A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Light-driven fine chemical production in yeast biohybrids

Inorganic-biological hybrid systems have potential to be sustainable, efficient, and versatile chemical synthesis platforms by integrating the light-harvesting properties of semiconductors with the synthetic potential of biological cells. We have developed a modular bioinorganic hybrid platform that consists of highly efficient light-harvesting indium phosphide nanoparticles and genetically engineered Saccharomyces cerevisiae, a workhorse microorganism in biomanufacturing. The yeast harvests photogenerated electrons from the illuminated nanoparticles and uses them for the cytosolic regeneration of redox cofactors. This process enables the decoupling of biosynthesis and cofactor regeneration, facilitating a carbon- and energy-efficient production of the metabolite shikimic acid, a common precursor for several drugs and fine chemicals. Our work provides a platform for the rational design of biohybrids for efficient biomanufacturing processes with higher complexity and functionality.

A comprehensive, mechanistically detailed, and executable model of the cell division cycle in Saccharomyces cerevisiae

Understanding how cellular functions emerge from the underlying molecular mechanisms is a key challenge in biology. This will require computational models, whose predictive power is expected to increase with coverage and precision of formulation. Genome-scale models revolutionised the metabolic field and made the first whole-cell model possible. However, the lack of genome-scale models of signalling networks blocks the development of eukaryotic whole-cell models. Here, we present a comprehensive mechanistic model of the molecular network that controls the cell division cycle in Saccharomyces cerevisiae. We use rxncon, the reaction-contingency language, to neutralise the scalability issues preventing formulation, visualisation and simulation of signalling networks at the genome-scale. We use parameter-free modelling to validate the network and to predict genotype-to-phenotype relationships down to residue resolution. This mechanistic genome-scale model offers a new perspective on eukaryotic cell cycle control, and opens up for similar models-and eventually whole-cell models-of human cells.

Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling

Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.

A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity

Background: The mechanisms responsible for calorie restriction (CR)-induced improvement in insulin sensitivity (IS) have not been fully elucidated. Greater insight can be achieved through deep biological phenotyping of subjects undergoing CR, and integration of big data. Materials and Methods: An integrative approach was applied to investigate associations between change in IS and factors from host, microbiota, and lifestyle after a 6-week CR period in 27 overweight or obese adults (ClinicalTrials.gov: NCT01314690). Partial least squares regression was used to determine associations of change (week 6 - baseline) between IS markers and lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics of serum, urine and feces, and gut microbiota composition. ScaleNet, a network learning approach based on spectral consensus strategy (SCS, developed by us) was used for reconstruction of biological networks. Results: A spectrum of variables from lifestyle factors (10 nutrients), gut microbiota (10 metagenomics species), and host multi-omics (metabolic features: 84 from serum, 73 from urine, and 131 from feces; and 257 sAT gene probes) most associated with IS were identified. Biological network reconstruction using SCS, highlighted links between changes in IS, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species (MGS). Linear regression analysis to model how changes of select variables over the CR period contribute to changes in IS, showed greatest contributions from gut MGS and fiber intake. Conclusion: This work has enhanced previous knowledge on links between host glucose homeostasis, lifestyle factors and the gut microbiota, and has identified potential biomarkers that may be used in future studies to predict and improve individual response to weight-loss interventions. Furthermore, this is the first study showing integration of the wide range of data presented herein, identifying 115 variables of interest with respect to IS from the initial input, consisting of 9,986 variables. Clinical Trial Registration: clinicaltrials.gov (NCT01314690).

Uncovering and resolving challenges of quantitative modeling in a simplified community of interacting cells

Quantitative modeling is useful for predicting behaviors of a system and for rationally constructing or modifying the system. The predictive power of a model relies on accurate quantification of model parameters. Here, we illustrate challenges in parameter quantification and offer means to overcome these challenges, using a case example in which we quantitatively predict the growth rate of a cooperative community. Specifically, the community consists of two Saccharomyces cerevisiae strains, each engineered to release a metabolite required and consumed by its partner. The initial model, employing parameters measured in batch monocultures with zero or excess metabolite, failed to quantitatively predict experimental results. To resolve the model-experiment discrepancy, we chemically identified the correct exchanged metabolites, but this did not improve model performance. We then remeasured strain phenotypes in chemostats mimicking the metabolite-limited community environments, while mitigating or incorporating effects of rapid evolution. Almost all phenotypes we measured, including death rate, metabolite release rate, and the amount of metabolite consumed per cell birth, varied significantly with the metabolite environment. Once we used parameters measured in a range of community-like chemostat environments, prediction quantitatively agreed with experimental results. In summary, using a simplified community, we uncovered and devised means to resolve modeling challenges that are likely general to living systems.

Metabolic profiling analysis upon acylcarnitines in tissues of hepatocellular carcinoma revealed the inhibited carnitine shuttle system caused by the downregulated carnitine palmitoyltransferase 2

The carnitine shuttle system (CSS) plays a crucial role in the transportation of fatty acyls during fatty acid β-oxidation for energy supplementation, especially in cases of high energy demand, such as in cancer. In this study, to systematically characterize alterations of the CSS in hepatocellular carcinoma (HCC), acylcarnitine metabolic profiling was carried out on 80 pairs of HCC tissues and adjacent noncancerous tissues (ANTs) by using ultra-performance liquid chromatography coupled to mass spectrometry. Twenty-four acylcarnitines classified into five categories were identified and characterized between HCCs and ANTs. Notably, increased saturated long-chain acylcarnitines (LCACs) and decreased short- and medium-chain acylcarnitines (S/MCACs) were simultaneously observed in HCC samples. Subsequent correlation network and heatmap analysis indicated low correlations between LCACs and S/MCACs. The mRNA and protein expressions of carnitine palmitoyltransferase 2 (CPT2) was significantly downregulated in HCC samples, whereas CPT1A expression was not significantly changed. Correspondingly, the relative levels of S/MCACs were reduced and those of LCACs were increased in BEL-7402/CPT2-knockdown cells compared to negative controls. Both results suggested that decreased shuttling efficiency in HCC might be associated with downregulation of CPT2. In addition, decreases in the mRNA expression of acetyl-CoA acyltransferase 2 were also observed in HCC tissues and BEL-7402/CPT2-knockdown cells, suggesting potential low β-oxidation efficiency, which was consistent with the increased expression of stearoyl-CoA desaturase 1 in both samples. The systematic strategy applied in our study illustrated decreased shuttling efficiency of the carnitine shuttle system in HCC and can provide biologists with an in-depth understanding of β-oxidation in HCC.

SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework

BACKGROUND

A recurrent problem in genome-scale metabolic models (GEMs) is to correctly represent lipids as biomass requirements, due to the numerous of possible combinations of individual lipid species and the corresponding lack of fully detailed data. In this study we present SLIMEr, a formalism for correctly representing lipid requirements in GEMs using commonly available experimental data.

RESULTS

SLIMEr enhances a GEM with mathematical constructs where we Split Lipids Into Measurable Entities (SLIME reactions), in addition to constraints on both the lipid classes and the acyl chain distribution. By implementing SLIMEr on the consensus GEM of Saccharomyces cerevisiae, we can represent accurate amounts of lipid species, analyze the flexibility of the resulting distribution, and compute the energy costs of moving from one metabolic state to another.

CONCLUSIONS

The approach shows potential for better understanding lipid metabolism in yeast under different conditions. SLIMEr is freely available at https://github.com/SysBioChalmers/SLIMEr .

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.

Established and Upcoming Yeast Expression Systems

Yeast was the first microorganism used by mankind for biotransformation of feedstock that laid the foundations of industrial biotechnology. Long historical use, vast amount of data, and experience paved the way for Saccharomyces cerevisiae as a first yeast cell factory, and still it is an important expression platform as being the production host for several large volume products. Continuing special needs of each targeted product and different requirements of bioprocess operations have led to identification of different yeast expression systems. Modern bioprocess engineering and advances in omics technology, i.e., genomics, transcriptomics, proteomics, secretomics, and interactomics, allow the design of novel genetic tools with fine-tuned characteristics to be used for research and industrial applications. This chapter focuses on established and upcoming yeast expression platforms that have exceptional characteristics, such as the ability to utilize a broad range of carbon sources or remarkable resistance to various stress conditions. Besides the conventional yeast S. cerevisiae, established yeast expression systems including the methylotrophic yeasts Pichia pastoris and Hansenula polymorpha, the dimorphic yeasts Arxula adeninivorans and Yarrowia lipolytica, the lactose-utilizing yeast Kluyveromyces lactis, the fission yeast Schizosaccharomyces pombe, and upcoming yeast platforms, namely, Kluyveromyces marxianus, Candida utilis, and Zygosaccharomyces bailii, are compiled with special emphasis on their genetic toolbox for recombinant protein production.

Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges

Genome-scale metabolic models (GEMs) are mathematical models that enable systematic analysis of metabolism. This modeling concept has been applied to study the metabolism of many organisms including the eukaryal model organism, the yeast Saccharomyces cerevisiae, that also serves as an important cell factory for production of fuels and chemicals. With the application of yeast GEMs, our knowledge of metabolism is increasing. Therefore, GEMs have also been used for modeling human cells to study metabolic diseases. Here we introduce the concept of GEMs and provide a protocol for reconstructing GEMs. Besides, we show the historic development of yeast GEMs and their applications. Also, we review human GEMs as well as their uses in the studies of complex diseases.

Quantitative flux coupling analysis

Flux coupling analysis (FCA) aims to describe the functional dependencies among reactions in a metabolic network. Currently studied coupling relations are qualitative in the sense that they identify pairs of reactions for which the activity of one reaction necessitates the activity of the other one, but without giving any numerical bounds relating the possible activity rates. The potential applications of FCA are heavily investigated, however apart from some trivial cases there is no clue of what bottleneck in the metabolic network causes each dependency. In this article, we introduce a quantitative approach to the same flux coupling problem named quantitative flux coupling analysis (QFCA). It generalizes the current concepts as we show that all the qualitative information provided by FCA is readily available in the quantitative flux coupling equations of QFCA, without the need for any additional analysis. Moreover, we design a simple algorithm to efficiently identify these flux coupling equations which scales up to the genome-scale metabolic networks with thousands of reactions and metabolites in an effective way. Furthermore, this framework enables us to quantify the "strength" of the flux coupling relations. We also provide different biologically meaningful interpretations, including one which gives an intuitive certificate of precisely which metabolites in the network enforce each flux coupling relation. Eventually, we conclude by suggesting the probable application of QFCA to the metabolic gap-filling problem, which we only begin to address here and is left for future research to further investigate.

Network Community Detection Based on the Physarum-Inspired Computational Framework

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, a kind of slime, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost.

Synthetic biology tools for engineering Yarrowia lipolytica

The non-conventional oleaginous yeast Yarrowia lipolytica shows great industrial promise. It naturally produces certain compounds of interest but can also artificially generate non-native metabolites, thanks to an engineering process made possible by the significant expansion of a dedicated genetic toolbox. In this review, we present recently developed synthetic biology tools that facilitate the manipulation of Y. lipolytica, including 1) DNA assembly techniques, 2) DNA parts for constructing expression cassettes, 3) genome-editing techniques, and 4) computational tools.

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Due to its metabolic features, Y. lipolytica is a promising cell factory for producing compounds of biotechnological interest.

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Fast and efficient synthetic biology tools have been developed, which has boosted engineering strategies for Y. lipolytica.

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This review describes the latest advances in synthetic biology for Y. lipolytica.

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DNA assembly tools, DNA parts for expression cassettes, genome-editing techniques, and computational tools are covered here.

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

A challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.

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The proteome of kinase knockouts is dominated by enzyme abundance changes

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The enzyme expression profiles of kinase knockouts are non-redundant

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Metabolism is regulated by many expression changes acting in concert

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Machine learning accurately predicts the metabolome from enzyme abundance

An appeal to magic? The discovery of a non-enzymatic metabolism and its role in the origins of life

Until recently, prebiotic precursors to metabolic pathways were not known. In parallel, chemistry achieved the synthesis of amino acids and nucleotides only in reaction sequences that do not resemble metabolic pathways, and by using condition step changes, incompatible with enzyme evolution. As a consequence, it was frequently assumed that the topological organisation of the metabolic pathway has formed in a Darwinian process. The situation changed with the discovery of a non-enzymatic glycolysis and pentose phosphate pathway. The suite of metabolism-like reactions is promoted by a metal cation, (Fe(II)), abundant in Archean sediment, and requires no condition step changes. Knowledge about metabolism-like reaction topologies has accumulated since, and supports non-enzymatic origins of gluconeogenesis, the S-adenosylmethionine pathway, the Krebs cycle, as well as CO2 fixation. It now feels that it is only a question of time until essential parts of metabolism can be replicated non-enzymatically. Here, I review the 'accidents' that led to the discovery of the non-enzymatic glycolysis, and on the example of a chemical network based on hydrogen cyanide, I provide reasoning why metabolism-like non-enzymatic reaction topologies may have been missed for a long time. Finally, I discuss that, on the basis of non-enzymatic metabolism-like networks, one can elaborate stepwise scenarios for the origin of metabolic pathways, a situation that increasingly renders the origins of metabolism a tangible problem.

Genome-scale fluxes predicted under the guidance of enzyme abundance using a novel hyper-cube shrink algorithm

Motivation

One of the long-expected goals of genome-scale metabolic modelling is to evaluate the influence of the perturbed enzymes on flux distribution. Both ordinary differential equation (ODE) models and constraint-based models, like Flux balance analysis (FBA), lack the capacity to perform metabolic control analysis (MCA) for large-scale networks.

Results

In this study, we developed a hyper-cube shrink algorithm (HCSA) to incorporate the enzymatic properties into the FBA model by introducing a pseudo reaction V constrained by enzymatic parameters. Our algorithm uses the enzymatic information quantitatively rather than qualitatively. We first demonstrate the concept by applying HCSA to a simple three-node network, whereby we obtained a good correlation between flux and enzyme abundance. We then validate its prediction by comparison with ODE and with a synthetic network producing voilacein and analogues in Saccharomyces cerevisiae. We show that HCSA can mimic the state-state results of ODE. Finally, we show its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast. We show the ability of HCSA to operate without biomass flux and perform MCA to determine rate-limiting reactions.

Availability and implementation

Algorithm was implemented by Matlab and C ++. The code is available at https://github.com/kekegg/HCSA.

Contact

xiezhengwei@hsc.pku.edu.cn or qi@pku.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Bacterial Component to Alzheimer's-Type Dementia Seen via a Systems Biology Approach that Links Iron Dysregulation and Inflammagen Shedding to Disease

The progression of Alzheimer's disease (AD) is accompanied by a great many observable changes, both molecular and physiological. These include oxidative stress, neuroinflammation, and (more proximal to cognitive decline) the death of neuronal and other cells. A systems biology approach seeks to organize these observed variables into pathways that discriminate those that are highly involved (i.e., causative) from those that are more usefully recognized as bystander effects. We review the evidence that iron dysregulation is one of the central causative pathway elements here, as this can cause each of the above effects. In addition, we review the evidence that dormant, non-growing bacteria are a crucial feature of AD, that their growth in vivo is normally limited by a lack of free iron, and that it is this iron dysregulation that is an important factor in their resuscitation. Indeed, bacterial cells can be observed by ultrastructural microscopy in the blood of AD patients. A consequence of this is that the growing cells can shed highly inflammatory components such as lipopolysaccharides (LPS). These too are known to be able to induce (apoptotic and pyroptotic) neuronal cell death. There is also evidence that these systems interact with elements of vitamin D metabolism. This integrative systems approach has strong predictive power, indicating (as has indeed been shown) that both natural and pharmaceutical iron chelators might have useful protective roles in arresting cognitive decline, and that a further assessment of the role of microbes in AD development is more than highly warranted.

Metabolic profiling of isolated mitochondria and cytoplasm reveals compartment-specific metabolic responses

INTRODUCTION

Subcellular compartmentalization enables eukaryotic cells to carry out different reactions at the same time, resulting in different metabolite pools in the subcellular compartments. Thus, mutations affecting the mitochondrial energy metabolism could cause different metabolic alterations in mitochondria compared to the cytoplasm. Given that the metabolite pool in the cytosol is larger than that of other subcellular compartments, metabolic profiling of total cells could miss these compartment-specific metabolic alterations.

OBJECTIVES

To reveal compartment-specific metabolic differences, mitochondria and the cytoplasmic fraction of baker's yeast Saccharomyces cerevisiae were isolated and subjected to metabolic profiling.

METHODS

Mitochondria were isolated through differential centrifugation and were analyzed together with the remaining cytoplasm by gas chromatography-mass spectrometry (GC-MS) based metabolic profiling.

RESULTS

Seventy-two metabolites were identified, of which eight were found exclusively in mitochondria and sixteen exclusively in the cytoplasm. Based on the metabolic signature of mitochondria and of the cytoplasm, mutants of the succinate dehydrogenase (respiratory chain complex II) and of the F_OF₁-ATP-synthase (complex V) can be discriminated in both compartments by principal component analysis from wild-type and each other. These mitochondrial oxidative phosphorylation machinery mutants altered not only citric acid cycle related metabolites but also amino acids, fatty acids, purine and pyrimidine intermediates and others.

CONCLUSION

By applying metabolomics to isolated mitochondria and the corresponding cytoplasm, compartment-specific metabolic signatures can be identified. This subcellular metabolomics analysis is a powerful tool to study the molecular mechanism of compartment-specific metabolic homeostasis in response to mutations affecting the mitochondrial metabolism.

Constraint-based stoichiometric modelling from single organisms to microbial communities

Microbial communities are ubiquitously found in Nature and have direct implications for the environment, human health and biotechnology. The species composition and overall function of microbial communities are largely shaped by metabolic interactions such as competition for resources and cross-feeding. Although considerable scientific progress has been made towards mapping and modelling species-level metabolism, elucidating the metabolic exchanges between microorganisms and steering the community dynamics remain an enormous scientific challenge. In view of the complexity, computational models of microbial communities are essential to obtain systems-level understanding of ecosystem functioning. This review discusses the applications and limitations of constraint-based stoichiometric modelling tools, and in particular flux balance analysis (FBA). We explain this approach from first principles and identify the challenges one faces when extending it to communities, and discuss the approaches used in the field in view of these challenges. We distinguish between steady-state and dynamic FBA approaches extended to communities. We conclude that much progress has been made, but many of the challenges are still open.

Metabolic adjustment upon repetitive substrate perturbations using dynamic 13C-tracing in yeast

BACKGROUND

Natural and industrial environments are dynamic with respect to substrate availability and other conditions like temperature and pH. Especially, metabolism is strongly affected by changes in the extracellular space. Here we study the dynamic flux of central carbon metabolism and storage carbohydrate metabolism under dynamic feast/famine conditions in Saccharomyces cerevisiae.

