Coarse-grained modeling of the metabolism of Saccharomyces cerevisiae

Nguyen, Viviana

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https://hdl.handle.net/2142/120194 Copy

Description

Title

Coarse-grained modeling of the metabolism of Saccharomyces cerevisiae

Author(s)

Nguyen, Viviana

Issue Date

2023-01-10

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

Lu, Ting

Doctoral Committee Chair(s)

Kim, Sangjin

Committee Member(s)

• Cooper, Lance
• Selvin, Paul

Department of Study

Physics

Discipline

Physics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Systems Biology
• Microbial Metabolism
• Saccharomyces Cerevisiae
• Mathematical Modeling
• Emergence

Language

eng

Abstract

Cellular metabolism is a complex process that requires coordination between multiple cellular processes. Although this complexity gives rise to some of the interesting emergent properties of metabolic networks, the complexity makes metabolism difficult to study and comprehend. In this dissertation, I present my work on developing and applying an integrated mathematical framework that is able to quantitatively capture and explain several of the emergent properties of the metabolism of Saccharomyces cerevisiae. In Chapter 2, I described the development our coarse-grained, system-level, and dynamic model of the metabolism of S. cerevisiae. To develop our mathematical framework, we first decomposed the system into three modules, namely metabolic reactions, signaling, and gene regulation. We then developed each of these modules individually before assembling the networks into one system-level description of S. cerevisiae metabolism. The parameters for our model were either estimated from literature or were manually fitted by simulating and reproducing several characteristic metabolic features of S. cerevisiae. In Chapter 3, I showed that our coarse-grained model is able capture several characteristic cellular behaviors of S. cerevisiae metabolism that were described in literature. Specifically, I showed that our model reproduces the Crabtree effect in cells that are cultivated in chemostat or batch conditions as well as the diauxic shift when cells are grown in glucose batch cultures. I also used our model to explain the origin of the diauxic lag time. Finally, I showed that our model recreates the differential growth and metabolic patterns that occur when cells are grown in rich media compared to minimal media. In Chapter 4, I demonstrated that our modular model design enables us to zoom in on specific pathways for a more detailed description of specific metabolite profiles. Specifically, we extended our framework from a one-precursor description to a four-precursor description. I showed that our extended model is able to capture the distinct patterns exhibited by the upper glycolytic and lower glycoltic metabolites in both chemostat experiments and batch fermentations. In Chapter 5, I showed how we combined modeling with experiment to test the hypothesis that S. cerevisiae optimizes the operation of its native cAMP/PKA and GCN2 signaling networks to maximize its growth rate. We found that, in the conditions tested, the growth rate of S. cerevisiae is always either maximal or nearly maximal, confirming that S. cerevisiae adjusts the concentrations of these signaling molecules to maximize its growth rate. In the final chapter, I summarize the significance and innovation of my thesis work. I also provide some potential model extensions and future directions for our modeling framework.

Graduation Semester

2023-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/120194

Copyright and License Information

Copyright 2023 Viviana Nguyen

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