Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments

Precise metabolic modeling in post-omics era: accomplishments and perspectives

Microbes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules.

Reducing batch effects in single cell chromatin accessibility measurements by pooled transposition with MULTI-ATAC

Large-scale scATAC-seq experiments are challenging because of their costs, lengthy protocols, and confounding batch effects. Several sample multiplexing technologies aim to address these challenges, but do not remove batch effects introduced when performing transposition reactions in parallel. We demonstrate that sample-to-sample variability in nuclei-to-Tn5 ratios is a major cause of batch effects and develop MULTI-ATAC, a multiplexing method that pools samples prior to transposition, as a solution. MULTI-ATAC provides high accuracy in sample classification and doublet detection while eliminating batch effects associated with variable nucleus-to-Tn5 ratio. We illustrate the power of MULTI-ATAC by performing a 96-plex multiomic drug assay targeting epigenetic remodelers in a model of primary immune cell activation, uncovering tens of thousands of drug-responsive chromatin regions, cell-type specific effects, and potent differences between matched inhibitors and degraders. MULTI-ATAC therefore enables batch-free and scalable scATAC-seq workflows, providing deeper insights into complex biological processes and potential therapeutic targets.

A framework for understanding collective microbiome metabolism

Microbiome metabolism underlies numerous vital ecosystem functions. Individual microbiome members often perform partial catabolism of substrates or do not express all of the metabolic functions required for growth. Microbiome members can complement each other by exchanging metabolic intermediates and cellular building blocks to achieve a collective metabolism. We currently lack a mechanistic framework to explain why microbiome members adopt partial metabolism and how metabolic functions are distributed among them. Here we argue that natural selection for proteome efficiency-that is, performing essential metabolic fluxes at a minimal protein investment-explains partial metabolism of microbiome members, which underpins the collective metabolism of microbiomes. Using the carbon cycle as an example, we discuss motifs of collective metabolism, the conditions under which these motifs increase the proteome efficiency of individuals and the metabolic interactions they result in. In summary, we propose a mechanistic framework for how collective metabolic functions emerge from selection on individuals.

The PRC2.1 Subcomplex Opposes G1 Progression through Regulation of CCND1 and CCND2

Progression through the G1 phase of the cell cycle is the most highly regulated step in cellular division. We employed a chemogenetic approach to discover novel cellular networks that regulate cell cycle progression. This approach uncovered functional clusters of genes that altered sensitivity of cells to inhibitors of the G1/S transition. Mutation of components of the Polycomb Repressor Complex 2 rescued proliferation inhibition caused by the CDK4/6 inhibitor palbociclib, but not to inhibitors of S phase or mitosis. In addition to its core catalytic subunits, mutation of the PRC2.1 accessory protein MTF2, but not the PRC2.2 protein JARID2, rendered cells resistant to palbociclib treatment. We found that PRC2.1 (MTF2), but not PRC2.2 (JARID2), was critical for promoting H3K27me3 deposition at CpG islands genome-wide and in promoters. This included the CpG islands in the promoter of the CDK4/6 cyclins CCND1 and CCND2, and loss of MTF2 lead to upregulation of both CCND1 and CCND2. Our results demonstrate a role for PRC2.1, but not PRC2.2, in antagonizing G1 progression in a diversity of cell linages, including CML, breast cancer and immortalized cell lines.

A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and on ProteomeXchange under the identifier PXD051318.

Biopurification using non-growing microorganisms to improve plant protein ingredients

Securing a sustainable global food supply for a growing population requires a shift toward a more plant-based diet. The application of plant-based proteins is therefore increasing, but unpleasant off-flavors complicate their use. Here, we screened 97 microorganisms for their potential to remove off-flavors in a process with limiting amounts of fermentable sugar. This allowed the production of a more neutral-tasting, purified food ingredient while limiting microbial growth and the production of typical fermentation end products. We demonstrate that various lactic acid bacteria (LAB) and yeasts remove "green" aldehydes and ketones. This conversion can be carried out in less than one hour in almond, pea, potato, and oat proteins. Heterofermentative LAB was best at aldehyde and ketone neutralization with minimum de novo formation of microbial volatiles such as ethylacetate (sweet, fruity) or alpha-diketones (butter- and cheese-like). While sensory properties were improved, changes in protein solubility, emulsification, foaming, and in vitro digestibility were limited.

Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0

Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.

