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

Design and application of a kinetic model of lipid metabolism in Saccharomyces cerevisiae

Lipid biosynthesis plays a vital role in living cells and has been increasingly engineered to overproduce various lipid-based chemicals. However, owing to the tightly constrained and interconnected nature of lipid biosynthesis, both understanding and engineering of lipid metabolism remain challenging, even with the help of mathematical models. Here we report the development of a kinetic metabolic model of lipid metabolism in Saccharomyces cerevisiae that integrates fatty acid biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism, storage lipids, lumped sterol synthesis, and the synthesis and transport of relevant target-chemicals, such as fatty acids and fatty alcohols. The model was trained on lipidomic data of a reference S. cerevisiae strain, single knockout mutants, and lipid overproduction strains reported in literature. The model was used to design mutants for fatty alcohol overproduction and the lipidomic analysis of the resultant mutant strains coupled with model-guided hypothesis led to discovery of a futile cycle in the triacylglycerol biosynthesis pathway. In addition, the model was used to explain successful and unsuccessful mutant designs in metabolic engineering literature. Thus, this kinetic model of lipid metabolism can not only enable the discovery of new phenomenon in lipid metabolism but also the engineering of mutant strains for overproduction of lipids.

Flux balance analysis of metabolic networks for efficient engineering of microbial cell factories

Metabolic engineering principles have long been applied to explore the metabolic networks of complex microbial cell factories under a variety of environmental constraints for effective deployment of the microorganisms in the optimal production of biochemicals like biofuels, polymers, amino acids, recombinant proteins. One of the methodologies used for analyzing microbial metabolic networks is the Flux Balance Analysis (FBA), which employs applications of optimization techniques for forecasting biomass growth and metabolic flux distribution of industrially important products under specified environmental conditions. The in silico flux simulations are instrumental for designing the production-specific microbial cell factories. However, FBA has some inherent limitations. The present review emphasizes how the incorporation of additional kinetic, thermodynamic, expression and regulatory constraints and integration of omics data into the classical FBA platform improve the prediction accuracy of FBA. A programmed comparison of the simulated data with the experimental observations is presented for supporting the claim. The review further accounts for the successful implementation of classical FBA in biotechnological applications and identifies areas in which classical FBA fails to make correct predictions. The analysis of the predictive capabilities of the different FBA strategies presented here is expected to help researchers in finding new avenues in engineering highly efficient microbial metabolic pathways and identify the key metabolic bottlenecks during the process. Based on the appropriate metabolic network design, fermentation engineers will be able to effectively design the bioreactors and optimize large-scale biochemical production through suitable pathway modifications.

Pathway engineering strategies for improved product yield in yeast-based industrial ethanol production

Product yield on carbohydrate feedstocks is a key performance indicator for industrial ethanol production with the yeast Saccharomyces cerevisiae. This paper reviews pathway engineering strategies for improving ethanol yield on glucose and/or sucrose in anaerobic cultures of this yeast by altering the ratio of ethanol production, yeast growth and glycerol formation. Particular attention is paid to strategies aimed at altering energy coupling of alcoholic fermentation and to strategies for altering redox-cofactor coupling in carbon and nitrogen metabolism that aim to reduce or eliminate the role of glycerol formation in anaerobic redox metabolism. In addition to providing an overview of scientific advances we discuss context dependency, theoretical impact and potential for industrial application of different proposed and developed strategies.

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

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.

Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

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.

Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

Background

Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level.

Results

Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k_{{cat}}$$\end{document}kcat values, predicting the differential expression of enzymes under different growth conditions.

Conclusions

This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01614-2.

