Constraint-based modeling of yeast metabolism and protein secretion
Doctoral thesis, 2021
Yeasts are extensively exploited as cell factories for producing alcoholic beverages, biofuels, bio-pharmaceutical proteins, and other value-added chemicals. To improve the performance of yeast cell factories, it is necessary to understand their metabolism. Genome-scale metabolic models (GEMs) have been widely used to study cellular metabolism systematically. However, GEMs for yeast species have not been equally developed. GEMs for the well-studied yeasts such as Saccharomyces cerevisiae have been updated several times, while most of the other yeast species have no available GEM. Additionally, classical GEMs only account for the metabolic reactions, which limits their usage to study complex phenotypes that are not controlled by metabolism alone. Thus, other biological processes can be integrated with GEMs to fulfill diverse research purposes.
In this thesis, the GEM for S. cerevisiae was updated to the latest version Yeast8, which serves as the basic model for the remaining work of the thesis including two dimensions: 1) Yeast8 was used as a template for generating GEMs of other yeast species/strains, and 2) Yeast8 was expanded to account for more biological processes. Regarding the first dimension, strain-specific GEMs for 1,011 S. cerevisiae isolates from diverse origins and species-specific GEMs for 343 yeast/fungi species were generated. These GEMs enabled explore the phenotypic diversity of the single species from diverse ecological and geographical origins, and evolution tempo among diverse yeast species. Regarding the second dimension, other biological processes were formulated within Yeast8. Firstly, Yeast8 was expanded to account for enzymatic constraints, resulting in enzyme-constrained GEMs (ecGEMs). Secondly, Yeast8 was expanded to the model CofactorYeast by accounting for enzyme cofactors such as metal ions, which was used to simulate the interaction between metal ions and metabolism, and the cellular responses to metal ion limitation. Lastly, Yeast8 was expanded to include the protein synthesis and secretion processes, named as pcSecYeast. pcSecYeast was used to simulate the competition of the recombinant protein with the native secretory-pathway-processed proteins. Besides that, pcSecYeast enabled the identification of overexpression targets for improving recombinant protein production.
When developing these complex models, issues were identified among which the lack of enzyme turnover rates, i.e., kcatvalues, needs to be solved. Accordingly, a machine learning method for kcat prediction and automated incorporation into GEMs were developed, facilitating the generation of functional ecGEMs in a large scale.
In this thesis, the GEM for S. cerevisiae was updated to the latest version Yeast8, which serves as the basic model for the remaining work of the thesis including two dimensions: 1) Yeast8 was used as a template for generating GEMs of other yeast species/strains, and 2) Yeast8 was expanded to account for more biological processes. Regarding the first dimension, strain-specific GEMs for 1,011 S. cerevisiae isolates from diverse origins and species-specific GEMs for 343 yeast/fungi species were generated. These GEMs enabled explore the phenotypic diversity of the single species from diverse ecological and geographical origins, and evolution tempo among diverse yeast species. Regarding the second dimension, other biological processes were formulated within Yeast8. Firstly, Yeast8 was expanded to account for enzymatic constraints, resulting in enzyme-constrained GEMs (ecGEMs). Secondly, Yeast8 was expanded to the model CofactorYeast by accounting for enzyme cofactors such as metal ions, which was used to simulate the interaction between metal ions and metabolism, and the cellular responses to metal ion limitation. Lastly, Yeast8 was expanded to include the protein synthesis and secretion processes, named as pcSecYeast. pcSecYeast was used to simulate the competition of the recombinant protein with the native secretory-pathway-processed proteins. Besides that, pcSecYeast enabled the identification of overexpression targets for improving recombinant protein production.
