Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community.

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

CRI-SPA: a high-throughput method for systematic genetic editing of yeast libraries.

Biological functions are orchestrated by intricate networks of interacting genetic elements. Predicting the interaction landscape remains a challenge for systems biology and new research tools allowing simple and rapid mapping of sequence to function are desirable. Here, we describe CRI-SPA, a method allowing the transfer of chromosomal genetic features from a CRI-SPA Donor strain to arrayed strains in large libraries of Saccharomyces cerevisiae. CRI-SPA is based on mating, CRISPR-Cas9-induced gene conversion, and Selective Ploidy Ablation. CRI-SPA can be massively parallelized with automation and can be executed within a week. We demonstrate the power of CRI-SPA by transferring four genes that enable betaxanthin production into each strain of the yeast knockout collection (≈4800 strains). Using this setup, we show that CRI-SPA is highly efficient and reproducible, and even allows marker-free transfer of genetic features. Moreover, we validate a set of CRI-SPA hits by showing that their phenotypes correlate strongly with the phenotypes of the corresponding mutant strains recreated by reverse genetic engineering. Hence, our results provide a genome-wide overview of the genetic requirements for betaxanthin production. We envision that the simplicity, speed, and reliability offered by CRI-SPA will make it a versatile tool to forward systems-level understanding of biological processes.

Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae.

Heme is an oxygen carrier and a cofactor of both industrial enzymes and food additives. The intracellular level of free heme is low, which limits the synthesis of heme proteins. Therefore, increasing heme synthesis allows an increased production of heme proteins. Using the genome-scale metabolic model (GEM) Yeast8 for the yeast Saccharomyces cerevisiae, we identified fluxes potentially important to heme synthesis. With this model, in silico simulations highlighted 84 gene targets for balancing biomass and increasing heme production. Of those identified, 76 genes were individually deleted or overexpressed in experiments. Empirically, 40 genes individually increased heme production (up to threefold). Heme was increased by modifying target genes, which not only included the genes involved in heme biosynthesis, but also those involved in glycolysis, pyruvate, Fe-S clusters, glycine, and succinyl-coenzyme A (CoA) metabolism. Next, we developed an algorithmic method for predicting an optimal combination of these genes by using the enzyme-constrained extension of the Yeast8 model, ecYeast8. The computationally identified combination for enhanced heme production was evaluated using the heme ligand-binding biosensor (Heme-LBB). The positive targets were combined using CRISPR-Cas9 in the yeast strain (IMX581-HEM15-HEM14-HEM3-Δshm1-HEM2-Δhmx1-FET4-Δgcv2-HEM1-Δgcv1-HEM13), which produces 70-fold-higher levels of intracellular heme.

Multi Omics Applications in Biological Systems.

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Combining electromyographic and electrical impedance data sets through machine learning: A study in D2-mdx and wild-type mice.

INTRODUCTION/AIMS: Needle impedance-electromyography (iEMG) assesses the active and passive electrical properties of muscles concurrently by using a novel needle with six electrodes, two for EMG and four for electrical impedance myography (EIM). Here, we assessed an approach for combining multifrequency EMG and EIM data via machine learning (ML) to discriminate D2-mdx muscular dystrophy and wild-type (WT) mouse skeletal muscle. METHODS: iEMG data were obtained from quadriceps of D2-mdx mice, a muscular dystrophy model, and WT animals. EIM data were collected with the animals under deep anesthesia and EMG data collected under light anesthesia, allowing for limited spontaneous movement. Fourier transformation was performed on the EMG data to provide power spectra that were sampled across the frequency range using three different approaches. Random forest-based, nested ML was applied to the EIM and EMG data sets separately and then together to assess healthy versus disease category classification using a nested cross-validation procedure. RESULTS: Data from 20 D2-mdx and 20 WT limbs were analyzed. EIM data fared better than EMG data in differentiating healthy from disease mice with 93.1% versus 75.6% accuracy, respectively. Combining EIM and EMG data sets yielded similar performance as EIM data alone with 92.2% accuracy. DISCUSSION: We have demonstrated an ML-based approach for combining EIM and EMG data obtained with an iEMG needle. While EIM-EMG in combination fared no better than EIM alone with this data set, the approach used here demonstrates a novel method of combining the two techniques to characterize the full electrical properties of skeletal muscle.