YPIBP: A repository for phosphoinositide-binding proteins in yeast

Phosphoinositides (PIs) are a family of eight lipids consisting of phosphatidylinositol (PtdIns) and its seven phosphorylated forms. PIs have important regulatory functions in the cell including lipid signaling, protein transport, and membrane trafficking. Yeast has been recognized as a eukaryotic model system to study lipid-protein interactions. Hundreds of yeast PI-binding proteins have been identified, but this research knowledge remains scattered. Besides, the complete PI-binding spectrum and potential PI-binding domains have not been interlinked. No comprehensive databases are available to support the lipid-protein interaction research on phosphoinositides. Here we constructed the first knowledgebase of Yeast Phosphoinositide-Binding Proteins (YPIBP), a repository consisting of 679 PI-binding proteins collected from high-throughput proteome-array and lipid-array studies, QuickGO, and a rigorous literature mining. The YPIBP also contains protein domain information in categories of lipid-binding domains, lipid-related domains and other domains. The YPIBP provides search and browse modes along with two enrichment analyses (PI-binding enrichment analysis and domain enrichment analysis). An interactive visualization is given to summarize the PI-domain-protein interactome. Finally, three case studies were given to demonstrate the utility of YPIBP. The YPIBP knowledgebase consolidates the present knowledge and provides new insights of the PI-binding proteins by bringing comprehensive and in-depth interaction network of the PI-binding proteins. YPIBP is available at http://cosbi7.ee.ncku.edu.tw/YPIBP/.

Publishing reproducible dynamic kinetic models

Publishing repeatable and reproducible computational models is a crucial aspect of the scientific method in computational biology and one that is often forgotten in the rush to publish. The pressures of academic life and the lack of any reward system at institutions, granting agencies and journals means that publishing reproducible science is often either non-existent or, at best, presented in the form of an incomplete description. In the article, we will focus on repeatability and reproducibility in the systems biology field where a great many published models cannot be reproduced and in many cases even repeated. This review describes the current landscape of software tooling, model repositories, model standards and best practices for publishing repeatable and reproducible kinetic models. The review also discusses possible future remedies including working more closely with journals to help reviewers and editors ensure that published kinetic models are at minimum, repeatable. Contact: hsauro@uw.edu.

Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species

Metabolic network reconstructions have become an important tool for probing cellular metabolism in the field of systems biology. They are used as tools for quantitative prediction but also as scaffolds for further knowledge contextualization. The yeast Saccharomyces cerevisiae was one of the first organisms for which a genome-scale metabolic model (GEM) was reconstructed, in 2003, and since then 45 metabolic models have been developed for a wide variety of relevant yeasts species. A systematic evaluation of these models revealed that-despite this long modeling history-the sequential process of tracing model files, setting them up for basic simulation purposes and comparing them across species and even different versions, is still not a generalizable task. These findings call the yeast modeling community to comply to standard practices on model development and sharing in order to make GEMs accessible and useful for a wider public.

Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts

Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using ¹³C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.

Bayesian genome scale modelling identifies thermal determinants of yeast metabolism

The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovers enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) is predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtain a thermotolerant strain that outgrows the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.

While temperature impacts the function of all cellular components, it’s hard to rule out how the temperature dependence of cell phenotypes emerged from the dependence of individual components. Here, the authors develop a Bayesian genome scale modelling approach to identify thermal determinants of yeast metabolism.

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants

Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the 630 genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.

redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast

Over the last decades, yeast has become a key model organism for the study of lipid biochemistry. Because the regulation of lipids has been closely linked to various physiopathologies, the study of these biomolecules could lead to new diagnostics and treatments. Before the field can reach this point, however, sufficient tools for integrating and analyzing the ever-growing availability of lipidomics data will need to be developed. To this end, genome-scale models (GEMs) of metabolic networks are useful tools, though their large size and complexity introduces too much uncertainty in the accuracy of predicted outcomes. Ideally, therefore, a model for studying lipids would contain only the pathways required for the proper analysis of these biomolecules, but would not be an ad hoc reduction. We hereby present a metabolic model that focuses on lipid metabolism constructed through the integration of detailed lipid pathways into an already existing GEM of Saccharomyces cerevisiae. Our model was then systematically reduced around the subsystems defined by these pathways to provide a more manageable model size for complex studies. We show that this model is as consistent and inclusive as other yeast GEMs regarding the focus and detail on the lipid metabolism, and can be used as a scaffold for integrating lipidomics data to improve predictions in studies of lipid-related biological functions.

Gsmodutils: a python based framework for test-driven genome scale metabolic model development

MOTIVATION

Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle.

RESULTS

As part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions.

