# CAREER: Elucidating and Leveraging the Connection Between Label-Free Biomedical Imaging and Gene Network Regulation

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · University of Arizona (AZ) · $625,000

## Abstract

Label-free biomedical optical imaging (LBI) is a technology used to study tissues by measuring interactions between tissue and light. This CAREER project will link biological activity to the signals detected by LBI.  The goal is to determine how biological changes, such as changes in how genes are expressed, affect the way light interacts with tissue.  The research will create new artificial intelligence (AI) methods to map the relationship between gene activity and LBI. The results could lead to better tools for diagnosing disease, studying tissue health, and improving pathology. In addition, the project includes a strong educational plan to prepare future leaders in bioengineering and AI. New learning activities will be created for students and the public that combine biology and AI. These activities will be designed to improve public understanding of AI and prepare students for modern science and technology careers. Overall, this project supports national interests by advancing leadership in biotechnology, optics, and AI.

A major knowledge gap exists in understanding how high-level biological changes influence interaction between light and tissue. The goal of this CAREER project is to establish a clear, measurable relationship between changes in gene networks and contrast observed in LBI. The research will measure tissue-wide gene expression patterns and determine how these patterns influence optical properties such as tissue fluorescence. First, the project will define how alterations in gene networks correspond to changes in LBI contrast across tissues. Second, new LBI image features and feature extraction algorithms will be developed to better represent transcriptomic signatures. Third, the relationship between gene expression and LBI will be incorporated into novel AI models to digitally analyze and classify tissues without chemical assays. This framework will help improve interpretation of optical imaging data and develop novel applications of AI in biote

## Key facts

- **NSF award ID:** 2539043
- **Awardee organization:** University of Arizona (AZ)
- **SAM.gov UEI:** ED44Y3W6P7B9
- **PI:** Travis W Sawyer
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** CAREER-Faculty Erly Career Dev, BIOPHOTONICS, IMAGING &SENSING
- **Estimated total:** $625,000
- **Funds obligated:** $625,000
- **Transaction type:** Standard Grant
- **Period:** 06/01/2026 → 05/31/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2539043

## Citation

> US National Science Foundation, Award 2539043, CAREER: Elucidating and Leveraging the Connection Between Label-Free Biomedical Imaging and Gene Network Regulation. Retrieved via AI Analytics 2026-06-24 from https://api.ai-analytics.org/grant/nsf/2539043. Licensed CC0.

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