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.

yeast evolution

phenotype diversity

metabolism

genome-scale metabolic model

protein secretion

10:an,Kemigården 4, Chalmers.
Opponent: Steve Oliver, University of Cambridge, UK.

Author

Feiran Li

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Lu H†, Li F†, Yuan L†, Domenzain I, Yu R, Wang H, Li G, Chen Y, Ji B, Kerkhoven EJ and Nielsen J. Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection.

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

Li F, Chen Y, Qi Q, Wang Y, Yuan L, Huang M, Elsemman IE, Feizi A, Kerkhoven EJ and Nielsen J. Genome-scale modeling of the protein secretory pathway reveals novel targets for improved recombinant protein production by yeast.

Li F†, Yuan L†, Lu H, Li G, Chen Y, Engqvist MKM, Kerkhoven EJ and Nielsen J. Deep learning based kcat prediction enables improved enzyme constrained model reconstruction.

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.

Bioinformatics Services for Data-Driven Design of Cell Factories and Communities (DD-DeCaF)

European Commission (EC) (DeCaFGA686070), 2016-03-01 -- 2020-02-28.

Driving Forces

Sustainable development

Roots

Basic sciences

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Subject Categories

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

ISBN

978-91-7905-557-8

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5024

Publisher

Chalmers University of Technology

10:an,Kemigården 4, Chalmers.

Online

Opponent: Steve Oliver, University of Cambridge, UK.

More information

Latest update

10/8/2021