AVAILABILITY AND IMPLEMENTATION

The software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A systematic assessment of current genome-scale metabolic reconstruction tools

BACKGROUND

Several genome-scale metabolic reconstruction software platforms have been developed and are being continuously updated. These tools have been widely applied to reconstruct metabolic models for hundreds of microorganisms ranging from important human pathogens to species of industrial relevance. However, these platforms, as yet, have not been systematically evaluated with respect to software quality, best potential uses and intrinsic capacity to generate high-quality, genome-scale metabolic models. It is therefore unclear for potential users which tool best fits the purpose of their research.

RESULTS

In this work, we perform a systematic assessment of current genome-scale reconstruction software platforms. To meet our goal, we first define a list of features for assessing software quality related to genome-scale reconstruction. Subsequently, we use the feature list to evaluate the performance of each tool. To assess the similarity of the draft reconstructions to high-quality models, we compare each tool's output networks with that of the high-quality, manually curated, models of Lactobacillus plantarum and Bordetella pertussis, representatives of gram-positive and gram-negative bacteria, respectively. We additionally compare draft reconstructions with a model of Pseudomonas putida to further confirm our findings. We show that none of the tools outperforms the others in all the defined features.

CONCLUSIONS

Model builders should carefully choose a tool (or combinations of tools) depending on the intended use of the metabolic model. They can use this benchmark study as a guide to select the best tool for their research. Finally, developers can also benefit from this evaluation by getting feedback to improve their software.

Metabolic flux analysis for metabolome data validation of naturally xylose-fermenting yeasts

BACKGROUND

Efficient xylose fermentation still demands knowledge regarding xylose catabolism. In this study, metabolic flux analysis (MFA) and metabolomics were used to improve our understanding of xylose metabolism. Thus, a stoichiometric model was constructed to simulate the intracellular carbon flux and used to validate the metabolome data collected within xylose catabolic pathways of non-Saccharomyces xylose utilizing yeasts.

RESULTS

A metabolic flux model was constructed using xylose fermentation data from yeasts Scheffersomyces stipitis, Spathaspora arborariae, and Spathaspora passalidarum. In total, 39 intracellular metabolic reactions rates were utilized validating the measurements of 11 intracellular metabolites, acquired by mass spectrometry. Among them, 80% of total metabolites were confirmed with a correlation above 90% when compared to the stoichiometric model. Among the intracellular metabolites, fructose-6-phosphate, glucose-6-phosphate, ribulose-5-phosphate, and malate are validated in the three studied yeasts. However, the metabolites phosphoenolpyruvate and pyruvate could not be confirmed in any yeast. Finally, the three yeasts had the metabolic fluxes from xylose to ethanol compared. Xylose catabolism occurs at twice-higher flux rates in S. stipitis than S. passalidarum and S. arborariae. Besides, S. passalidarum present 1.5 times high flux rate in the xylose reductase reaction NADH-dependent than other two yeasts.

CONCLUSIONS

This study demonstrated a novel strategy for metabolome data validation and brought insights about naturally xylose-fermenting yeasts. S. stipitis and S. passalidarum showed respectively three and twice higher flux rates of XR with NADH cofactor, reducing the xylitol production when compared to S. arborariae. Besides then, the higher flux rates directed to pentose phosphate pathway (PPP) and glycolysis pathways resulted in better ethanol production in S. stipitis and S. passalidarum when compared to S. arborariae.

Yeast Systems Biology: Model Organism and Cell Factory

For thousands of years, the yeast Saccharomyces cerevisiae (S. cerevisiae) has served as a cell factory for the production of bread, beer, and wine. In more recent years, this yeast has also served as a cell factory for producing many different fuels, chemicals, food ingredients, and pharmaceuticals. S. cerevisiae, however, has also served as a very important model organism for studying eukaryal biology, and even today many new discoveries, important for the treatment of human diseases, are made using this yeast as a model organism. Here a brief review of the use of S. cerevisiae as a model organism for studying eukaryal biology, its use as a cell factory, and how advances in systems biology underpin developments in both these areas, is provided.

Yeast mitochondria: an overview of mitochondrial biology and the potential of mitochondrial systems biology

Mitochondria are dynamic organelles of endosymbiotic origin that are essential components of eukaryal cells. They contain their own genetic machinery, have multicopy genomes and like their bacterial ancestors they consist of two membranes. However, the majority of the ancestral genome has been lost or transferred to the nuclear genome of the host, preserving only a core set of genes involved in oxidative phosphorylation. Mitochondria perform numerous biological tasks ranging from bioenergetics to production of protein co-factors, including heme and iron-sulfur clusters. Due to the importance of mitochondria in many cellular processes, mitochondrial dysfunction is implicated in a wide variety of human disorders. Much of our current knowledge on mitochondrial function and dysfunction comes from studies using Saccharomyces cerevisiae. This yeast has good fermenting capacity, rendering tolerance to mutations that inactivate oxidative phosphorylation and complete loss of mitochondrial DNA. Here, we review yeast mitochondrial metabolism and function with focus on S. cerevisiae and its contribution in understanding mitochondrial biology. We further review how systems biology studies, including mathematical modeling, has allowed gaining new insight into mitochondrial function, and argue that this approach may enable us to gain a holistic view on how mitochondrial function interacts with different cellular processes.

Yeast Genome-Scale Metabolic Models for Simulating Genotype-Phenotype Relations

Understanding genotype-phenotype dependency is a universal aim for all life sciences. While the complete genotype-phenotype relations remain challenging to resolve, metabolic phenotypes are moving within the reach through genome-scale metabolic model simulations. Genome-scale metabolic models are available for commonly investigated yeasts, such as model eukaryote and domesticated fermentation species Saccharomyces cerevisiae, and automatic reconstruction methods facilitate obtaining models for any sequenced species. The models allow for investigating genotype-phenotype relations through simulations simultaneously considering the effects of nutrient availability, and redox and energy homeostasis in cells. Genome-scale models also offer frameworks for omics data integration to help to uncover how the translation of genotypes to the apparent phenotypes is regulated at different levels. In this chapter, we provide an overview of the yeast genome-scale metabolic models and the simulation approaches for using these models to interrogate genotype-phenotype relations. We review the methodological approaches according to the underlying biological reasoning in order to inspire formulating novel questions and applications that the genome-scale metabolic models could contribute to. Finally, we discuss current challenges and opportunities in the genome-scale metabolic model simulations.

Exposure to the lampricide TFM elicits an environmental stress response in yeast

The pesticide 3-trifluoromethyl-4-nitrophenol (TFM) is used to control sea lamprey populations in the Great Lakes and Lake Champlain. Little is known about the effects of this pesticide on gene expression in eukaryotic species. This study used microarray analysis to determine the effects of short term, high dose TFM exposure on gene expression in Saccharomyces cerevisiae. Yeast grown in standard glucose-containing media showed significant variation in gene expression in pathways related to cytoplasmic translation with the majority of these genes being downregulated. These findings were supported by the analysis of a similar but glucose-free experiment revealing that these cytoplasmic translation genes, mostly ribosomal subunit expressing genes, were similarly downregulated. The repression of the ribosomal subunit genes suggests that TFM exposure, regardless of glucose availability, evokes features of the environmental stress response in yeast.

Regulation of glycolytic flux and overflow metabolism depending on the source of energy generation for energy demand

Overflow metabolism is a common phenomenon observed at higher glycolytic flux in many bacteria, yeast (known as Crabtree effect), and mammalian cells including cancer cells (known as Warburg effect). This phenomenon has recently been characterized as the trade-offs between protein costs and enzyme efficiencies based on coarse-graining approaches. Moreover, it has been recognized that the glycolytic flux increases as the source of energy generation changes from energetically efficient respiration to inefficient respiro-fermentative or fermentative metabolism causing overflow metabolism. It is highly desired to clarify the metabolic regulation mechanisms behind such phenomena. Metabolic fluxes are located on top of the hierarchical regulation systems, and represent the outcome of the integrated response of all levels of cellular regulation systems. In the present article, we discuss about the different levels of regulation systems for the modulation of fluxes depending on the growth rate, growth condition such as oxygen limitation that alters the metabolism towards fermentation, and genetic perturbation affecting the source of energy generation from respiration to respiro-fermentative metabolism in relation to overflow metabolism. The intracellular metabolite of the upper glycolysis such as fructose 1,6-bisphosphate (FBP) plays an important role not only for flux sensing, but also for the regulation of the respiratory activity either directly or indirectly (via transcription factors) at higher growth rate. The glycolytic flux regulation is backed up (enhanced) by unphosphorylated EIIA and HPr of the phosphotransferase system (PTS) components, together with the sugar-phosphate stress regulation, where the transcriptional regulation is further modulated by post-transcriptional regulation via the degradation of mRNA (stability of mRNA) in Escherichia coli. Moreover, the channeling may also play some role in modulating the glycolytic cascade reactions.