Emergence of Phenotypically Distinct Subpopulations Is a Factor in Adaptation of Recombinant Saccharomyces cerevisiae under Glucose-Limited Conditions.

Cells cultured in a nutrient-limited environment can undergo adaptation, which confers improved fitness under long-term energy limitation. We have shown previously how a recombinant Saccharomyces cerevisiae strain, producing a heterologous insulin product, under glucose-limited conditions adapts over time at the average population level. Here, we investigated this adaptation at the single-cell level by application of fluorescence-activated cell sorting (FACS) and showed that the following three apparent phenotypes underlie the adaptive response observed at the bulk level: (i) cells that drastically reduced insulin production (23%), (ii) cells with reduced enzymatic capacity in central carbon metabolism (46%), and (iii) cells that exhibited pseudohyphal growth (31%). We speculate that the phenotypic heterogeneity is a result of different mechanisms to increase fitness. Cells with reduced insulin productivity have increased fitness by reducing the burden of the heterologous insulin production, and the populations with reduced enzymatic capacity of the central carbon metabolism and pseudohyphal growth have increased fitness toward the glucose-limited conditions. The results highlight the importance of considering population heterogeneity when studying adaptation and evolution. IMPORTANCE The yeast Saccharomyces cerevisiae is an attractive microbial host for industrial production and is used widely for manufacturing, e.g., pharmaceuticals. Chemostat cultivation mode is an efficient cultivation strategy for industrial production processes as it ensures a constant, well-controlled cultivation environment. Nevertheless, both the production of a heterologous product and the constant cultivation environment in the chemostat impose a selective pressure on the production organism, which may result in adaptation and loss of productivity. The exact mechanisms behind the observed adaptation and loss of performance are often unidentified. We used a recombinant S. cerevisiae strain producing heterologous insulin and investigated the adaptation occurring during chemostat growth at the single-cell level. We showed that three apparent phenotypes underlie the adaptive response observed at the bulk level in the chemostat. These findings highlight the importance of considering population heterogeneity when studying adaptation in industrial bioprocesses.

Design and pilot testing of a 26-gauge impedance-electromyography needle in wild-type and ALS mice.

INTRODUCTION/AIMS: Needle impedance-electromyography (iEMG) is a diagnostic modality currently under development that combines intramuscular electrical impedance with concentric electromyography (EMG) in a single needle. We designed, manufactured, and tested a prototype iEMG needle in a cohort of wild-type (WT) and SOD1G93A amyotrophic lateral sclerosis (ALS) mice to assess its ability to record impedance and EMG data. METHODS: A new six-electrode, 26-gauge, iEMG needle was designed, manufactured and tested. Quantitative impedance and qualitative "gestalt" EMG were performed sequentially on bilateral quadriceps of 16-wk-old SOD1G93A ALS (N = 6) and WT (N = 6) mice by connecting the needle first to an impedance analyzer (with the animal at rest) and then to a standard EMG system (with the animal fully under anesthesia to measure spontaneous activity and briefly during awakening to measure voluntary activity). The needle remained in the muscle throughout the measurement period. RESULTS: EMG data were qualitatively similar to that observed with a commercially available concentric EMG needle; fibrillation potentials were observed in 84% of the ALS mice and none of the WT mice; motor unit potentials were also readily identified. Impedance data revealed significant differences in resistance, reactance, and phase values between the two groups, with ALS animals having reduced reactance and resistance values. DISCUSSION: This work demonstrates the feasibility of a single iEMG needle conforming to standard dimensions of size and function. Further progress of iEMG technology for enhanced neuromuscular diagnosis and quantification of disease status is currently in development.