Cross-regulation between proteome reallocation and metabolic flux redistribution governs bacterial growth transition kinetics

Bacteria need to adjust their metabolism and protein synthesis simultaneously to adapt to changing nutrient conditions. It's still a grand challenge to predict how cells coordinate such adaptation due to the cross-regulation between the metabolic fluxes and the protein synthesis. Here we developed a dynamic Constrained Allocation Flux Balance Analysis method (dCAFBA), which integrates flux-controlled proteome allocation and protein limited flux balance analysis. This framework can predict the redistribution dynamics of metabolic fluxes without requiring detailed enzyme parameters. We reveal that during nutrient up-shifts, the calculated metabolic fluxes change in agreement with experimental measurements of enzyme protein dynamics. During nutrient down-shifts, we uncover a switch of metabolic bottleneck from carbon uptake proteins to metabolic enzymes, which disrupts the coordination between metabolic flux and their enzyme abundance. Our method provides a quantitative framework to investigate cellular metabolism under varying environments and reveals insights into bacterial adaptation strategies.

Gene regulatory network topology governs resistance and treatment escape in glioma stem-like cells

Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing non-genetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupts acquired resistance in GBM.

The hierarchy of sugar catabolization in Lactococcus cremoris

IMPORTANCE

The availability of nutrients to microorganisms varies considerably between different environments, and changes can occur rapidly. As a general rule, a fast growth rate-typically growth on glucose-is associated with the repression of other carbohydrate utilization genes, but it is not clear to what extent catabolite repression is exerted by other sugars. We investigated the hierarchy of sugar utilization after substrate transitions in Lactococcus cremoris. For this, we determined the proteome and carbohydrate utilization capacity after growth on different sugars. The results show that the preparedness of cells for the utilization of "slower" sugars is not strictly determined by the growth rate. The data point to individual proteins relevant for various sugar transitions and suggest that the evolutionary history of the organism might be responsible for deviations from a strictly growth rate-related sugar catabolization hierarchy.

Trade-offs between the instantaneous growth rate and long-term fitness: Consequences for microbial physiology and predictive computational models

Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade-offs between cellular objectives such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long-term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework.

Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover numbers in metabolic models

Genome-scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number ( k cat ${k}_{\mathrm{cat}}$ ) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme-constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme-constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 k cat ${k}_{\mathrm{cat}}$ c), highly sensitive (1759 k cat ${k}_{\mathrm{cat}}$ ), and nonsensitive (2502 k cat ${k}_{\mathrm{cat}}$ ) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy.

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems

With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.

A Computational Toolbox to Investigate the Metabolic Potential and Resource Allocation in Fission Yeast

The fission yeast, Schizosaccharomyces pombe, is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the baker's yeast, Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of S. cerevisiae to generate a manually curated high-quality reconstruction of S. pombe's metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in S. pombe. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of S. pombe with S. cerevisiae, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behavior and the role of resource allocation in driving different strategies in fission yeast. IMPORTANCE Our understanding of microbial metabolism relies mostly on the knowledge we have obtained from a limited number of model organisms, and the diversity of metabolism beyond the handful of model species thus remains largely unexplored in mechanistic terms. Computational modeling of metabolic networks offers an attractive platform to bridge the knowledge gap and gain new insights into physiology of lesser-studied organisms. Here we showcase an example of successful knowledge transfer from the budding yeast Saccharomyces cerevisiae to a popular model organism in molecular and cell biology, fission yeast Schizosaccharomyces pombe, using computational models.

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.

Manganese Modulates Metabolic Activity and Redox Homeostasis in Translationally Blocked Lactococcus cremoris, Impacting Metabolic Persistence, Cell Culturability, and Flavor Formation

Manganese (Mn) is an essential trace element that is supplemented in microbial media with varying benefits across species and growth conditions. We found that growth of Lactococcus cremoris was unaffected by manganese omission from the growth medium. The main proteome adaptation to manganese omission involved increased manganese transporter production (up to 2,000-fold), while the remaining 10 significant proteome changes were between 1.4- and 4-fold. Further investigation in translationally blocked (TB), nongrowing cells showed that Mn supplementation (20 μM) led to approximately 1.5 X faster acidification compared with Mn-free conditions. However, this faster acidification stagnated within 24 h, likely due to draining of intracellular NADH that coincides with substantial loss of culturability. Conversely, without manganese, nongrowing cells persisted to acidify for weeks, albeit at a reduced rate, but maintaining redox balance and culturability. Strikingly, despite being unculturable, α-keto acid-derived aldehydes continued to accumulate in cells incubated in the presence of manganese, whereas without manganese cells predominantly formed the corresponding alcohols. This is most likely reflecting NADH availability for the alcohol dehydrogenase-catalyzed conversion. Overall, manganese influences the lactococcal acidification rate, and flavor formation capacity in a redox dependent manner. These are important industrial traits especially during cheese ripening, where cells are in a non-growing, often unculturable state.

IMPORTANCE In nature as well as in various biotechnology applications, microorganisms are often in a nongrowing state and their metabolic persistence determines cell survival and functionality. Industrial examples are dairy fermentations where bacteria remain active during the ripening phases that can take up to months and even years. Here we investigated environmental factors that can influence lactococcal metabolic persistence throughout such prolonged periods. We found that in the absence of manganese, acidification of nongrowing cells remained active for weeks while in the presence of manganese it stopped within 1 day. The latter coincided with the accumulation of amino acid derived volatile metabolites. Based on metabolic conversions, proteome analysis, and a reporter assay, we demonstrated that the manganese elicited effects were NADH dependent. Overall the results show the effect of environmental modulation on prolonged cell-based catalysis, which is highly relevant to non-growing cells in nature and biotechnological applications.

