This program is targeted at graduate students, post-docs, faculty and industry researchers with either of two backgrounds: training mainly in experimental biology (e.g. molecular biology, cell biology, genetics, biochemistry, physiology, etc.), or training mainly in mathematics, physics, engineering or computer science. For short, we like to call these backgrounds “wet” and “dry”, respectively.
Whatever your background, if you are reading this, you’ve probably had some specific experience—in the classroom, library or laboratory—that led you to wonder whether you could have a more effective and more satisfying research experience by reaching across the Wet/Dry divide.
Maybe you are a “Wet” individual who has seen the value of modeling in making biological systems understandable, or you find yourself confronted with a new massive dataset that you sense requires some sophisticated algorithmic or statistical thinking to be fully understood. Or maybe you are a “Dry” individual who senses that your quantitative skills ought to be of great value to biology, but you are unsure of how to apply them in a manner that solves real—and not just toy—problems.
Whatever the reason, and at whatever career stage you may be, some of the issues that are likely to stand in your way are deficits in knowledge (Wets are sometimes very far behind in quantitative skills; Drys sometimes have had only very superficial contact with real biological systems); language barriers; institutional training structures that hinder taking courses outside your discipline; fellowship funding mechanisms that favor uni-disciplinarity; and a lack of interdisciplinary role models available for mentoring. This course seeks to address each of these issues, through skills training, network building, and mentoring that continues beyond the three weeks you spend in the course itself.