that are otherwise difficult if not impossible to obtain by just doing modeling or just doing experiments alone,” he said.
Bill Snyder, (615) 322-4747
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