Abstract Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.

/pdf/genome-scale-modeling-of-yeast-metabolism-retrospectives-and-2so629nj.pdf

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

Microbes are key to creating safe, edible and enriched fermented food products. This is largely achieved by their metabolism. Thus, the ability to understand the wiring of the complete cellular metabolism is critical to control the fermentation processes. Metabolic modelling is a useful tool for integration of large datasets to link genotype to phenotype. Here, we summarise how metabolic models are being used to address the challenges in safety, biotransformation and food enhancement in food-relevant settings. Finally, we discuss how metabolic modelling can be integrated to assess more complex scenarios such as microbial communities. Despite many remaining challenges, metabolic models hold a large potential for use in food microbiology. 

Use and limitations of genome-scale metabolic models in food microbiology

Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis

Stoichiometric ratios for biotics and xenobiotics capture effective metabolic coupling to re(de)fine biodegradation.

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues. 

Resource Allocation in Mammalian Systems

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here we developed a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions (16304 reactions in total). The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data revealed that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation - known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimization, providing a framework to understand metabolic adaptation in eukaryal cells.

/pdf/whole-cell-modeling-in-yeast-predicts-compartment-specific-5ds7qp15oa.pdf

Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies

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Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies