Towards a comprehensive modeling framework for studying glucose repression in yeast
Doctoral thesis, 2022
The yeast Saccharomyces cerevisiae is an important model organism for human health and for industry applications as a cell factory. For both purposes, it has been an important organism for studying glucose repression. Glucose sensing and signaling is a complex biological system, where the SNF1 pathway is the main pathway responsible for glucose repression. However, it is highly interconnected with the cAMP-PKA, Snf3-Rgt2 and TOR pathways. To handle the complexity, mathematical modeling has successfully aided in elucidating the structure, mechanism, and dynamics of the pathway. In this thesis, I aim to elucidate what the effect of the interconnection of glucose repression with sensory and metabolic pathways in yeast is, specifically, how crosstalk influences the signaling cascade; what the main effects of nutrient signaling on the metabolism are and how those are affected by intrinsic stress, such as damage accumulation. Here, I have addressed these questions by developing new frameworks for mathematical modeling.
A vector based method for Boolean representation of complex signaling events is presented. The method reduces the amount of necessary nodes and eases the interpretation of the Boolean states by separating different events that could alter the activity of a protein. This method was used to study how crosstalk influences the signaling cascade.
To be able to represent a diverse biological network using methods suitable for respective pathways, we also developed two hybrid models. The first is demonstrating a framework to connect signaling pathways with metabolic networks, enabling the study of long-term signaling effects on the metabolism. The second hybrid model is demonstrating a framework to connect models of signaling and metabolism to growth and damage accumulation, enabling the study of how the long-term signaling effects on the metabolism influence the lifespan. This thesis represents a step towards comprehensive models of glucose repression. In addition, the methods and frameworks in this thesis can be applied and extended to other signaling pathways.
A vector based method for Boolean representation of complex signaling events is presented. The method reduces the amount of necessary nodes and eases the interpretation of the Boolean states by separating different events that could alter the activity of a protein. This method was used to study how crosstalk influences the signaling cascade.
To be able to represent a diverse biological network using methods suitable for respective pathways, we also developed two hybrid models. The first is demonstrating a framework to connect signaling pathways with metabolic networks, enabling the study of long-term signaling effects on the metabolism. The second hybrid model is demonstrating a framework to connect models of signaling and metabolism to growth and damage accumulation, enabling the study of how the long-term signaling effects on the metabolism influence the lifespan. This thesis represents a step towards comprehensive models of glucose repression. In addition, the methods and frameworks in this thesis can be applied and extended to other signaling pathways.
budding yeast
glucose repression
metabolism
mathematical modelling
signaling
Konferensrummet 10’an, Kemihuset våning 10, Kemigården 4.
Opponent: Peter Swain, University of Edinburgh, UK
Author
Linnea Österberg
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Robustness of Nutrient Signaling Is Maintained by Interconnectivity Between Signal Transduction Pathways
Frontiers in Physiology,;Vol. 9(2019)
Journal article
A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism
PLoS Computational Biology,;Vol. 17(2021)
Journal article
Österberg L, Welkenhuysen N, Persson S, Hohmann S, Cvijovic M. Localization and phosphorylation in the Snf1 network is controlled by two independent pathways
Multi-scale model suggests the trade-off between protein and ATP demand as a driver of metabolic changes during yeast replicative ageing
PLoS Computational Biology,;Vol. 18(2022)
Journal article
The choice of the objective function in flux balance analysis is crucial for predicting replicative lifespans in yeast
PLoS ONE,;Vol. 17(2022)p. e0276112-
Journal article
Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae
FEMS Yeast Research,;Vol. 22(2022)
Review article
Mathematical modelling of glucose sensing, signaling and metabolism in yeast.
The cells’ ability to sense and adapt to the environment is a result of complex interactions in the cell. Dysfunction of these complex interactions in human cells has been associated with diseases and states such as diabetes, cancer, and aging. In addition, it is also a crucial property to understand when utilizing cells for biotechnological applications such as microbial production of fuels and pharmaceuticals.
This thesis focuses on glucose signaling. Glucose is the preferred source of carbon for yeast. When a yeast cell encounter glucose, it changes its metabolism to facilitate growth. When the glucose is consumed, the cell adapts to be able to grow on other carbon sources. This ability is mediated trough a large network of reactions that senses the state of the environment as well as the state of the cell and adjust the metabolism accordingly. To understand the complex interactions, we use mathematical modelling. Three models were created to understand how different parts of the network cooperates, more specifically; how the glucose sensing and signaling pathways interacts with pathways sensing other nutrients; how the signaling affect metabolism and how the cells ability to sense and adapt impact the aging process. This work enabels improved predictions for models commonly used in the design of cell factories and presents a framework connecting the effect signaling imposes on metabolism to longevity traits and aging.
The cells’ ability to sense and adapt to the environment is a result of complex interactions in the cell. Dysfunction of these complex interactions in human cells has been associated with diseases and states such as diabetes, cancer, and aging. In addition, it is also a crucial property to understand when utilizing cells for biotechnological applications such as microbial production of fuels and pharmaceuticals.
This thesis focuses on glucose signaling. Glucose is the preferred source of carbon for yeast. When a yeast cell encounter glucose, it changes its metabolism to facilitate growth. When the glucose is consumed, the cell adapts to be able to grow on other carbon sources. This ability is mediated trough a large network of reactions that senses the state of the environment as well as the state of the cell and adjust the metabolism accordingly. To understand the complex interactions, we use mathematical modelling. Three models were created to understand how different parts of the network cooperates, more specifically; how the glucose sensing and signaling pathways interacts with pathways sensing other nutrients; how the signaling affect metabolism and how the cells ability to sense and adapt impact the aging process. This work enabels improved predictions for models commonly used in the design of cell factories and presents a framework connecting the effect signaling imposes on metabolism to longevity traits and aging.
Subject Categories
Biological Sciences
Other Mathematics
Bioinformatics and Systems Biology
Roots
Basic sciences
Areas of Advance
Life Science Engineering (2010-2018)
ISBN
978-91-7905-620-9
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5086
Publisher
Chalmers
Konferensrummet 10’an, Kemihuset våning 10, Kemigården 4.
Opponent: Peter Swain, University of Edinburgh, UK