Using machine learning algorithms to enhance the diagnostic performance of electrical impedance myography.

INTRODUCTION/AIMS: We assessed the classification performance of machine learning (ML) using multifrequency electrical impedance myography (EIM) values to improve upon diagnostic outcomes as compared to those based on a single EIM value. METHODS: EIM data was obtained from unilateral excised gastrocnemius in eighty diseased mice (26 D2-mdx, Duchenne muscular dystrophy model, 39 SOD1G93A ALS model, and 15 db/db, a model of obesity-induced muscle atrophy) and 33 wild-type (WT) animals. We assessed the classification performance of a ML random forest algorithm incorporating all the data (multifrequency resistance, reactance and phase values) comparing it to the 50 kHz phase value alone. RESULTS: ML outperformed the 50 kHz analysis as based on receiver-operating characteristic curves and measurement of the area under the curve (AUC). For example, comparing all diseases together versus WT from the test set outputs, the AUC was 0.52 for 50 kHz phase, but was 0.94 for the ML model. Similarly, when comparing ALS versus WT, the AUCs were 0.79 for 50 kHz phase and 0.99 for ML. DISCUSSION: Multifrequency EIM using ML improves upon classification compared to that achieved with a single-frequency value. ML approaches should be considered in all future basic and clinical diagnostic applications of EIM.

A universal, high-performance ECG signal processing engine to reduce clinical burden.

BACKGROUND: Electrocardiogram (ECG) signal conditioning is a vital step in the ECG signal processing chain that ensures effective noise removal and accurate feature extraction. OBJECTIVE: This study evaluates the performance of the FDA 510 (k) cleared HeartKey Signal Conditioning and QRS peak detection algorithms on a range of annotated public and proprietary ECG databases (HeartKey is a UK Registered Trademark of B-Secur Ltd). METHODS: Seven hundred fifty-one raw ECG files from a broad range of use cases were individually passed through the HeartKey signal processing engine. The algorithms include several advanced filtering steps to enable significant noise removal and accurate identification of the QRS complex. QRS detection statistics were generated against the annotated ECG files. RESULTS: HeartKey displayed robust performance across 14 ECG databases (seven public, seven proprietary), covering a range of healthy and unhealthy patient data, wet and dry electrode types, various lead configurations, hardware sources, and stationary/ambulatory recordings from clinical and non-clinical settings. Over the NSR, MIT-BIH, AHA, and MIT-AF public databases, average QRS Se and PPV values of 98.90% and 99.08% were achieved. Adaptable performance (Se 93.26%, PPV 90.53%) was similarly observed on the challenging NST database. Crucially, HeartKey's performance effectively translated to the dry electrode space, with an average QRS Se of 99.22% and PPV of 99.00% observed over eight dry electrode databases representing various use cases, including two challenging motion-based collection protocols. CONCLUSION: HeartKey demonstrated robust signal conditioning and QRS detection performance across the broad range of tested ECG signals. It should be emphasized that in no way have the algorithms been altered or trained to optimize performance on a given database, meaning that HeartKey is potentially a universal solution capable of maintaining a high level of performance across a broad range of clinical and everyday use cases.

Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae.

Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for predicting genome-wide absolute protein abundances. A known caveat of this approach, however, is the poor technical reproducibility, that is, how consistent predictions are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We demonstrate that our simple normalization and rescaling approach can perform as accurately, yet more precisely, than methods which rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results offer a new benchmark for assessing MS data quality for protein quantification, while also underscoring current limitations in this approach.

Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism.

Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.

Estimating myofiber cross-sectional area and connective tissue deposition with electrical impedance myography: A study in D2-mdx mice.