RESULTS

The metabolic flux reacts fast and sensitive to cyclic perturbations in substrate availability. Compared to well-documented stimulus-response experiments using substrate pulses, different metabolic responses are observed. Especially, cells experiencing cyclic perturbations do not show a drop in ATP with the addition of glucose, but an immediate increase in energy charge. Although a high glycolytic flux of up to 5.4 mmol g DW-1  h-1 is observed, no overflow metabolites are detected. From famine to feast the glucose uptake rate increased from 170 to 4788 μmol g DW-1  h-1 in 24 s. Intracellularly, even more drastic changes were observed. Especially, the T6P synthesis rate increased more than 100-fold upon glucose addition. This response indicates that the storage metabolism is very sensitive to changes in glycolytic flux and counterbalances these rapid changes by diverting flux into large pools to prevent substrate accelerated death and potentially refill the central metabolism when substrates become scarce. Using 13C-tracer we found a dilution in the labeling of extracellular glucose, G6P, T6P and other metabolites, indicating an influx of unlabeled carbon. It is shown that glycogen and trehalose degradation via different routes could explain these observations. Based on the 13C labeling in average 15% of the carbon inflow is recycled via trehalose and glycogen. This average fraction is comparable to the steady-state turnover, but changes significantly during the cycle, indicating the relevance for dynamic regulation of the metabolic flux.

CONCLUSIONS

Comparable to electric energy grids, metabolism seems to use storage units to buffer peaks and keep reserves to maintain a robust function. During the applied fast feast/famine conditions about 15% of the metabolized carbon were recycled in storage metabolism. Additionally, the resources were distributed different to steady-state conditions. Most remarkably is a fivefold increased flux towards PPP that generated a reversed flux of transaldolase and the F6P-producing transketolase reactions. Combined with slight changes in the biomass composition, the yield decrease of 5% can be explained.

Modeling Microbial Communities: A Call for Collaboration between Experimentalists and Theorists

With our growing understanding of the impact of microbial communities, understanding how such communities function has become a priority. The influence of microbial communities is widespread. Human-associated microbiota impacts health, environmental microbes determine ecosystem sustainability, and microbe-driven industrial processes are expanding. This broad range of applications has led to a wide range of approaches to analyze and describe microbial communities. In particular, theoretical work based on mathematical modeling has been a steady source of inspiration for explaining and predicting microbial community processes. Here, we survey some of the modeling approaches used in different contexts. We promote classifying different approaches using a unified platform, and encourage cataloging the findings in a database. We believe that the synergy emerging from a coherent collection facilitates a better understanding of important processes that determine microbial community functions. We emphasize the importance of close collaboration between theoreticians and experimentalists in formulating, classifying, and improving models of microbial communities.

Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification

Genome‐scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene‐reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome‐scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA, and steroid biosynthetic processes, cellular alcohol metabolic process, and oxidation‐reduction process. Furthermore, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development.

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.

Multi-omics approaches to disease

High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of “integrative genetics”. Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

Genome-scale metabolic modelling common cofactors metabolism in microorganisms

The common cofactors ATP/ADP, NAD(P)(H), and acetyl-CoA/CoA are indispensable participants in biochemical reactions in industrial microbes. To systematically explore the effects of these cofactors on cell growth and metabolic phenotypes, the first genome-scale cofactor metabolic model, icmNX6434, including 6434 genes, 1782 metabolites, and 6877 reactions, was constructed from 14 genome-scale metabolic models of 14 industrial strains. The origin, consumption, and interactions of these common cofactors in microbial cells were elucidated by the icmNX6434 model, and they played important roles in cell growth. The essential cofactor modules contained 2480 genes and 2948 reactions; therefore, improving cofactor biosynthesis, directing these cofactors into essential metabolic pathways, as well as avoiding cofactor utilization during byproduct biosynthesis and futile cycles, are three ways to increase cell growth. The effects of these common cofactors on the distribution and rate of the carbon flux in four universal modes, as well as an optimized metabolic flux, could be obtained by manipulating cofactor availability and balance. Significant changes in the ATP, NAD(H), NADP(H), or acetyl-CoA concentrations triggered relevant metabolic responses to acidic, oxidative, heat, and osmotic stress. Globally, the model icmNX6434 provides a comprehensive platform to elucidate the physiological effects of these cofactors on cell growth, metabolic flux, and industrial robustness. Moreover, the results of this study are a further example of using a consensus genome-scale metabolic model to increase our understanding of key biological processes.

