In silico analysis of design of experiment methods for metabolic pathway optimization.

Microbial cell factories allow the production of chemicals presenting an alternative to traditional fossil fuel-dependent production. However, finding the optimal expression of production pathway genes is crucial for the development of efficient production strains. Unlike sequential experimentation, combinatorial optimization captures the relationships between pathway genes and production, albeit at the cost of conducting multiple experiments. Fractional factorial designs followed by linear modeling and statistical analysis reduce the experimental workload while maximizing the information gained during experimentation. Although tools to perform and analyze these designs are available, guidelines for selecting appropriate factorial designs for pathway optimization are missing. In this study, we leverage a kinetic model of a seven-genes pathway to simulate the performance of a full factorial strain library. We compare this approach to resolution V, IV, III, and Plackett Burman (PB) designs. Additionally, we evaluate the performance of these designs as training sets for a random forest algorithm aimed at identifying best-producing strains. Evaluating the robustness of these designs to noise and missing data, traits inherent to biological datasets, we find that while resolution V designs capture most information present in full factorial data, they necessitate the construction of a large number of strains. On the other hand, resolution III and PB designs fall short in identifying optimal strains and miss relevant information. Besides, given the small number of experiments required for the optimization of a pathway with seven genes, linear models outperform random forest. Consequently, we propose the use of resolution IV designs followed by linear modeling in Design-Build-Test-Learn (DBTL) cycles targeting the screening of multiple factors. These designs enable the identification of optimal strains and provide valuable guidance for subsequent optimization cycles.

Machine Learning-Guided Optimization of p-Coumaric Acid Production in Yeast.

Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a biobased economy. To use them effectively, appropriate connections between the phases of the cycle are crucial. Using p-coumaric acid (pCA) production in Saccharomyces cerevisiae as a case study, we propose the use of one-pot library generation, random screening, targeted sequencing, and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of the ML models strongly enable pathway optimization and propose feature importance and Shapley additive explanation values as a guide to expand the design space of original libraries. This approach allowed a 68% increased production of pCA within two DBTL cycles, leading to a 0.52 g/L titer and a 0.03 g/g yield on glucose.

Combinatorial optimization of pathway, process and media for the production of p-coumaric acid by Saccharomyces cerevisiae.

Microbial cell factories are instrumental in transitioning towards a sustainable bio-based economy, offering alternatives to conventional chemical processes. However, fulfilling their potential requires simultaneous screening for optimal media composition, process and genetic factors, acknowledging the complex interplay between the organism's genotype and its environment. This study employs statistical design of experiments to systematically explore these relationships and optimize the production of p-coumaric acid (pCA) in Saccharomyces cerevisiae. Two rounds of fractional factorial designs were used to identify factors with a significant effect on pCA production, which resulted in a 168-fold variation in pCA titre. Moreover, a significant interaction between the culture temperature and expression of ARO4 highlighted the importance of simultaneous process and strain optimization. The presented approach leverages the strengths of experimental design and statistical analysis and could be systematically applied during strain and bioprocess design efforts to unlock the full potential of microbial cell factories.

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia.

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

A bacterial sulfoglycosidase highlights mucin O-glycan breakdown in the gut ecosystem.

Mucinolytic bacteria modulate host-microbiota symbiosis and dysbiosis through their ability to degrade mucin O-glycans. However, how and to what extent bacterial enzymes are involved in the breakdown process remains poorly understood. Here we focus on a glycoside hydrolase family 20 sulfoglycosidase (BbhII) from Bifidobacterium bifidum, which releases N-acetylglucosamine-6-sulfate from sulfated mucins. Glycomic analysis showed that, in addition to sulfatases, sulfoglycosidases are involved in mucin O-glycan breakdown in vivo and that the released N-acetylglucosamine-6-sulfate potentially affects gut microbial metabolism, both of which were also supported by a metagenomic data mining analysis. Enzymatic and structural analysis of BbhII reveals the architecture underlying its specificity and the presence of a GlcNAc-6S-specific carbohydrate-binding module (CBM) 32 with a distinct sugar recognition mode that B. bifidum takes advantage of to degrade mucin O-glycans. Comparative analysis of the genomes of prominent mucinolytic bacteria also highlights a CBM-dependent O-glycan breakdown strategy used by B. bifidum.

Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels.

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.

Tailoring and optimizing fatty acid production by oleaginous yeasts through the systematic exploration of their physiological fitness.

BACKGROUND: The use of palm oil for our current needs is unsustainable. Replacing palm oil with oils produced by microbes through the conversion of sustainable feedstocks is a promising alternative. However, there are major technical challenges that must be overcome to enable this transition. Foremost among these challenges is the stark increase in lipid accumulation and production of higher content of specific fatty acids. Therefore, there is a need for more in-depth knowledge and systematic exploration of the oil productivity of the oleaginous yeasts. In this study, we cultivated Cutaneotrichosporon oleaginosus and Yarrowia lipolytica at various C/N ratios and temperatures in a defined medium with glycerol as carbon source and urea as nitrogen source. We ascertained the synergistic effect between various C/N ratios of a defined medium at different temperatures with Response Surface Methodology (RSM) and explored the variation in fatty acid composition through Principal Component Analysis. RESULTS: By applying RSM, we determined a temperature of 30 °C and a C/N ratio of 175 g/g to enable maximal oil production by C. oleaginosus and a temperature of 21 °C and a C/N ratio of 140 g/g for Y. lipolytica. We increased production by 71% and 66% respectively for each yeast compared to the average lipid accumulation in all tested conditions. Modulating temperature enabled us to steer the fatty acid compositions. Accordingly, switching from higher temperature to lower cultivation temperature shifted the production of oils from more saturated to unsaturated by 14% in C. oleaginosus and 31% in Y. lipolytica. Higher cultivation temperatures resulted in production of even longer saturated fatty acids, 3% in C. oleaginosus and 1.5% in Y. lipolytica. CONCLUSIONS: In this study, we provided the optimum C/N ratio and temperature for C. oleaginosus and Y. lipolytica by RSM. Additionally, we demonstrated that lipid accumulation of both oleaginous yeasts was significantly affected by the C/N ratio and temperature. Furthermore, we systematically analyzed the variation in fatty acids composition and proved that changing the C/N ratio and temperature steer the composition. We have further established these oleaginous yeasts as platforms for production of tailored fatty acids.

Intraspecies characterization of bacteria via evolutionary modeling of protein domains.

The ability to detect and characterize bacteria within a biological sample is crucial for the monitoring of infections and epidemics, as well as for the study of human health and its relationship with commensal microorganisms. To this aim, a commonly used technique is the 16S rRNA gene targeted sequencing. PCR-amplified 16S sequences derived from the sample of interest are usually clustered into the so-called Operational Taxonomic Units (OTUs) based on pairwise similarities. Then, representative OTU sequences are compared with reference (human-made) databases to derive their phylogeny and taxonomic classification. Here, we propose a new reference-free approach to define the phylogenetic distance between bacteria based on protein domains, which are the evolving units of proteins. We extract the protein domain profiles of 3368 bacterial genomes and we use an ecological approach to model their Relative Species Abundance distribution. Based on the model parameters, we then derive a new measurement of phylogenetic distance. Finally, we show that such model-based distance is capable of detecting differences between bacteria in cases in which the 16S rRNA-based method fails, providing a possibly complementary approach , which is particularly promising for the analysis of bacterial populations measured by shotgun sequencing.

SALARECON connects the Atlantic salmon genome to growth and feed efficiency.

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

Classification of the plant-associated lifestyle of Pseudomonas strains using genome properties and machine learning.

The rhizosphere, the region of soil surrounding roots of plants, is colonized by a unique population of Plant Growth Promoting Rhizobacteria (PGPR). Many important PGPR as well as plant pathogens belong to the genus Pseudomonas. There is, however, uncertainty on the divide between beneficial and pathogenic strains as previously thought to be signifying genomic features have limited power to separate these strains. Here we used the Genome properties (GP) common biological pathways annotation system and Machine Learning (ML) to establish the relationship between the genome wide GP composition and the plant-associated lifestyle of 91 Pseudomonas strains isolated from the rhizosphere and the phyllosphere representing both plant-associated phenotypes. GP enrichment analysis, Random Forest model fitting and feature selection revealed 28 discriminating features. A test set of 75 new strains confirmed the importance of the selected features for classification. The results suggest that GP annotations provide a promising computational tool to better classify the plant-associated lifestyle.

Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum.

BACKGROUND: Microbial production of propionate from diluted streams of ethanol (e.g., deriving from syngas fermentation) is a sustainable alternative to the petrochemical production route. Yet, few ethanol-fermenting propionigenic bacteria are known, and understanding of their metabolism is limited. Anaerotignum neopropionicum is a propionate-producing bacterium that uses the acrylate pathway to ferment ethanol and CO2 to propionate and acetate. In this work, we used computational and experimental methods to study the metabolism of A. neopropionicum and, in particular, the pathway for conversion of ethanol into propionate. RESULTS: Our work describes iANEO_SB607, the first genome-scale metabolic model (GEM) of A. neopropionicum. The model was built combining the use of automatic tools with an extensive manual curation process, and it was validated with experimental data from this and published studies. The model predicted growth of A. neopropionicum on ethanol, lactate, sugars and amino acids, matching observed phenotypes. In addition, the model was used to implement a dynamic flux balance analysis (dFBA) approach that accurately predicted the fermentation profile of A. neopropionicum during batch growth on ethanol. A systematic analysis of the metabolism of A. neopropionicum combined with model simulations shed light into the mechanism of ethanol fermentation via the acrylate pathway, and revealed the presence of the electron-transferring complexes NADH-dependent reduced ferredoxin:NADP+ oxidoreductase (Nfn) and acryloyl-CoA reductase-EtfAB, identified for the first time in this bacterium. CONCLUSIONS: The realisation of the GEM iANEO_SB607 is a stepping stone towards the understanding of the metabolism of the propionate-producer A. neopropionicum. With it, we have gained insight into the functioning of the acrylate pathway and energetic aspects of the cell, with focus on the fermentation of ethanol. Overall, this study provides a basis to further exploit the potential of propionigenic bacteria as microbial cell factories.

Fermentative Indole Production via Bacterial Tryptophan Synthase Alpha Subunit and Plant Indole-3-Glycerol Phosphate Lyase Enzymes.

Indole is produced in nature by diverse organisms and exhibits a characteristic odor described as animal, fecal, and floral. In addition, it contributes to the flavor in foods, and it is applied in the fragrance and flavor industry. In nature, indole is synthesized either from tryptophan by bacterial tryptophanases (TNAs) or from indole-3-glycerol phosphate (IGP) by plant indole-3-glycerol phosphate lyases (IGLs). While it is widely accepted that the tryptophan synthase α-subunit (TSA) has intrinsically low IGL activity in the absence of the tryptophan synthase ß-subunit, in this study, we show that Corynebacterium glutamicum TSA functions as a bona fide IGL and can support fermentative indole production in strains providing IGP. By bioprospecting additional bacterial TSAs and plant IGLs that function as bona fide IGLs were identified. Capturing indole in an overlay enabled indole production to titers of about 0.7 g L-1 in fermentations using C. glutamicum strains expressing either the endogenous TSA gene or the IGL gene from wheat.

A large-scale analysis of codon usage bias in 4868 bacterial genomes shows association of codon adaptation index with GC content, protein functional domains and bacterial phenotypes.

Multiple synonymous codons code for the same amino acid, resulting in the degeneracy of the genetic code and in the preferred used of some codons called codon bias usage (CBU). We performed a large-scale analysis of codon usage bias analysing the distribution of the codon adaptation index (CAI) and the codon relative adaptiveness index (RA) in 4868 bacterial genomes. We found that CAI values differ significantly between protein functional domains and part of the protein outside domains and show how CAI, GC content and preferred usage of polymerase III alpha subunits are related. Additionally, we give evidence of the association between CAI and bacterial phenotypes.

Production of indole by Corynebacterium glutamicum microbial cell factories for flavor and fragrance applications.

BACKGROUND: The nitrogen containing aromatic compound indole is known for its floral odor typical of jasmine blossoms. Due to its characteristic scent, it is frequently used in dairy products, tea drinks and fine fragrances. The demand for natural indole by the flavor and fragrance industry is high, yet, its abundance in essential oils isolated from plants such as jasmine and narcissus is low. Thus, there is a strong demand for a sustainable method to produce food-grade indole. RESULTS: Here, we established the biotechnological production of indole upon L-tryptophan supplementation in the bacterial host Corynebacterium glutamicum. Heterologous expression of the tryptophanase gene from E. coli enabled the conversion of supplemented L-tryptophan to indole. Engineering of the substrate import by co-expression of the native aromatic amino acid permease gene aroP increased whole-cell biotransformation of L-tryptophan to indole by two-fold. Indole production to 0.2 g L-1 was achieved upon feeding of 1 g L-1 L-tryptophan in a bioreactor cultivation, while neither accumulation of side-products nor loss of indole were observed. To establish an efficient and robust production process, new tryptophanases were recruited by mining of bacterial sequence databases. This search retrieved more than 400 candidates and, upon screening of tryptophanase activity, nine new enzymes were identified as most promising. The highest production of indole in vivo in C. glutamicum was achieved based on the tryptophanase from Providencia rettgeri. Evaluation of several biological aspects identified the product toxicity as major bottleneck of this conversion. In situ product recovery was applied to sequester indole in a food-grade organic phase during the fermentation to avoid inhibition due to product accumulation. This process enabled complete conversion of L-tryptophan and an indole product titer of 5.7 g L-1 was reached. Indole partitioned to the organic phase which contained 28 g L-1 indole while no other products were observed indicating high indole purity. CONCLUSIONS: The bioconversion production process established in this study provides an attractive route for sustainable indole production from tryptophan in C. glutamicum. Industrially relevant indole titers were achieved within 24 h and indole was concentrated in the organic layer as a pure product after the fermentation.

