# 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 · 2021 · $261,020

## Abstract

PROJECT SUMMARY
This application represents the first competitive renewal application for the University of Utah’s (UofU)
Interdisciplinary Training T32 Program in Computational Approaches to Diabetes and Metabolism Research
(CADMR T32). This program cross-trains both predoctoral and postdoctoral trainees in the computational and
mathematical sciences and in the biological basis of diabetes and obesity. Our program trainees gain the
expertise and leadership skills to apply computational and mathematical methods to complex biological
questions that will ultimately impact the prevention, treatment, and outcomes of people with diabetes and
related metabolic diseases. This training program consists of a combination of mentored research and career
development training, coursework, and extensive interactions with faculty and trainees across campus and
beyond. With unique resources such as the Center for Genomic Medicine, the Utah Population Database, the
Utah Genome Project, the Department of Biomedical Informatics, and the Diabetes and Metabolism Research
Center, the University of Utah has an exceptional training environment and has seen many dual-mentored
trainees establish independent research careers. The CADMR T32 will be led by two PIs who have
computational (Dr. Karen Eilbeck) and diabetes expertise (Dr. Simon Fisher). The proposed multidisciplinary
training program spans 13 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 38 MD and PhD
investigators (15 computational/mathematic mentors and 23 diabetes/metabolism mentors). The training
program will be overseen by a Steering Committee comprising the two Principal Investigators and four
committee members, 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 15 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:** 10172496
- **Project number:** 2T32DK110966-06
- **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:** 2021
- **Award amount:** $261,020
- **Award type:** 2
- **Project period:** 2016-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10172496

## Citation

> US National Institutes of Health, RePORTER application 10172496, University of Utah Interdisciplinary Training Program in Computational Approaches to Diabetes and Metabolism Research (2T32DK110966-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10172496. Licensed CC0.

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