Computing abundance constraints in Saccharomyces cerevisiae’s metabolism
Doktorsavhandling, 2019

The unicellular eukaryotic organism Saccharomyces cerevisiae (budding yeast) is routinely used for production of high-value chemical compounds in the biotechnology industry. To improve production yields, it is fundamental to understand cellular metabolism, i.e. all biochemical reactions that occur inside the cell. In the past 20 years, genome-scale metabolic models (GEMs) have risen as computational tools for simulating all possible metabolic phenotypes that the cell can attain, while respecting constraints such as mass balances and reaction reversibilities. However, the number of metabolic states bound to only those constraints is infinite; therefore, it becomes necessary to include additional condition-specific constraints. Moreover, we would like these constraints to reflect physical limitations inside the cell, avoiding arbitrary ad-hoc bounds.

In this thesis, approaches for including abundance constraints (i.e. constraints based on absolute abundances of different biomolecules) are evaluated in a GEM of S. cerevisiae. First, the GEM approach and how it has been used in S. cerevisiae is reviewed, identifying key areas for development. Afterwards, the concepts of sustainable model development and multi-layer experimental data generation are presented as foundation stones for constructing integrative analysis. Regarding the first concept, a systematic way of recording changes in a GEM using a version-controlled system is introduced, allowing reproducibility and open collaboration from the community. Regarding the second concept, a multi-omics dataset of yeast grown under different temperature, osmotic and ethanol stresses is presented and used throughout the thesis for studying metabolism.

The major part of this work focuses on the integration into GEMs of abundance data of two types of bio-molecules: lipids and enzymes. First, a method for integrating lipid requirements in an unbiased way (SLIMEr) is presented and implemented for yeast, to show that lipid metabolism can be re-arranged without spending high amounts of energy. Secondly, a method for adding so-called “enzyme constraints” into a GEM (GECKO) is developed. These enzyme constraints limit reaction rates by the absolute abundance of enzymes, and prove to be crucial for explaining yeast physiology and computing enzyme usage in metabolism. Thirdly, the quantification technique used for estimating enzyme abundances is analyzed in terms of accuracy and precision, and further improved by varying the normalization and scaling steps. Finally, GECKO is used on the stress dataset to create enzyme-constrained models of yeast representing each stress condition. This allows comparing the distribution of enzyme usage within and between conditions, highlighting enzymes that play an important role in the metabolic response to stress.

proteomics

lipidomics

flux balance analysis

Genome-scale modeling

Room KA, Kemigården 4, Chalmers
Opponent: Markus Herrgård, Danmarks Tekniske Universitet, Danmark

Författare

Benjamín José Sánchez

Chalmers, Biologi och bioteknik, Systembiologi

Genome scale models of yeast: towards standardized evaluation and consistent omic integration

Integrative Biology (United Kingdom),; Vol. 7(2015)p. 846-858

Artikel i vetenskaplig tidskrift

Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

Molecular Systems Biology,; Vol. 13(2017)p. Article no 935 -

Artikel i vetenskaplig tidskrift

Biotechnology, the exploitation of biological processes for the benefit of humanity, has become one of the main industries in our current economy. A big part of biotechnology relies on understanding biological microorganisms and how to tune them to best suit the corresponding applications. Metabolism, the interplay of biochemical reactions inside a cell to generate energy and components needed for growth, is an important layer of information for understanding microorganisms. In this thesis, I study metabolism of Saccharomyces cerevisiae (budding yeast), one of the most popular microorganisms in biotechnology, used for the production of bread, beer, wine, biofuels and many other biotechnology products.

As metabolism is very complex, the best way to study it is with the aid of computers. A common approach for doing this is by creating what is known as a genome-scale metabolic model (GEM), which is a mathematical representation of all reactions in metabolism. A problem that this method has is that it lacks information to be able to computationally simulate biological responses under certain experimental conditions. Important information that could be added are the abundances inside the cell of different biomolecules, such as lipids and proteins. In this thesis, I present methods for improving computational simulations of GEMs with so-called “abundance constraints”, together with novel observations about yeast biology.

Among the computational methods, I present i) a way of storing and updating GEMs so we keep track of every change we do; ii) a method for combining GEMs with intracellular levels of lipids; iii) a method for combining GEMs with enzymes, which are proteins that can catalyze metabolic reactions; and iv) an improved method for estimating protein levels based on experimental data.

Among other results, I show that i) lipid metabolism is very flexible, meaning that yeast is able to re-arrange it in different ways without spending too much energy; ii) the total enzyme content in yeast imposes an important constraint on metabolism, as the cell will be limited to certain options based on the amount of available enzyme; iii) specifically, when yeast has to grow fast, it will have to become less efficient in processing substrate; and iv) specific enzyme levels can create limitations in metabolism when the cell is grown under an environmental stress.

The results presented in this thesis show that by accounting for extra levels of information, computational approaches can be improved to better understand biology. Furthermore, the methods developed are expected to be useful for industrial applications of S. cerevisiae, as tools for guiding researchers to develop new strains with improved performance.

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

Europeiska kommissionen (Horisont 2020), 2016-03-01 -- 2020-02-28.

Drivkrafter

Hållbar utveckling

Fundament

Grundläggande vetenskaper

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

ISBN

978-91-7597-863-5

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4544

Utgivare

Chalmers tekniska högskola

Room KA, Kemigården 4, Chalmers

Opponent: Markus Herrgård, Danmarks Tekniske Universitet, Danmark

Mer information

Senast uppdaterat

2019-03-04