The Pivotal Role of Protein Phosphorylation in the Control of Yeast Central Metabolism

Protein phosphorylation is the most frequent eukaryotic post-translational modification and can act as either a molecular switch or rheostat for protein functions. The deliberate manipulation of protein phosphorylation has great potential for regulating specific protein functions with surgical precision, rather than the gross effects gained by the over/underexpression or complete deletion of a protein-encoding gene. In order to assess the impact of phosphorylation on central metabolism, and thus its potential for biotechnological and medical exploitation, a compendium of highly confident protein phosphorylation sites (p-sites) for the model organism Saccharomyces cerevisiae has been analyzed together with two more datasets from the fungal pathogen Candida albicans. Our analysis highlights the global properties of the regulation of yeast central metabolism by protein phosphorylation, where almost half of the enzymes involved are subject to this sort of post-translational modification. These phosphorylated enzymes, compared to the nonphosphorylated ones, are more abundant, regulate more reactions, have more protein–protein interactions, and a higher fraction of them are ubiquitinated. The p-sites of metabolic enzymes are also more conserved than the background p-sites, and hundreds of them have the potential for regulating metabolite production. All this integrated information has allowed us to prioritize thousands of p-sites in terms of their potential phenotypic impact. This multi-source compendium should enable the design of future high-throughput (HTP) mutation studies to identify key molecular switches/rheostats for the manipulation of not only the metabolism of yeast, but also that of many other biotechnologically and medically important fungi and eukaryotes.

Enhancing Drug Efficacy and Therapeutic Index through Cheminformatics-Based Selection of Small Molecule Binary Weapons That Improve Transporter-Mediated Targeting: A Cytotoxicity System Based on Gemcitabine

The transport of drug molecules is mainly determined by the distribution of influx and efflux transporters for which they are substrates. To enable tissue targeting, we sought to develop the idea that we might affect the transporter-mediated disposition of small-molecule drugs via the addition of a second small molecule that of itself had no inhibitory pharmacological effect but that influenced the expression of transporters for the primary drug. We refer to this as a “binary weapon” strategy. The experimental system tested the ability of a molecule that on its own had no cytotoxic effect to increase the toxicity of the nucleoside analog gemcitabine to Panc1 pancreatic cancer cells. An initial phenotypic screen of a 500-member polar drug (fragment) library yielded three “hits.” The structures of 20 of the other 2,000 members of this library suite had a Tanimoto similarity greater than 0.7 to those of the initial hits, and each was itself a hit (the cheminformatics thus providing for a massive enrichment). We chose the top six representatives for further study. They fell into three clusters whose members bore reasonable structural similarities to each other (two were in fact isomers), lending strength to the self-consistency of both our conceptual and experimental strategies. Existing literature had suggested that indole-3-carbinol might play a similar role to that of our fragments, but in our hands it was without effect; nor was it structurally similar to any of our hits. As there was no evidence that the fragments could affect toxicity directly, we looked for effects on transporter transcript levels. In our hands, only the ENT1-3 uptake and ABCC2,3,4,5, and 10 efflux transporters displayed measurable transcripts in Panc1 cultures, along with a ribonucleoside reductase RRM1 known to affect gemcitabine toxicity. Very strikingly, the addition of gemcitabine alone increased the expression of the transcript for ABCC2 (MRP2) by more than 12-fold, and that of RRM1 by more than fourfold, and each of the fragment “hits” served to reverse this. However, an inhibitor of ABCC2 was without significant effect, implying that RRM1 was possibly the more significant player. These effects were somewhat selective for Panc cells. It seems, therefore, that while the effects we measured were here mediated more by efflux than influx transporters, and potentially by other means, the binary weapon idea is hereby fully confirmed: it is indeed possible to find molecules that manipulate the expression of transporters that are involved in the bioactivity of a pharmaceutical drug. This opens up an entirely new area, that of chemical genomics-based drug targeting.

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.

Genome-Wide Semi-Automated Annotation of Transporter Systems

Usually, transport reactions are added to genome-scale metabolic models (GSMMs) based on experimental data and literature. This approach does not allow associating specific genes with transport reactions, which impairs the ability of the model to predict effects of gene deletions. Novel methods for systematic genome-wide transporter functional annotation and their integration into GSMMs are therefore necessary. In this work, an automatic system to detect and classify all potential membrane transport proteins for a given genome and integrate the related reactions into GSMMs is proposed, based on the identification and classification of genes that encode transmembrane proteins. The Transport Reactions Annotation and Generation (TRIAGE) tool identifies the metabolites transported by each transmembrane protein and its transporter family. The localization of the carriers is also predicted and, consequently, their action is confined to a given membrane. The integration of the data provided by TRIAGE with highly curated models allowed the identification of new transport reactions. TRIAGE is included in the new release of merlin, a software tool previously developed by the authors, which expedites the GSMM reconstruction processes.

Perspectives and Challenges of Microbial Application for Crop Improvement

Global population increases and climate change pose a challenge to worldwide crop production. There is a need to intensify agricultural production in a sustainable manner and to find solutions to combat abiotic stress, pathogens, and pests. Plants are associated with complex microbiomes, which have an ability to promote plant growth and stress tolerance, support plant nutrition, and antagonize plant pathogens. The integration of beneficial plant-microbe and microbiome interactions may represent a promising sustainable solution to improve agricultural production. The widespread commercial use of the plant beneficial microorganisms will require a number of issues addressed. Systems approach using microscale information technology for microbiome metabolic reconstruction has potential to advance the microbial reproducible application under natural conditions.

Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models

Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design.

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.

Phosphorylation is a broad regulatory mechanism with implications in nearly all processes of the cell. However, a global understanding of possible regulatory mechanisms remains elusive. In this study, we examined the potential regulatory role of kinases, phosphatases and transcription-factors in yeast metabolism across a variety of steady-state and dynamic conditions. The main novelty of our analysis was to infer putative regulatory interactions from in silico estimated activity of transcription-factors and kinases/phosphatases. This provided functional information about the proteins important for the experimental conditions at hand that had not been uncovered before. We showed that activity profiles are predictive features to estimate metabolite changes in dynamic experiments, while the same was not visible in steady-state conditions. We also showed that dynamic experiments could be used to recapitulate and provide novel TFs-metabolite and K/Ps-metabolite regulatory associations. We believe these findings illustrates the usefulness of this approach for future integrative studies interested in studying metabolic regulation.

Spectral consensus strategy for accurate reconstruction of large biological networks

BACKGROUND

The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with few or no experimentally proven interactions. A striking example lies in the recent gut bacterial studies that provided researchers with a plethora of information sources. Despite a deeper knowledge of microbiome composition, inferring bacterial interactions remains a critical step that encounters significant issues, due in particular to high-dimensional settings, unknown gut bacterial taxa and unavoidable noise in sparse datasets. Such data type make any a priori choice of a learning method particularly difficult and urge the need for the development of new scalable approaches.

RESULTS

We propose a consensus method based on spectral decomposition, named Spectral Consensus Strategy, to reconstruct large networks from high-dimensional datasets. This novel unsupervised approach can be applied to a broad range of biological networks and the associated spectral framework provides scalability to diverse reconstruction methods. The results obtained on benchmark datasets demonstrate the interest of our approach for high-dimensional cases. As a suitable example, we considered the human gut microbiome co-presence network. For this application, our method successfully retrieves biologically relevant relationships and gives new insights into the topology of this complex ecosystem.

CONCLUSIONS

The Spectral Consensus Strategy improves prediction precision and allows scalability of various reconstruction methods to large networks. The integration of multiple reconstruction algorithms turns our approach into a robust learning method. All together, this strategy increases the confidence of predicted interactions from high-dimensional datasets without demanding computations.

Whole-genome metabolic model of Trichoderma reesei built by comparative reconstruction

BACKGROUND

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.

RESULTS

A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.

CONCLUSIONS

The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes.

Saccharomyces cerevisiae metabolism in ecological context

The architecture and regulation of Saccharomyces cerevisiae metabolic network are among the best studied owing to its widespread use in both basic research and industry. Yet, several recent studies have revealed notable limitations in explaining genotype-metabolic phenotype relations in this yeast, especially when concerning multiple genetic/environmental perturbations. Apparently unexpected genotype-phenotype relations may originate in the evolutionarily shaped cellular operating principles being hidden in common laboratory conditions. Predecessors of laboratory S. cerevisiae strains, the wild and the domesticated yeasts, have been evolutionarily shaped by highly variable environments, very distinct from laboratory conditions, and most interestingly by social life within microbial communities. Here we present a brief review of the genotypic and phenotypic peculiarities of S. cerevisiae in the context of its social lifestyle beyond laboratory environments. Accounting for this ecological context and the origin of the laboratory strains in experimental design and data analysis would be essential in improving the understanding of genotype-environment-phenotype relationships.

Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond

Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted.

In silico design of anaerobic growth-coupled product formation in Escherichia coli: experimental validation using a simple polyol, glycerol

Integrated approaches using in silico model-based design and advanced genetic tools have enabled efficient production of fuels, chemicals and functional ingredients using microbial cell factories. In this study, using a recently developed genome-scale metabolic model for Escherichia coli iJO1366, a mutant strain has been designed in silico for the anaerobic growth-coupled production of a simple polyol, glycerol. Computational complexity was significantly reduced by systematically reducing the target reactions used for knockout simulations. One promising penta knockout E. coli mutant (E. coli ΔadhE ΔldhA ΔfrdC ΔtpiA ΔmgsA) was selected from simulation study and was constructed experimentally by sequentially deleting five genes. The penta mutant E. coli bearing the Saccharomyces cerevisiae glycerol production pathway was able to grow anaerobically and produce glycerol as the major metabolite with up to 90% of theoretical yield along with stoichiometric quantities of acetate and formate. Using the penta mutant E. coli strain we have demonstrated that the ATP formation from the acetate pathway was essential for growth under anaerobic conditions. The general workflow developed can be easily applied to anaerobic production of other platform chemicals using E. coli as the cell factory.