INTRODUCTION: Surface electrical impedance myography (sEIM) has the potential for providing information on muscle composition and structure noninvasively. We sought to evaluate its use to predict myofiber size and connective tissue deposition in the D2-mdx model of Duchenne muscular dystrophy (DMD). METHODS: We applied a prediction algorithm, the least absolute shrinkage and selection operator, to select specific EIM measurements obtained with surface and ex vivo EIM data from D2-mdx and wild-type (WT) mice (analyzed together or separately). We assessed myofiber cross-sectional area histologically and hydroxyproline (HP), a surrogate measure for connective tissue content, biochemically. RESULTS: Using WT and D2-mdx impedance values together in the algorithm, sEIM gave average root-mean-square errors (RMSEs) of 26.6% for CSA and 45.8% for HP, which translate into mean errors of ±363 µm2 for a mean CSA of 1365 µm2 and of ±1.44 µg HP/mg muscle for a mean HP content of 3.15 µg HP/mg muscle. Stronger predictions were obtained by analyzing sEIM data from D2-mdx animals alone (RMSEs of 15.3% for CSA and 34.1% for HP content). Predictions made using ex vivo EIM data from D2-mdx animals alone were nearly equivalent to those obtained with sEIM data (RMSE of 16.59% for CSA), and slightly more accurate for HP (RMSE of 26.7%). DISCUSSION: Surface EIM combined with a predictive algorithm can provide estimates of muscle pathology comparable to values obtained using ex vivo EIM, and can be used as a surrogate measure of disease severity and progression and response to therapy.

Predicting myofiber cross-sectional area and triglyceride content with electrical impedance myography: A study in db/db mice.

BACKGROUND: Electrical impedance myography (EIM) provides insight into muscle composition and structure. We sought to evaluate its use in a mouse obesity model characterized by myofiber atrophy. METHODS: We applied a prediction algorithm, ie, the least absolute shrinkage and selection operator (LASSO), to surface, needle array, and ex vivo EIM data from db/db and wild-type mice and assessed myofiber cross-sectional area (CSA) histologically and triglyceride (TG) content biochemically. RESULTS: EIM data from all three modalities provided acceptable predictions of myofiber CSA with average root mean square error (RMSE) of 15% in CSA (ie, ±209 µm2 for a mean CSA of 1439 µm2 ) and TG content with RMSE of 30% in TG content (ie, ±7.3 nmol TG/mg muscle for a mean TG content of 25.4 nmol TG/mg muscle). CONCLUSIONS: EIM combined with a predictive algorithm provides reasonable estimates of myofiber CSA and TG content without the need for biopsy.

Exploring Determinants of Sexual Satisfaction in a Chilean Representative Sample.

This article examines the relationship between sexual satisfaction and gender, age, social status, being in stable relationships, intercourse frequency, and satisfaction with physical attractiveness (physical appearance and sex appeal). A self-report questionnaire was applied in a representative sample of 767 residents of Santiago (Chile), over 18 years old and that have had, at least, one sexual relation (with penetration) during their lives. Higher sexual satisfaction was predicted negatively by age and positively by higher satisfaction with sex appeal, higher intercourse frequency and having a stable relationship. Among women, the principal predictors are satisfaction with sex appeal and intercourse frequency. Among men to the above, it is necessary to add satisfaction with physical appearance and having a stable relationship - at odds with supposed higher relevance for women of body satisfaction and sensibility to social relationships. Discussion emphasizes the controversial nature of the gender differences found regarding the Latin American culture.

Validation of the Sexual Personal Well-being Index (PWI-Sex).

Objective: The purpose of this study is the validation of a proposed additional item for the PWI-7 scale for measuring sexual satisfaction as a dimension of Personal Well-being.Methodology: An adaptation of the PWI-7 questionnaire was administered to adult inhabitants of urban areas of Santiago, Chile. Analysis consisted of exposition of descriptive statistics, item-scale correlation, item-item correlation, multiple linear regression with the Overall Life Satisfaction Scale (OLS), moderation analysis and, confirmatory factor analysis. All analyses were divided by gender due to significant differences in sexual satisfaction found in literature reviewed.Results: All items were significantly and positively associated with the PWI. Internal consistency was satisfactory for Cronbach's alpha (α = 0.884 for males and α = 0.877 for females). After conducting a Confirmatory Factor Analysis using maximum likelihood (ML) as estimator, adequate levels of adjustment were obtained.Discussion: Results indicate that adding a new item on sexual satisfaction might be a contribution for the measurement of life satisfaction. The scale shows adequate internal consistency once the item is added and the new item on sexual satisfaction has a better fit than the one regarding satisfaction with spiritual life. Factor structure was invariant for males and females.

