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Tools: Lifespan DB
Lifespan Observation Database

Search our database of lifespan measurements made across hundreds of publications.

YODA
Yeast Outgrowth Data Analyzer

Analyze growth curves for doubling time and relative survivals.

Stan Fields


Location: Seattle, WA USA
Website: http://www.gs.washington.edu/faculty/fields.htm
Lab Name: the Fields Lab
Lab Website: http://www.gs.washington.edu/faculty/fields.htm

The major focus of our laboratory has been the development and implementation of new technologies. Our motivation is that a novel technology can catalyze research across a spectrum of biological investigations, often leading to multiple applications beyond those initially envisioned. Technology development has led us to experimental questions that we had previously not explored, stimulated collaborations with computational biologists, biochemists and structural biologists, and provided opportunities to contribute to findings in medicine, as through studies of cancer-associated proteins, polyglutamine aggregation, aging, Toll-like receptors and malaria. Technology development is a challenging and oftentimes risky area of biology, but it is integral to scientific progress. Much of our research has centered on methods of protein analysis, although we have also put substantial effort into methodologies to analyze DNA, RNA and metabolites. We are able to pursue questions in multiple directions, initiating new projects as creative ideas emerge or findings from other labs impact our own work. Our philosophy on technology development is to pursue projects that can address important questions in basic biology or medicine, that can be readily applied by other labs, and that take advantage when possible of our expertise with the yeast Saccharomyces cerevisiae. Some members of our group have concentrated on wholly new technologies, some have adapted existing ones, and others have focused on addressing biological questions by applying the lab’s expertise in technology. Our group is typically 10 to 12 members, unified by an interest in methodology, the use of common reagent sets and genomic or proteomic platforms, and a sharing of computational tools.