Computational Metabolomics: A Framework for the Million Metabolome

"Sola dosis facit venenum." These words of Paracelsus, "the dose makes the poison", can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80-85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability-based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed.

In Vivo Analysis of NH4+ Transport and Central Nitrogen Metabolism in Saccharomyces cerevisiae during Aerobic Nitrogen-Limited Growth

Ammonium is the most common N source for yeast fermentations. Although its transport and assimilation mechanisms are well documented, there have been only a few attempts to measure the in vivo intracellular concentration of ammonium and assess its impact on gene expression. Using an isotope dilution mass spectrometry (IDMS)-based method, we were able to measure the intracellular ammonium concentration in N-limited aerobic chemostat cultivations using three different N sources (ammonium, urea, and glutamate) at the same growth rate (0.05 h^-1). The experimental results suggest that, at this growth rate, a similar concentration of intracellular (IC) ammonium, about 3.6 mmol NH₄⁺/liter_IC, is required to supply the reactions in the central N metabolism, independent of the N source. Based on the experimental results and different assumptions, the vacuolar and cytosolic ammonium concentrations were estimated. Furthermore, we identified a futile cycle caused by NH₃ leakage into the extracellular space, which can cost up to 30% of the ATP production of the cell under N-limited conditions, and a futile redox cycle between Gdh1 and Gdh2 reactions. Finally, using shotgun proteomics with protein expression determined relative to a labeled reference, differences between the various environmental conditions were identified and correlated with previously identified N compound-sensing mechanisms.IMPORTANCE In our work, we studied central N metabolism using quantitative approaches. First, intracellular ammonium was measured under different N sources. The results suggest that Saccharomyces cerevisiae cells maintain a constant NH₄⁺ concentration (around 3 mmol NH₄⁺/liter_IC), independent of the applied nitrogen source. We hypothesize that this amount of intracellular ammonium is required to obtain sufficient thermodynamic driving force. Furthermore, our calculations based on thermodynamic analysis of the transport mechanisms of ammonium suggest that ammonium is not equally distributed, indicating a high degree of compartmentalization in the vacuole. Additionally, metabolomic analysis results were used to calculate the thermodynamic driving forces in the central N metabolism reactions, revealing that the main reactions in the central N metabolism are far from equilibrium. Using proteomics approaches, we were able to identify major changes, not only in N metabolism, but also in C metabolism and regulation.

A multi-scale, multi-disciplinary approach for assessing the technological, economic and environmental performance of bio-based chemicals

In recent years, bio-based chemicals have gained interest as a renewable alternative to petrochemicals. However, there is a significant need to assess the technological, biological, economic and environmental feasibility of bio-based chemicals, particularly during the early research phase. Recently, the Multi-scale framework for Sustainable Industrial Chemicals (MuSIC) was introduced to address this issue by integrating modelling approaches at different scales ranging from cellular to ecological scales. This framework can be further extended by incorporating modelling of the petrochemical value chain and the de novo prediction of metabolic pathways connecting existing host metabolism to desirable chemical products. This multi-scale, multi-disciplinary framework for quantitative assessment of bio-based chemicals will play a vital role in supporting engineering, strategy and policy decisions as we progress towards a sustainable chemical industry.

The metabolome 18 years on: a concept comes of age

BACKGROUND

The term 'metabolome' was introduced to the scientific literature in September 1998.

AIM AND KEY SCIENTIFIC CONCEPTS OF THE REVIEW

To mark its 18-year-old 'coming of age', two of the co-authors of that paper review the genesis of metabolomics, whence it has come and where it may be going.

Efficient Reconstruction of Predictive Consensus Metabolic Network Models

Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.

Flux balance analysis of genome-scale metabolic model of rice (Oryza sativa): aiming to increase biomass

Due to socio-economic reasons, it is essential to design efficient stress-tolerant, more nutritious, high yielding rice varieties. A systematic understanding of the rice cellular metabolism is essential for this purpose. Here, we analyse a genome-scale metabolic model of rice leaf using Flux Balance Analysis to investigate whether it has potential metabolic flexibility to increase the biosynthesis of any of the biomass components. We initially simulate the metabolic responses under an objective to maximize the biomass components. Using the estimated maximum value of biomass synthesis as a constraint, we further simulate the metabolic responses optimizing the cellular economy. Depending on the physiological conditions of a cell, the transport capacities of intracellular transporters (ICTs) can vary. To mimic this physiological state, we randomly vary the ICTs' transport capacities and investigate their effects. The results show that the rice leaf has the potential to increase glycine and starch in a wide range depending on the ICTs' transport capacities. The predicted biosynthesis pathways vary slightly at the two different optimization conditions. With the constraint of biomass composition, the cell also has the metabolic plasticity to fix a wide range of carbon-nitrogen ratio.