# Development of data driven and AI empowered systems biology to study human diseases

> **NIH NIH R35** · OREGON HEALTH & SCIENCE UNIVERSITY · 2024 · $388,370

## Abstract

Project Summary
Systems biology models provide an effective way to study the functional impact of biological process within
complex disease system. Despite a plethora of knowledge on the differential equation-based systems biology
model have gained, there are still major gaps in raising dynamic models within the context of human diseases.
Essentially, the parameters involved in the non-linear dependencies are largely unknown under disease
conditions and the systems biology models are always within a reductionist paradigm, which can hardly
characterize the complicated disease system. The large amount of single-cell, spatial or tissue multi-omics data
obtained from disease tissue has been proven to be endowed with the potential to deliver information on a cell
functioning state and its underlying phenotypic switches. Hence, advanced systems biology models and
computational tools are in pressing need to empower reliable characterization of biological processes and their
functional roles in disease by using multi-omics data. Our preliminary data include (1) a new computational
method to approximate systems biology model using transcriptomics data, and (2) computational principles to
approximate dynamic system by using omics data, which form the methodology and theoretical foundations of
this project. In this MIRA project, I proposed to develop a suite of novel computational methods, systems biology
models and quantitative metrics to bring the following unmet capabilities: (1) A computational framework to
establish dynamic models using omics data, which will enable the following analyses to study a complex disease
system: (i) assessing sample-wise activity of biological processes; (ii) perturbation analysis to evaluate the
impacts of biological features or model structures to the system, which could serve as new drug targets, and (iii)
evaluating how the system evolve through disease progression; (2) A natural language processing-based
extraction of biological functions and relations to automatically establish context specific knowledge of system
structure and components from scientific literature datal; and (3) computational principles and theories of the
identifiability and mathematical representation of dynamic systems in omics data. By implementing these
methods into multi-omics data analysis, we plan to address the following outstanding biological questions: (i)
identification of molecular features with high impact to metabolic variations in different diseases, (ii) the role of
metabolism in fueling epigenetic regulation, (iii) transcriptional regulation of metabolism and other biological
processes, (iv) functional annotation of genetic variations, and (v) assessment of biochemical variations. We will
also develop novel knowledge representation and transfer of metabolic and other variations in pan-disease
analysis to aid in better understanding of the basic disease pathology and promote the precision medicine
research, including prediction and va...

## Key facts

- **NIH application ID:** 11173498
- **Project number:** 7R35GM150971-02
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Chi Zhang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $388,370
- **Award type:** 7
- **Project period:** 2023-08-15 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11173498

## Citation

> US National Institutes of Health, RePORTER application 11173498, Development of data driven and AI empowered systems biology to study human diseases (7R35GM150971-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/11173498. Licensed CC0.

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