CHAPTER SEVEN
Achieving Interdisciplinary Training
Aglaring bottleneck in plant genome sciences is the paucity of biologists adequately trained in quantitative disciplines, such as mathematics, statistics, physics, and computer science. As plant biology moves toward becoming a predictive science, biologists trained in such fields will be in even greater demand. In the short term, biologists with training in computer science, rather than computer scientists with training in biology, may bring the most benefits, although the converse should be encouraged. Ideally, with the progression of the science, teams of biologists, bioinformaticists, and computer scientists will build on the strengths of the individual disciplines, with the bioinformaticists performing a facilitating role in the translation of data to research findings and in helping the community to develop the needed skills.
Therefore, we strongly urge the founding of training programs designed specifically to recruit students and postdoctoral scientists with degrees in the above disciplines into biology as a whole and plant biology in particular. These training programs need to be embedded in existing bioinformatics and genomics training environments that are demonstrably interdisciplinary and successful. Both individual fellowships and formal training grants need to be supported by the NPGI, as do in-depth training courses modeled on the Cold Spring Harbor courses. Training grants should be complemented by encouraging the development of both
semester-long and short courses in all aspects of bioinformatics, statistical genomics, evolutionary biology and computational biology. There is a long history of timely and focused training support in emerging disciplines among the agencies that support the NPGI, and we encourage the resurrection of past models designed to recruit people with new intellectual outlooks into plant biology. The need for different types of expertise in plant biology is particularly striking in database creation and maintenance and in the statistical analysis of complex data, such as data from mRNA-expression and protein-profiling experiments. Therefore, in combination with the overall bioinformatics goals outlined in the previous section, the NPGI should support interdisciplinary efforts to bridge the widening gap between biologists and scientists trained in quantitative disciplines.