Coarse-grained modeling of microbial metabolism in model and non-model yeasts

Li, Yifei

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https://hdl.handle.net/2142/124618 Copy

Description

Title

Coarse-grained modeling of microbial metabolism in model and non-model yeasts

Author(s)

Li, Yifei

Issue Date

2024-01-08

Director of Research (if dissertation) or Advisor (if thesis)

Lu, Ting

Doctoral Committee Chair(s)

Lu, Ting

Committee Member(s)

• Rao, Christopher
• Tajkhorshid, Emad
• Zhao, Huimin

Department of Study

School of Molecular & Cell Bio

Discipline

Biophysics & Quant Biology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Coarse-graining
• Microbial Metabolism
• Mathematical Modeling
• Dynamic Modeling
• Saccharomyces Cerevisiae
• Complexity And Emergence
• Rhodotorula Toruloides

Language

eng

Abstract

Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of several individual components have been greatly elucidated, how these components orchestrated and how coordination of molecular events produces collective cellular behaviors are not well understood quantitatively. While existing models have provided valuable insights, they often concentrate on particular sub-systems. Here, I present my efforts in developing and applying a coarse-grained, systematic, and dynamic mathematical modeling approach that can integrate different sub-systems. Our framework assists in quantitatively understanding and predicting the physiology and metabolism of both model and non-model yeasts. In Chapter 2, I presented a Saccharomyces cerevisiae model. This model employs a coarse-grained approach to integrate the metabolic pathway, gene regulation, and signaling pathway into a cohesive framework. With only a few variables, our model mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, the diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. Additionally, its modular design allows for focused expansion into specific pathways. In Chapter 3, I demonstrated the expansion of the glycolytic pathway of the model introduced in Chapter 2. While one of the limitations of the coarse-grained approach is the lack of molecular details, the modular design of our model compensates for this deficiency and makes the model readily expandable based on specific needs. Our expanded four-precursor model is capable of reproducing the results of its base model presented in Chapter 2 with increased resolution. Additionally, we showed that the four-precursor model can enhance the prediction of diauxic lag time and differential growth under nutrient-altered environments due to the increased resolution. In Chapter 4, I illustrated another expansion of the base S. cerevisiae model introduced in Chapter 2. While the previous model considered the effect of glucose level only, this model can predict cellular behavior under different glucose and nitrogen levels. We validated the model’s capabilities by showcasing its ability to capture metabolic and gene expression profiles under varying degrees of nitrogen limitation. This sheds light on how nitrogen levels influence S. cerevisiae physiology, including the Crabtree effect and diauxic shift. Other than being able to zoom in on the specific pathway, the simplicity of our model makes it highly adaptable to study the metabolism of non-model yeasts. In Chapter 5, we adapted our base S. cerevisiae model to describe a non-model oleaginous yeast, Rhodotorula toruloides. Together, this study enhances our basic understanding of the quantitative physiology of both model and non-model yeasts, and offers valuable insights into yeast metabolic engineering for biotechnological applications.

Graduation Semester

2024-05

Type of Resource

Text

Handle URL

https://hdl.handle.net/2142/124618

Copyright and License Information

Copyright 2024 Yifei Li

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