# University of Utah Interdisciplinary Training Program in Computational Approaches to Diabetes and Metabolism Research

> **NIH NIH T32** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $257,583

## Abstract

PROJECT SUMMARY
There is a broad consensus that obesity, diabetes, and its many complications represent one of the major
public health crises of the 21st century in the United States and globally. Biomedical research efforts aimed at
characterization of the etiology, diagnoses, prognosis, and outcomes of diabetes has increasingly generated
large amounts of complex data in many scientific fields including structural biology, informatics, metabolomics,
whole genome sequencing, proteomics, phenotypic datasets, electronic health records, and public health
datasets. Computational and mathematical methods in bioinformatics, clinical informatics, data visualization,
and mathematical modeling are needed to analyze and interpret the plethora of data to improve the lives of
people with diabetes. With unique resources such as the Utah Population Database, the Utah Genome Project,
the Department of Biomedical Informatics, and Utah's Diabetes and Metabolism Center, the University of Utah
has an exceptional training environment and has seen many dual-mentored trainees establish independent
research careers. In this application we propose to formally establish an NIDDK Interdisciplinary Training
Program in Computational Approaches to Diabetes and Metabolism Research at the University of Utah under
the leadership of Wendy Chapman, PhD, Chair of the Department of Biomedical Informatics, and Simon
Fisher, MD, PhD, Chief of the Division of Endocrinology and Co-Director of the Diabetes and Metabolism
Center at the University of Utah. The proposed multidisciplinary training program spans 18 departments at the
University. The goal of this interdisciplinary program is to prepare predoctoral and postdoctoral trainees to be
leaders in computational and mathematical methods and engage them in the analysis of large data sets
involving complex biological problems in diabetes, obesity, and metabolism. Each trainee will participate in a
two-year training program that includes a research project with a multidisciplinary mentoring committee,
didactic coursework, and professional development opportunities. Each trainee will receive dual mentorship
from both a computational and a biological mentor. The mentoring committees, tailored to each trainee's
research interests, will draw from a mentor pool of 55 MD and PhD investigators (26 computational/mathematic
mentors and 29 diabetes/metabolism mentors). The training program will be overseen by an executive
committee comprising the two Principal Investigators and five Co-Directors, all of whom are investigators with
strong track records of uncompromising commitment to mentoring trainees. We are requesting support for five
trainee positions (three predoctoral and two postdoctoral) to train a total of 12 scientists over 5 years. With this
unique interdisciplinary training experience, we expect our trainees to become world leaders in the application
of bioinformatics to diabetes, obesity, and metabolism research.

## Key facts

- **NIH application ID:** 9955255
- **Project number:** 5T32DK110966-05
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Karen Louise Eilbeck
- **Activity code:** T32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $257,583
- **Award type:** 5
- **Project period:** 2016-07-01 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9955255

## Citation

> US National Institutes of Health, RePORTER application 9955255, University of Utah Interdisciplinary Training Program in Computational Approaches to Diabetes and Metabolism Research (5T32DK110966-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9955255. Licensed CC0.

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