Center for Computational Molecular Biology

NIH Graduate Training Program

NIH-funded Predoctoral Training Program (T32) in Biological Data Science.

In this era of Big Data, building a successful and independently funded biomedical research program requires fluency in both biological data (experimental data generation, bioinformatics, and statistical inference) and theory relevant to living systems (analytical modeling, computational simulation, and evolutionanry theory). This dichotomy is challenging to address in doctoral training: biology students are rarely trained to develop or critique new quantitative methods, and quantitative students analyzing biological data rarely gain depth in biological data generation. There is an urgent need to curb fragmented efforts to address these challenges, and to instead develop a centralized community and training program focused on fostering Biological Data Scientists: scientists whose research leverages observed patterns in biological data to generate new models and hypotheses for biological processes and systems.


The objective of this 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 two core strengths: (1) generating and analyzing biological data, and (2) developing theoretical models for and testable hypotheses regarding biological processes. This centralized community at Brown University will be maintained by 28 engaged, crossdisciplinary faculty preceptors who will mentor four NIH-supported predoctoral trainees each year along with 4 Brown University-supported trainees each year (resulting in 40 Biological Data Scientists over 5 years) in a variety of didactic, research, and career development activities for one year. These activities will include a new year-long graduate seminar, crossdisciplinary research rotations, a program retreat for faculty and trainees, and a series of roundtable discussions focusing on professional development for interdisciplinary researchers. The resulting community will promote the development of skills essential for interdisciplinary biomedical research, including 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 science.

Research programs

The faculty preceptors' research programs cover multiple biological organisms, systems, and problems, ranging across evolutionary genetics, functional genomics, biological networks, molecular biology of aging, developmental robustness, biomedical informatics, regulation of immunity, and biological physics. Further, the preceptors have a combined annual research funding base of over $12 million in direct costs, offering a strong foundation to bolster this innovative training program. This training program will yield investigators equipped to extract new insights into living systems from complex biological datasets.


Investigating the nature and behavior of living systems increasingly involves the analysis of large-scale and multi- scale datasets; for example, many biomedical studies now routinely integrate -omic and cellular data with popu- lation-level covariates or an organism's phenotypic responses. This predoctoral training program will yield mul- tiple cohorts of rising investigators with fluency in both biological data and theory relevant to biological processes, via interdisciplinary didactic, research, and career development opportunities that foster a cohesive mentoring and training community at the faculty and student levels. This training program is relevant to NIGMS's mission because it will train a new generation of Biological Data Scientists, assuring the vitality and continued productivity of basic research as biological datasets expand in size, scale and complexity.

Faculty overseeing the T32

  • Sohini Ramachandran

    Director, Data Science Initiative, Professor of Biology, Department of Ecology, Evolution, and Organismal Biology, Associate Professor of Computer Science, Department of Computer Science
  • Bjorn Sandstede

    Chair, Division of Applied Mathematics, Royce Family Prof. of Teaching Excellence, Prof. of Applied Mathematics
  • Eliezer Upfal

    Rush C. Hawkins University Professor of Computer Science
  • Zhijin Wu

    Associate Professor of Biostatistics School of Public Health

About the Ph.D. program in Computational Biology