SALARECON connects the Atlantic salmon genome to growth and feed efficiency

Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds.

Atlantic salmon aquaculture generates billions of euros annually, but faces challenges of sustainability. Salmon are carnivores by nature, and fish oil and fish meal have become scarce resources in fish feed production. Novel, sustainable feedstuffs are being trialed hand in hand with studies of the genetics of growth and feed efficiency. This calls for a mathematical-biological framework to integrate data with understanding of the effects of novel feeds on salmon physiology and its interplay with genetics. We have developed the SALARECON model of the core salmon metabolic reaction network, linking its genome to metabolic fluxes and growth. Computational analyses show good agreement with observed growth, amino acid limitations, and feed efficiencies, illustrating the potential for in silico studies of potential feed mixtures. In particular, in silico screening of possible diets will enable more efficient animal experiments with improved knowledge gain. We have adopted best practices for test-driven development, virtual experiments to assay metabolic capabilities, revision control, and FAIR data and model management. This facilitates fast, collaborative, reliable development of the model for future applications in sustainable production biology.

Advances in constraint-based models: methods for improved predictive power based on resource allocation constraints

The concept of metabolic models with resource allocation constraints has been around for over a decade and has clear advantages even when implementation is relatively rudimentary. Nonetheless, the number of organisms for which such a model is reconstructed is low. Various approaches exist, from coarse-grained consideration of enzyme usage to fine-grained description of protein translation. These approaches are reviewed here, with a particular focus on user-friendly solutions that can introduce resource allocation constraints to metabolic models of any organism. The availability of k_cat data is a major hurdle, where recent advances might help to fill in the numerous gaps that exist for this data, especially for nonmodel organisms.

Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints

Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for α-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production.

Due to the complexity of the protein secretory pathway, strategy suitable for the production of a certain recombination protein cannot be generalized. Here, the authors construct a proteome-constrained genome-scale protein secretory model for yeast and show its application in the production of different misfolded or recombinant proteins.

Growth-rate-dependent and nutrient-specific gene expression resource allocation in fission yeast

Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Here, we determined the importance of the growth rate in explaining relative changes in protein and mRNA levels in the simple eukaryote Schizosaccharomyces pombe grown on non-limiting nitrogen sources. Although expression of half of fission yeast genes was significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production apart from RNA polymerase II-dependent transcription. Negatively correlated genes belonged mainly to the environmental stress response programme. Critically, metabolic enzymes, which represent ∼55-70% of the proteome by mass, showed mostly condition-specific regulation. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate-dependent and nutrient-specific gene expression.

Vitreoscilla Haemoglobin: A Tool to Reduce Overflow Metabolism

Overflow metabolism is a phenomenon extended in nature, ranging from microbial to cancer cells. Accumulation of overflow metabolites pose a challenge for large-scale bioprocesses. Yet, the causes of overflow metabolism are not fully clarified. In this work, the underlying mechanisms, reasons and consequences of overflow metabolism in different organisms have been summarized. The reported effect of aerobic expression of Vitreoscilla haemoglobin (VHb) in different organisms are revised. The use of VHb to reduce overflow metabolism is proposed and studied through flux balance analysis in E. coli at a fixed maximum substrate and oxygen uptake rates. Simulations showed that the presence of VHb increases the growth rate, while decreasing acetate production, in line with the experimental measurements. Therefore, aerobic VHb expression is considered a potential tool to reduce overflow metabolism in cells.

Multiscale models quantifying yeast physiology: towards a whole-cell model

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.

Understanding FBA Solutions under Multiple Nutrient Limitations

Genome-scale stoichiometric modeling methods, in particular Flux Balance Analysis (FBA) and variations thereof, are widely used to investigate cell metabolism and to optimize biotechnological processes. Given (1) a metabolic network, which can be reconstructed from an organism’s genome sequence, and (2) constraints on reaction rates, which may be based on measured nutrient uptake rates, FBA predicts which reactions maximize an objective flux, usually the production of cell components. Although FBA solutions may accurately predict the metabolic behavior of a cell, the actual flux predictions are often hard to interpret. This is especially the case for conditions with many constraints, such as for organisms growing in rich nutrient environments: it remains unclear why a certain solution was optimal. Here, we rationalize FBA solutions by explaining for which properties the optimal combination of metabolic strategies is selected. We provide a graphical formalism in which the selection of solutions can be visualized; we illustrate how this perspective provides a glimpse of the logic that underlies genome-scale modeling by applying our formalism to models of various sizes.