Assessing Escherichia coli metabolism models and simulation approaches in phenotype predictions: Validation against experimental data

Over the last years, several genome-scale metabolic models (GEMs) and kinetic models of Escherichia coli were published. Their predictive performance varies according to the evaluation metric considered, the computational simulation methods used, and the type/quality of experimental data available. However, the GEM approach is often not compared with the kinetic modeling framework. Also, the different genome-scale reconstruction versions and simulation methods of mutant phenotypes are usually not validated to predict intracellular fluxes using large experimental datasets. Here, we intended to (i) systematically evaluate the prediction performance of three E. coli GEMs (iJR904, iAF1260, and iJO1366) available in the literature according to predictive growth metrics (intracellular flux distribution); (ii) assess the reliability of a E. coli GEM in the prediction of gene knockout phenotypes when different simulation methods (parsimonious flux balance analysis, Minimization of Metabolic Adjustment, linear version of MoMA, Regulatory on/off minimization, and Minimization of Metabolites Balance) are used; and finally (iii) investigate the flux distribution predictive power of the constrained-based modeling approach (selected stoichiometric GEM) and compare it with the kinetic modeling approach (two published kinetic models) for E. coli central metabolism, in order to assess their accuracy. Results show that the phenotype predictions were not significantly sensitive to the metabolic models, although the GEM iAF1260 was more accurate in the prediction of central carbon fluxes at low dilution rates. Furthermore, we observed that the choice of the appropriate simulation method of mutant phenotypes depends on the biological question to be addressed. In terms of the two modeling approaches, none outperformed the other for all the tested scenarios. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1344-1354, 2018.

Elucidating redox balance shift in Scheffersomyces stipitis' fermentative metabolism using a modified genome-scale metabolic model

Background

Scheffersomyces stipitis is an important yeast species in the field of biorenewables due to its desired capacity for xylose utilization. It has been recognized that redox balance plays a critical role in S. stipitis due to the different cofactor preferences in xylose assimilation pathway. However, there has not been any systems level understanding on how the shift in redox balance contributes to the overall metabolic shift in S. stipitis to cope with reduced oxygen uptake. Genome-scale metabolic network models (GEMs) offer the opportunity to gain such systems level understanding; however, currently the two published GEMs for S. stipitis cannot be used for this purpose, as neither of them is able to capture the strain’s fermentative metabolism reasonably well due to their poor prediction of xylitol production, a key by-product under oxygen limited conditions.

Results

A system identification-based (SID-based) framework that we previously developed for GEM validation is expanded and applied to refine a published GEM for S. stipitis, iBB814. After the modified GEM, named iDH814, was validated using literature data, it is used to obtain genome-scale understanding on how redox cofactor shifts when cells respond to reduced oxygen supply. The SID-based framework for GEM analysis was applied to examine how the environmental perturbation (i.e., reduced oxygen supply) propagates through the metabolic network, and key reactions that contribute to the shifts of redox and metabolic state were identified. Finally, the findings obtained through GEM analysis were validated using transcriptomic data.

Conclusions

iDH814, the modified model, was shown to offer significantly improved performance in terms of matching available experimental results and better capturing available knowledge on the organism. More importantly, our analysis based on iDH814 provides the first genome-scale understanding on how redox balance in S. stipitis was shifted as a result of reduced oxygen supply. The systems level analysis identified the key contributors to the overall metabolic state shift, which were validated using transcriptomic data. The analysis confirmed that S. stipitis uses a concerted approach to cope with the stress associated with reduced oxygen supply, and the shift of reducing power from NADPH to NADH seems to be the center theme that directs the overall shift in metabolic states.

Electronic supplementary material

The online version of this article (10.1186/s12934-018-0983-y) contains supplementary material, which is available to authorized users.

