# Mathametical modeling of cell fate transitions regulated by ultra-feedbacks

> **NIH NIH R01** · UNIVERSITY OF TENNESSEE KNOXVILLE · 2020 · $200,000

## Abstract

Cell fate transition (conversion between cell types) is a fundamental process critical for development and
disease progression. Gene regulatory networks controlling cell fate transitions often involve positive
feedback loops. Recent data suggest that highly interconnected positive feedback loops (defined as ultra-
feedback circuit in this proposal) have additional functions, but the current understanding of these
networks is incomplete, partly due to the lack of theories and mathematical methods to analyze such
complex circuits. Epithelial-mesenchymal transition (EMT), a process in which rigid epithelial cells
convert to motile mesenchymal forms, is an example of cell fate transitions that are regulated by ultra-
feedback circuits. EMT occurs in both normal and pathological conditions such as metastasis. Recent
discoveries suggest two complex cellular properties that make EMT difficult to understand intuitively: the
formation of multiple intermediate EMT states and the partial reversibility of EMT. The functions of the
ultra-feedback circuits in regulating the two cellular properties are yet to be defined. The goal of the
proposed study is to gain deeper understanding of these properties of EMT by developing new methods,
models and theories to characterize the ultra-feedback circuits. We will combine real algebraic geometry,
stability analysis and numerical methods to identify stable steady states that arise from ultra-feedback
systems, and we will apply the method to analyze the EMT spectrum of cell types. We will quantify partially
reversible EMT with a new theoretical framework based on information theory and dynamical systems.
Theory driven simulations and experiments will be performed to examine how ultra-feedback circuits
control reversibility. We will characterize the roles of ultra-feedback circuits in cell motility and proliferation
during EMT using multiscale modeling and live-cell imaging. The proposal brings about new methods to
analyze a large, emerging family of dynamical systems containing a wide range of network structures, a
new theoretical framework for understanding information transmission and retainment, and a new
multiscale modeling framework for systems with complex state transitions and multiple sources of
stochasticity. The proposed study addresses fundamental questions about the interplay between two
important and emerging properties of EMT (its multistate nature and its restricted reversibility) with
mathematical innovations, and it will provide critical insights into gene regulations of cell fate transitions
during development and disease progression. The success of the project will lead to new quantitative
information of EMT and new concepts for better understanding EMT properties and for analyzing other cell
fate transitions involving ultra-feedback circuits.

## Key facts

- **NIH application ID:** 10133773
- **Project number:** 1R01GM140462-01
- **Recipient organization:** UNIVERSITY OF TENNESSEE KNOXVILLE
- **Principal Investigator:** Tian Hong
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $200,000
- **Award type:** 1
- **Project period:** 2020-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10133773

## Citation

> US National Institutes of Health, RePORTER application 10133773, Mathametical modeling of cell fate transitions regulated by ultra-feedbacks (1R01GM140462-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10133773. Licensed CC0.

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