Stanford University has a strong record of training both the biomedical sciences and in computer science/engineering. The new NIH roadmap stresses the importance of training the next generation of scientists with quantitative and computational skills. With the increased demand for scientists with credentials in biocomputation, we have an opportunity to increase our capacity for training at this intersection. The Stanford Center for Biomedical Computation has participating faculty from the departments of genetics, structural biology, biochemistry, medicine, radiology, mechanical engineering, electrical engineering, bioengineering and computer science. It is affiliated closely with the interdisciplinary training program in biomedical informatics. In our first three years, we have successfully created a culture of biocomputation science that crosses across these fields, so that students can get dual-training in computation/informatics as well as in their primary discipline. In this proposal, we outline a proposal to continue this training grant, in order to increase the impact of computing in biomedical research. We target biocomputation students working broadly in six areas:: (1) structural and functional genomics, (2) biomechanical simulation, (3) computer assisted interventions and robotics, (4) image acquisition and processing, (5) computer-assisted instruction and networked education, and (6) informatics, data modeling, and statistics. In addition to dual research mentorship, training mechanisms include cross-disciplinary courses that teach fundamentals of biomedicine to technical graduate students, and teach fundamentals of these technical fields to biomedical graduate students. We also have symposia and journal clubs designed to encourage transfer of knowledge between biomedical computation students across disciplinary boundaries. The creation of a biocomputation laboratory on two floors of the new Clark Center for Biomedical Engineering and Science further serves to create a coherent community of biocomputation scientists at Stanford. The training grant will therefore fund disciplinary degree graduate students for an average of 3 years, and provide mechanisms for creating a cadre of young, well-qualified scientists equipped with the quantitative and computational tools needed to tackle the scientific challenges that arise in the 21st century.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
National Institute of General Medical Sciences Initial Review Group (BRT)
Program Officer
Li, Jerry
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Stanford University
Internal Medicine/Medicine
Schools of Medicine
United States
Zip Code
Faruque, Jessica; Rubin, Daniel L; Beaulieu, Christopher F et al. (2013) Modeling perceptual similarity measures in CT images of focal liver lesions. J Digit Imaging 26:714-20
Wong, J; Göktepe, S; Kuhl, E (2013) Computational modeling of chemo-electro-mechanical coupling: a novel implicit monolithic finite element approach. Int J Numer Method Biomed Eng 29:1104-33
John, Chand T; Anderson, Frank C; Higginson, Jill S et al. (2013) Stabilisation of walking by intrinsic muscle properties revealed in a three-dimensional muscle-driven simulation. Comput Methods Biomech Biomed Engin 16:451-62
John, Chand T; Seth, Ajay; Schwartz, Michael H et al. (2012) Contributions of muscles to mediolateral ground reaction force over a range of walking speeds. J Biomech 45:2438-43
Abernethy, Neil F; DeRimer, Kathy; Small, Peter M (2011) Methods to identify standard data elements in clinical and public health forms. AMIA Annu Symp Proc 2011:19-27
Markova-Raina, Penka; Petrov, Dmitri (2011) High sensitivity to aligner and high rate of false positives in the estimates of positive selection in the 12 Drosophila genomes. Genome Res 21:863-74
Tepole, Adrián Buganza; Ploch, Christopher Joseph; Wong, Jonathan et al. (2011) Growing skin: A computational model for skin expansion in reconstructive surgery. J Mech Phys Solids 59:2177-2190
Fox, Melanie D; Delp, Scott L (2010) Contributions of muscles and passive dynamics to swing initiation over a range of walking speeds. J Biomech 43:1450-5
Chang, Catie; Cunningham, John P; Glover, Gary H (2009) Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage 44:857-69
Chang, Catie; Thomason, Moriah E; Glover, Gary H (2008) Mapping and correction of vascular hemodynamic latency in the BOLD signal. Neuroimage 43:90-102

Showing the most recent 10 out of 21 publications