# Modeling metabolic dynamics and regulation in biological systems

> **NIH NIH R35** · GEORGIA INSTITUTE OF TECHNOLOGY · 2020 · $314,800

## Abstract

Project Summary
The goal of the proposed work is to develop species-specific, dynamic, genome-scale metabolic
models for sixteen different yeast species. Metabolism is the set of processes that catalyze the
production of energy and cellular building blocks from the nutrients a cell takes up from the
environment. The building blocks and energy that metabolism creates are critical for all cellular
functions, from transcription to cell division. The structure of the metabolic network is surprisingly
well-conserved across evolution, yet different species exhibit different metabolic characteristics
through different regulation and utilization of the same reactions. Understanding how the same
set of reactions can be used to generate such different metabolic behaviors, and in turn
understanding some of the evolutionary underpinnings that lead to these different behaviors,
would be beneficial for a variety of biomedical and biotechnological tasks. In this work, we
leverage our recently-developed approaches for analysis and processing of metabolomics data
and for creation of genome-scale, dynamic linear models based on metabolomics data to study
the metabolism of sixteen different yeast species. We will measure the concentrations of
metabolites for all of these species in time-course experiments in response to environmental and
genetic perturbations. We will then use these data as the basis for creation of species-specific
metabolic models, using our novel metabolic modeling approach. We will also further improve on
those approaches to better enable the construction of these metabolic models. We will then use
all of these models in a comparative context to study which metabolic regulatory and dynamic
behaviors are conserved across evolution and which ones are more variable, and will use multiple
methods to validate the regulatory interactions that our model predicts.
This project will allow for fundamental insights into how metabolism behaves and is controlled
across closely and distantly related yeast species, including two opportunistic pathogens and two
sets of species exhibiting a metabolic phenotype very similar to that of cancer cells. Just like many
biological principles were first established in yeast species before being confirmed in higher
eukaryotes, a better understanding of the evolution of yeast metabolism will provide broad
principles of regulation that can be brought to bear on understanding and modeling human
metabolism. Moreover, successful completion of this work could provide a deeper understanding
of the evolution of pathogenicity and its functional underpinnings, as well as an understanding of
pathological changes in metabolism in human disease, such as diabetes or cancer.

## Key facts

- **NIH application ID:** 9951069
- **Project number:** 5R35GM119701-05
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Mark Philip-Walter Styczynski
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $314,800
- **Award type:** 5
- **Project period:** 2016-07-15 → 2022-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9951069

## Citation

> US National Institutes of Health, RePORTER application 9951069, Modeling metabolic dynamics and regulation in biological systems (5R35GM119701-05). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9951069. Licensed CC0.

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