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Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

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Finding modulators of stochasticity levels by quantitative genetics.

Steffen Fehrmann¹, Gaël Yvert

¹Laboratoire de Biologie Moléculaire de la Cellule, Ecole Normale Superieure de Lyon, Lyon, France.

Methods in Molecular Biology (Clifton, N.J.)

|April 7, 2011

Summary

Explore wild yeast genetic variation to understand gene regulatory network stochasticity. This approach uses genetic crosses and genomic analysis to identify DNA polymorphisms influencing gene expression variability.

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Area of Science:

* Molecular Biology
* Genetics
* Systems Biology

Background:

* Saccharomyces cerevisiae (baker's yeast) is widely used in research, but typically only a few standard strains are studied.
* The vast genetic diversity within wild yeast strains remains largely untapped in academic research.
* Gene regulatory network (GRN) stochasticity, or noise, is crucial for cellular function but its molecular sources are not fully understood.

Purpose of the Study:

* To present a generalizable method for investigating the molecular basis of stochasticity in gene regulatory networks.
* To leverage the natural genetic variation present in wild Saccharomyces cerevisiae strains for this investigation.
* To identify genomic regions and DNA polymorphisms that modulate gene expression noise.

Main Methods:

* Cross two yeast strains (A and B) exhibiting different levels of stochasticity in a target gene regulatory network.
* Analyze progeny from the A × B cross using microarray or resequencing technologies for genotyping.
* Employ yeast genome manipulation tools to fine-map identified genomic regions and confirm the role of specific DNA polymorphisms.

Main Results:

* The described approach successfully identifies genomic regions associated with variations in gene regulatory network stochasticity.
* DNA polymorphisms within these regions are shown to be responsible for modulating gene expression noise.
* This method provides a framework for dissecting the genetic architecture of biological variability.

Conclusions:

* Wild yeast genetic variation is a valuable resource for understanding fundamental biological processes like gene regulatory network stochasticity.
* The presented methodology enables the identification and characterization of genetic modulators of noise in any biological network with robust readouts.
* This work opens new avenues for exploring the evolutionary and functional significance of gene expression variability.