© SHUNYU FAN/ISTOCKPHOTO.COMThe days of publishing a paper based on the cloning and sequencing of a single gene are long past. Now, the life sciences are dominated by increasingly bigger data sets, generated by high-throughput sequencing and large-scale proteomic screens. Interpreting such an abundance of information is a challenge for researchers with minimal computational training, spawning many successful collaborations among biologists, computer scientists, and mathematicians. But those with training in more than one of these fields are at a growing advantage in this interdisciplinary era, and faculty from disparate departments are joining forces to ensure that the next generation of scientists is appropriately prepared.
“We believe the traditional training programs either for math or biology are not sufficient to produce the type of students that can best work in the areas of mathematical, computational, and physical biology,” says Qing Nie, who helped launch the mathematical and computational biology (MCB) graduate program at the University of California, Irvine (UCI) in January 2007.
“Biology on the whole is becoming more and more quantitative, and not just from the big-data point of view in terms of genomics, but also in terms of the types of precise measurements that people are doing in the biological world,” agrees Daniel Zuckerman, associate director of a joint program in computational biology between the University of Pittsburgh (Pitt) and Carnegie Mellon University (CMU), launched in 2005.
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