Predoctoral Training Program in Biological Data Science at Brown University - Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. Due to genomic technologies, electronic medical records, and digitized high-throughput experiment readouts, building an independent biomedical research career requires fluency in both biomedical data generation and data science methods for analyzing large-scale biomedical datasets. This dichotomy is challenging to address in doctoral training; biology doctoral students may take quantitative coursework with no emphasis on biomedical data, while computational biology students often focus on one data type as end users. The objective of our Predoctoral Training Program in Biological Data Science at Brown University is to turn “I-shaped” predoctoral students — with strength in one discipline — into “pi-shaped” Biological Data Scientists with fluency in two languages: (1) generating biological data motivated by questions across a range of scales and systems, and (2) developing quantitative methods for modeling and testing hypotheses using large-scale biomedical datasets. The established Biological Data Science training community at Brown University has 34 engaged faculty mentors across multiple disciplines who will jointly and actively mentor a steady state of six NIH-supported predoctoral trainees during the first and second years of doctoral study (resulting in >30 Biological Data Scientists over 5 years) in a variety of didactic, research, and mentoring activities, as well as in research and professional development events that continue to foster interdisciplinary community for senior trainees. These activities will include coursework in inference for genomics and molecular biology, laboratory practicums, computational workshops, a year-long second-year graduate seminar focused on extensive peer review of methods for analyzing biological data, an annual program retreat, and a series of professional development events for interdisciplinary researchers. The resulting community will promote the development of professional skills essential for interdisciplinary biological data science research, including an emphasis on the ability to communicate science to both broad and field-specific audiences, navigate interdisciplinary collaboration and grant applications, interview for academic and industry-based research careers, and conduct reproducible and open biological data science research. The faculty mentors’ research programs cover multiple biological organisms, systems, and problems, ranging across biological and neuronal networks, computational biophysics, computer vision and visualization, evolutionary and statistical genetics, functional genomics, host-pathogen interactions, the microbiome, and the molecular biology of aging. These activities will expand successful activities funded under a previous NIGMS T32 FOA (now in year 5 of funding), resulting in the persistence of 23 trainees in biomedical research. These trainees have secured external fellowships and produced 42 peer-reviewed publications and 8 preprints under review thus far. The mentors have a combined annual research funding base of over $24 million in direct costs this year, offering a strong foundation to bolster this innovative interdisciplinary training program.
