This renewal application seeks continued support for the Predoctoral Training Program in Bioinformatics and Computational Biology at Boston University. Five predoctoral training slots per year are requested, to support trainees for the first year of residence. The Bioinformatics Program aims to prepare top researchers for careers in the molecular life sciences who will support the advances now underway in modern medicine. Based largely on the framework of the human genome sequence, these advances depend upon a battery of high-throughput techniques that generate enormous quantities of data that in turn reflect the complexities of biological networks. Full exploitation of this information requires computation-based, interdisciplinary efforts. The Program will inculcate the skills required in this new environment, including (a) an understanding of biological systems, (b) facility with computational methods and statistics, especially for network analysis, and (c) the ability to interact and communicate with colleagues from diverse disciplines. The curriculum includes a strong didactic foundation in computational methods, statistics, biological networks, and database development, along with in-depth examination of biological systems. Experiential learning is emphasized throughout the training (1) the annual International Workshop in Bioinformatics and Systems Biology, a joint undertaking of computational biology graduate training programs in Boston, Kyoto/Tokyo, and Berlin; (2) the Challenge Project, which offers teams of first-year trainees the opportunity to do original research on large-scale problems; (3) the Wet-Lab Experience, which introduces new trainees to state-of-the- curriculum. Program features include art experimental methods in the summer before they officially enter the program; (4) the annual Student- Organized Symposium; (5) three rotations; and (6) an annual Student Seminar at which trainees present their research. Emphasis is placed throughout in developing skills in reproducible and rigorous research, and in communicating science. Numerous mechanisms are employed to assist trainees in preparing for diverse career paths. A distinguished training faculty of 28 members is drawn from 16 departments from the Charles River and Medical campuses of Boston University. Areas of mentor research include genomics, proteomics, epigenetics, neurobiology, biological networks, synthetic biology, systems biology, statistical methods in bioinformatics, metabolomics, virology, immunology, the microbiome, and structural biology. Several mentors are engaged in translational bioinformatics . Co-advising of trainees, by computational and experimental mentors, is strongly encouraged. Students play a large role in defining their interdisciplinary research projects. 16 trainees who have been supported by this T32 grant currently are working towards the Ph.D.; they are a subset of the 54 trainees who currently are members of the Boston University Bioinformatics Ph.D. program.

Public Health Relevance

Students completing this program have already played vital roles in the acquisition and analysis of data vital to the diagnosis and treatment of infections, cancer and hereditary diseases, as well as in designing and producing new drugs and therapies. Their understanding of biological networks will enable them to help medical researchers cope with complex diseases, including diabetes, cardiovascular disease and neurological dysfunctions such as Alzheimer's and Parkinson's diseases.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
NIGMS Initial Review Group (TWD)
Program Officer
Resat, Haluk
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Boston University
Other Basic Sciences
Graduate Schools
United States
Zip Code
Logue, Mark W; Lancour, Daniel; Farrell, John et al. (2018) Targeted Sequencing of Alzheimer Disease Genes in African Americans Implicates Novel Risk Variants. Front Neurosci 12:592
Maoz, Ben M; Herland, Anna; FitzGerald, Edward A et al. (2018) A linked organ-on-chip model of the human neurovascular unit reveals the metabolic coupling of endothelial and neuronal cells. Nat Biotechnol 36:865-874
Lancour, Daniel; Naj, Adam; Mayeux, Richard et al. (2018) One for all and all for One: Improving replication of genetic studies through network diffusion. PLoS Genet 14:e1007306
Goldford, Joshua E; Lu, Nanxi; Baji?, Djordje et al. (2018) Emergent simplicity in microbial community assembly. Science 361:469-474
DiMucci, Demetrius; Kon, Mark; Segrè, Daniel (2018) Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks. mSystems 3:
Goldford, Joshua E; Hartman, Hyman; Smith, Temple F et al. (2017) Remnants of an Ancient Metabolism without Phosphate. Cell 168:1126-1134.e9