# Advancing computational modeling of disease metabolism by integrating AI and systems biology

> **NIH NIH R35** · OREGON HEALTH & SCIENCE UNIVERSITY · 2024 · $387,579

## Abstract

Project summary
Metabolic pathways are crucial for cellular energy and function. Dysregulated metabolism is a distinctive feature of
various diseases, including cancer, diabetes, cardiovascular disease, inflammatory, and neurodegenerative diseases.
Given its integral role in disease pathology, accurately and comprehensively characterizing metabolic alterations offers
immense potential for enhancing our understanding of disease biology, improving clinical diagnostics, prevention, and
management. Specifically, such characterizations can lead to: (1) a deeper understanding of metabolic variation and
heterogeneity in disease tissue microenvironments; (2) the identification of novel drug targets or metabolic biomarkers
for early diagnosis and treatment optimization; and (3) the development of tailored nutritional and dietary
recommendations to enhance patient health quality. While significant progress has been made in studying metabolic
activities across species, a pivotal gap exists. On one hand, current analyses often fail to delineate the variations and
heterogeneities of metabolic activities in highly altered disease microenvironment. Existing methodologies tend to
present an averaged view of heterogeneous cell populations within tissues, overlooking the intricate metabolic
heterogeneity and exchanges that occur in complex tissues. This is especially concerning given that cells are known
to adapt their metabolism in response to various biochemical conditions. On the other hand, compared to other areas
like transcriptional regulation or immune response, there exists a marked gap in leveraging omics data to characterize
metabolic variations, necessitating tailored systems biology models and tools. In this MIRA project, we aim to address
these essential gaps by overcoming four challenges. First, we seek a systematic and data-driven approach to
characterize metabolic landscapes in disease systems, recognizing that metabolic pathways are multifaceted and
variations could span from flux to network topology. Second, we intend to harness the power of multi-omics data and
extant knowledge to bridge gaps in metabolic modeling, aiming for a thorough elucidation of the metabolic fluxome
and thereby achieving a holistic characterization of metabolic activities. Third, we aim to mechanistically decode the
functional roles of metabolic variations in diseases, emphasizing the heterogeneity and adaptability of metabolic
phenotypes. Finally, by applying our systematic research framework into our -omics testbeds covering several disease
types, we aim to pinpoint metabolic abnormalities, that could aid in precision medical interventions, and diet and
nutrient recommendations. Overall, our project promises to develop a suite of unparalleled computational tools: a
data-driven research framework built upon a novel biophysics-informed neural network, dynamic models for in-depth
metabolic analysis, cutting-edge statistical metrics, and a natural language processing tool t...

## Key facts

- **NIH application ID:** 10941152
- **Project number:** 1R35GM155028-01
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Sha Cao
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $387,579
- **Award type:** 1
- **Project period:** 2024-09-25 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10941152

## Citation

> US National Institutes of Health, RePORTER application 10941152, Advancing computational modeling of disease metabolism by integrating AI and systems biology (1R35GM155028-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10941152. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*