Quantitative muscle ultrasound in upper extremity mononeuropathies.

INTRODUCTION: We assessed the potential use of quantitative ultrasound (QUS) in the evaluation hand muscles affected by upper extremity mononeuropathies. METHODS: The gray scale levels (GSLs) of abductor pollicis brevis (APB), abductor digiti minimi (ADM), and first dorsal interosseous (FDI) of 30 healthy and 30 upper extremity mononeuropathy patients were measured and compared with standard electrophysiological values. RESULTS: Mean GSL was elevated in 34 APBs of carpal tunnel syndrome patients and 18 FDIs of ulnar neuropathy patients (e.g., FDI mean GSL (interquartile range) 31.5 (27.3~43.8) arbitrary units for patients and 24.0(23.0~29.0) for healthy subjects (P = 0.020)). GSL correlated with motor response amplitudes (Spearman's rho (ρ) = -0.39, P = 0.002 in APB, ρ = -0.72, P = 0.002 in FDI, and ρ = -0.43, P = 0.013 in ADM). The APB GSL correlated with electromyographic severity and disease duration (ρ = 0.46, P < 0.001 and ρ = 0.45, P = 0.003). CONCLUSIONS: Muscle QUS may serve as a useful tool in upper extremity mononeuropathy evaluation. Further study of this concept is recommended.

Altered muscle electrical tissue properties in a mouse model of premature aging.

INTRODUCTION: Improved methods are needed to detect and quantify age-related muscle change. In this study we assessed the electrical properties of muscle impacted by acquired mitochondrial DNA mutations via the PolG mouse, which exhibits typical age-associated features, and the impact of a potential therapy, nicotinamide mononucleotide (NMN). METHODS: The gastrocnemii of 24 PolG and 30 wild-type (WT) mice (8 PolG and 17 WT treated with NMN) were studied in an electrical impedance-measuring cell. Conductivity and relative permittivity were determined from the impedance data. Myofiber cross-sectional area (CSA) was quantified histologically. RESULTS: Untreated PolG mice demonstrated alterations in several impedance features, including 50-kHz relative permittivity and center frequency. A potential effect of NMN was also observed in these parameters in PolG but not WT animals. Impedance values correlated with myofiber CSA. DISCUSSION: Electrical impedance is sensitive to myofiber features considered characteristic of aging and to the impact of a potential therapy.

RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor.

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).

Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability.

PRIMARY OBJECTIVE: The objective of this study is to assess the functional state of the autonomic nervous system in healthy individuals and in individuals in coma using measures of heart rate variability (HRV) and to evaluate its efficiency in predicting mortality. DESIGN AND METHODS: Retrospective group comparison study of patients in coma classified into two subgroups, according to their Glasgow coma score, with a healthy control group. HRV indices were calculated from 7 min of artefact-free electrocardiograms using the Hilbert-Huang method in the spectral range 0.02-0.6 Hz. A special procedure was applied to avoid confounding factors. Stepwise multiple regression logistic analysis (SMLRA) and ROC analysis evaluated predictions. RESULTS: Progressive reduction of HRV was confirmed and was associated with deepening of coma and a mortality score model that included three spectral HRV indices of absolute power values of very low, low and very high frequency bands (0.4-0.6 Hz). The SMLRA model showed sensitivity of 95.65%, specificity of 95.83%, positive predictive value of 95.65%, and overall efficiency of 95.74%. CONCLUSIONS: HRV is a reliable method to assess the integrity of the neural control of the caudal brainstem centres on the hearts of patients in coma and to predict patient mortality.

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

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.