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.

Genome-scale modeling of yeast: chronology, applications and critical perspectives

Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed.

Flux control through protein phosphorylation in yeast

Protein phosphorylation is one of the most important mechanisms regulating metabolism as it can directly modify metabolic enzymes by the addition of phosphate groups. Attributed to such a rapid and reversible mechanism, cells can adjust metabolism rapidly in response to temporal changes. The yeast Saccharomyces cerevisiae, a widely used cell factory and model organism, is reported to show frequent phosphorylation events in metabolism. Studying protein phosphorylation in S. cerevisiae allows for gaining new insight into the function of regulatory networks, which may enable improved metabolic engineering as well as identify mechanisms underlying human metabolic diseases. Here we collect functional phosphorylation events of 41 enzymes involved in yeast metabolism and demonstrate functional mechanisms and the application of this information in metabolic engineering. From a systems biology perspective, we describe the development of phosphoproteomics in yeast as well as approaches to analysing the phosphoproteomics data. Finally, we focus on integrated analyses with other omics data sets and genome-scale metabolic models. Despite the advances, future studies improving both experimental technologies and computational approaches are imperative to expand the current knowledge of protein phosphorylation in S. cerevisiae.

Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast

Protein synthesis is the most energy-consuming process in a proliferating cell, and understanding what controls protein abundances represents a key question in biology and biotechnology. We quantified absolute abundances of 5,354 mRNAs and 2,198 proteins in Saccharomyces cerevisiae under ten environmental conditions and protein turnover for 1,384 proteins under a reference condition. The overall correlation between mRNA and protein abundances across all conditions was low (0.46), but for differentially expressed proteins (n = 202), the median mRNA-protein correlation was 0.88. We used these data to model translation efficiencies and found that they vary more than 400-fold between genes. Non-linear regression analysis detected that mRNA abundance and translation elongation were the dominant factors controlling protein synthesis, explaining 61% and 15% of its variance. Metabolic flux balance analysis further showed that only mitochondrial fluxes were positively associated with changes at the transcript level. The present dataset represents a crucial expansion to the current resources for future studies on yeast physiology.

Systems Biology of Metabolism

Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

Metabolic network modeling with model organisms

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.

Improving the flux distributions simulated with genome-scale metabolic models of Saccharomyces cerevisiae

Genome-scale metabolic models (GEMs) can be used to evaluate genotype-phenotype relationships and their application to microbial strain engineering is increasing in popularity. Some of the algorithms used to simulate the phenotypes of mutant strains require the determination of a wild-type flux distribution. However, the accuracy of this reference, when calculated with flux balance analysis, has not been studied in detail before.

Here, the wild-type simulations of selected GEMs for Saccharomyces cerevisiae have been analysed and most of the models tested predicted erroneous fluxes in central pathways, especially in the pentose phosphate pathway. Since the problematic fluxes were mostly related to areas of the metabolism consuming or producing NADPH/NADH, we have manually curated all reactions including these cofactors by forcing the use of NADPH/NADP⁺ in anabolic reactions and NADH/NAD⁺ for catabolic reactions. The curated models predicted more accurate flux distributions and performed better in the simulation of mutant phenotypes.

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The flux distributions of the genome-scale models of Saccharomyces cerevisiae were evaluated

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Most of the tested models showed fluxes inconsistent with experimental data

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A manual curation process was performed on all reactions including NADH or NADPH

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The curated models showed flux distributions more consistent with experimental data

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Phenotype simulations improved when the curated flux distributions were used

Engineering cytosolic acetyl-coenzyme A supply in Saccharomyces cerevisiae: Pathway stoichiometry, free-energy conservation and redox-cofactor balancing