When developing these complex models, issues were identified among which the lack of enzyme turnover rates, i.e., kcatvalues, needs to be solved. Accordingly, a machine learning method for kcat prediction and automated incorporation into GEMs were developed, facilitating the generation of functional ecGEMs in a large scale.
protein secretion
metabolism
yeast evolution
genome-scale metabolic model
phenotype diversity
Author
Feiran Li
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
Nature Communications,;Vol. 10(2019)
Journal article
Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection
Molecular Systems Biology,;Vol. 17(2021)
Journal article
Yeast optimizes metal utilization based on metabolic network and enzyme kinetics
Proceedings of the National Academy of Sciences of the United States of America,;Vol. 118(2021)
Journal article
Genome-scale modeling of yeast metabolism: retrospectives and perspectives
FEMS Yeast Research,;Vol. 22(2022)
Review article
Deep learning-based k(cat) prediction enables improved enzyme-constrained model reconstruction
Nature Catalysis,;Vol. In Press(2022)
Journal article
Cell is the fundamental unit of living organisms, which operates in organized interactions of a massive number of biomolecules such as proteins and metabolites. Understanding how the interaction would give rise to cell behavior is of high interest. In the meantime, as more organisms are identified, comparative analysis among organisms also becomes an interest. Mathematic models are promising in systematically analyzing interactions within one organism and providing mechanistic insight into versatile potential among multiple organisms.
Metabolism is one of the most critical processes in the living cell, which represents the interconversion of chemical compounds required to achieve cell growth. Mathematic models have been built to investigate metabolism, and genome-scale metabolic models (GEMs) are one of the most utilized models. In this thesis, I built many GEMs for different types of yeasts, such as baker’s yeast (Saccharomyces cerevisiae), heat-tolerant yeast (Kluyveromyces marxianus), lipid-producing yeast (Yarrowia lipolytica), and pathogenic yeast (Candida glabrata), which have been widely used in the production of alcoholic beverages, biofuels, bio-pharmaceutical proteins, and other value-added chemicals. Simulating with the GEMs enabled identifying the difference among yeast strains and species and uncovering the mechanism behind the long history of evolution.
To further improve the model performance, two types of efforts were done in this thesis. First, I expanded the GEMs to account for more biological processes, including enzyme cofactor binding and protein secretion, which enabled models to predict more cellular behaviors such as response to iron deficiency and overproduction of recombinant proteins. Second, I developed a machine learning approach to determine a key model parameter, i.e., enzyme turnover rate, which improved the predictive accuracy of the models.
In this thesis, there are 1,699 models generated, which serve as comprehensive reconstructions of yeast metabolism, enabling a deep understanding of yeast cells.
Metabolism is one of the most critical processes in the living cell, which represents the interconversion of chemical compounds required to achieve cell growth. Mathematic models have been built to investigate metabolism, and genome-scale metabolic models (GEMs) are one of the most utilized models. In this thesis, I built many GEMs for different types of yeasts, such as baker’s yeast (Saccharomyces cerevisiae), heat-tolerant yeast (Kluyveromyces marxianus), lipid-producing yeast (Yarrowia lipolytica), and pathogenic yeast (Candida glabrata), which have been widely used in the production of alcoholic beverages, biofuels, bio-pharmaceutical proteins, and other value-added chemicals. Simulating with the GEMs enabled identifying the difference among yeast strains and species and uncovering the mechanism behind the long history of evolution.
To further improve the model performance, two types of efforts were done in this thesis. First, I expanded the GEMs to account for more biological processes, including enzyme cofactor binding and protein secretion, which enabled models to predict more cellular behaviors such as response to iron deficiency and overproduction of recombinant proteins. Second, I developed a machine learning approach to determine a key model parameter, i.e., enzyme turnover rate, which improved the predictive accuracy of the models.
In this thesis, there are 1,699 models generated, which serve as comprehensive reconstructions of yeast metabolism, enabling a deep understanding of yeast cells.
Bioinformatics Services for Data-Driven Design of Cell Factories and Communities (DD-DeCaF)
European Commission (EC) (EC/H2020/686070), 2016-03-01 -- 2020-02-28.
Driving Forces
Sustainable development
Subject Categories
Biochemistry and Molecular Biology
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
Roots
Basic sciences
Infrastructure
C3SE (Chalmers Centre for Computational Science and Engineering)
ISBN
978-91-7905-557-8
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5024
Publisher
Chalmers