Saccharomyces cerevisiae is an important industrial cell factory and an attractive experimental model for evaluating novel metabolic engineering strategies. Many current and potential products of this yeast require acetyl coenzyme A (acetyl-CoA) as a precursor and pathways towards these products are generally expressed in its cytosol. The native S. cerevisiae pathway for production of cytosolic acetyl-CoA consumes 2 ATP equivalents in the acetyl-CoA synthetase reaction. Catabolism of additional sugar substrate, which may be required to generate this ATP, negatively affects product yields. Here, we review alternative pathways that can be engineered into yeast to optimize supply of cytosolic acetyl-CoA as a precursor for product formation. Particular attention is paid to reaction stoichiometry, free-energy conservation and redox-cofactor balancing of alternative pathways for acetyl-CoA synthesis from glucose. A theoretical analysis of maximally attainable yields on glucose of four compounds (n-butanol, citric acid, palmitic acid and farnesene) showed a strong product dependency of the optimal pathway configuration for acetyl-CoA synthesis. Moreover, this analysis showed that combination of different acetyl-CoA production pathways may be required to achieve optimal product yields. This review underlines that an integral analysis of energy coupling and redox-cofactor balancing in precursor-supply and product-formation pathways is crucial for the design of efficient cell factories.

Cyanobacterial Biofuels: Strategies and Developments on Network and Modeling

Cyanobacteria, the phototrophic microorganisms, have attracted much attention recently as a promising source for environmentally sustainable biofuels production. However, barriers for commercial markets of cyanobacteria-based biofuels concern the economic feasibility. Miscellaneous strategies for improving the production performance of cyanobacteria have thus been developed. Among these, the simple ad hoc strategies resulting in failure to optimize fully cell growth coupled with desired product yield are explored. With the advancement of genomics and systems biology, a new paradigm toward systems metabolic engineering has been recognized. In particular, a genome-scale metabolic network reconstruction and modeling is a crucial systems-based tool for whole-cell-wide investigation and prediction. In this review, the cyanobacterial genome-scale metabolic models, which offer a system-level understanding of cyanobacterial metabolism, are described. The main process of metabolic network reconstruction and modeling of cyanobacteria are summarized. Strategies and developments on genome-scale network and modeling through the systems metabolic engineering approach are advanced and employed for efficient cyanobacterial-based biofuels production.

Industrial systems biology and its impact on synthetic biology of yeast cell factories

Engineering industrial cell factories to effectively yield a desired product while dealing with industrially relevant stresses is usually the most challenging step in the development of industrial production of chemicals using microbial fermentation processes. Using synthetic biology tools, microbial cell factories such as Saccharomyces cerevisiae can be engineered to express synthetic pathways for the production of fuels, biopharmaceuticals, fragrances, and food flavors. However, directing fluxes through these synthetic pathways towards the desired product can be demanding due to complex regulation or poor gene expression. Systems biology, which applies computational tools and mathematical modeling to understand complex biological networks, can be used to guide synthetic biology design. Here, we present our perspective on how systems biology can impact synthetic biology towards the goal of developing improved yeast cell factories. Biotechnol. Bioeng. 2016;113: 1164-1170. © 2015 Wiley Periodicals, Inc.

Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction

We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.

Using Gene Essentiality and Synthetic Lethality Information to Correct Yeast and CHO Cell Genome-Scale Models

Essentiality (ES) and Synthetic Lethality (SL) information identify combination of genes whose deletion inhibits cell growth. This information is important for both identifying drug targets for tumor and pathogenic bacteria suppression and for flagging and avoiding gene deletions that are non-viable in biotechnology. In this study, we performed a comprehensive ES and SL analysis of two important eukaryotic models (S. cerevisiae and CHO cells) using a bilevel optimization approach introduced earlier. Information gleaned from this study is used to propose specific model changes to remedy inconsistent with data model predictions. Even for the highly curated Yeast 7.11 model we identified 50 changes (metabolic and GPR) leading to the correct prediction of an additional 28% of essential genes and 36% of synthetic lethals along with a 53% reduction in the erroneous identification of essential genes. Due to the paucity of mutant growth phenotype data only 12 changes were made for the CHO 1.2 model leading to an additional correctly predicted 11 essential and eight non-essential genes. Overall, we find that CHO 1.2 was 76% less accurate than the Yeast 7.11 metabolic model in predicting essential genes. Based on this analysis, 14 (single and double deletion) maximally informative experiments are suggested to improve the CHO cell model by using information from a mouse metabolic model. This analysis demonstrates the importance of single and multiple knockout phenotypes in assessing and improving model reconstructions. The advent of techniques such as CRISPR opens the door for the global assessment of eukaryotic models.