Enzyme-constrained models predict the dynamics of Saccharomyces cerevisiae growth in continuous, batch and fed-batch bioreactors.

Genome-scale, constraint-based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bioprocess design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme-constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well-known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyse flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell-based processes and, thus, the usefulness of such models.

Systems Biology in ELIXIR: modelling in the spotlight.

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Model-driven approach for the production of butyrate from CO2/H2 by a novel co-culture of C. autoethanogenum and C. beijerinckii.

One-carbon (C1) compounds are promising feedstocks for the sustainable production of commodity chemicals. CO2 is a particularly advantageous C1-feedstock since it is an unwanted industrial off-gas that can be converted into valuable products while reducing its atmospheric levels. Acetogens are microorganisms that can grow on CO2/H2 gas mixtures and syngas converting these substrates into ethanol and acetate. Co-cultivation of acetogens with other microbial species that can further process such products, can expand the variety of products to, for example, medium chain fatty acids (MCFA) and longer chain alcohols. Solventogens are microorganisms known to produce MCFA and alcohols via the acetone-butanol-ethanol (ABE) fermentation in which acetate is a key metabolite. Thus, co-cultivation of an acetogen and a solventogen in a consortium provides a potential platform to produce valuable chemicals from CO2. In this study, metabolic modeling was implemented to design a new co-culture of an acetogen and a solventogen to produce butyrate from CO2/H2 mixtures. The model-driven approach suggested the ability of the studied solventogenic species to grow on lactate/glycerol with acetate as co-substrate. This ability was confirmed experimentally by cultivation of Clostridium beijerinckii on these substrates in batch serum bottles and subsequently in pH-controlled bioreactors. Community modeling also suggested that a novel microbial consortium consisting of the acetogen Clostridium autoethanogenum, and the solventogen C. beijerinckii would be feasible and stable. On the basis of this prediction, a co-culture was experimentally established. C. autoethanogenum grew on CO2/H2 producing acetate and traces of ethanol. Acetate was in turn, consumed by C. beijerinckii together with lactate, producing butyrate. These results show that community modeling of metabolism is a valuable tool to guide the design of microbial consortia for the tailored production of chemicals from renewable resources.

Proteomic Analysis of a Syntrophic Coculture of Syntrophobacter fumaroxidans MPOBT and Geobacter sulfurreducens PCAT.

We established a syntrophic coculture of Syntrophobacter fumaroxidans MPOBT (SF) and Geobacter sulfurreducens PCAT (GS) growing on propionate and Fe(III). Neither of the bacteria was capable of growth on propionate and Fe(III) in pure culture. Propionate degradation by SF provides acetate, hydrogen, and/or formate that can be used as electron donors by GS with Fe(III) citrate as electron acceptor. Proteomic analyses of the SF-GS coculture revealed propionate conversion via the methylmalonyl-CoA (MMC) pathway by SF. The possibility of interspecies electron transfer (IET) via direct (DIET) and/or hydrogen/formate transfer (HFIT) was investigated by comparing the differential abundance of associated proteins in SF-GS coculture against (i) SF coculture with Methanospirillum hungatei (SF-MH), which relies on HFIT, (ii) GS pure culture growing on acetate, formate, hydrogen as propionate products, and Fe(III). We noted some evidence for DIET in the SF-GS coculture, i.e., GS in the coculture showed significantly lower abundance of uptake hydrogenase (43-fold) and formate dehydrogenase (45-fold) and significantly higher abundance of proteins related to acetate metabolism (i.e., GltA; 62-fold) compared to GS pure culture. Moreover, SF in the SF-GS coculture showed significantly lower abundance of IET-related formate dehydrogenases, Fdh3 (51-fold) and Fdh5 (29-fold), and the rate of propionate conversion in SF-GS was 8-fold lower than in the SF-MH coculture. In contrast, compared to GS pure culture, we found lower abundance of pilus-associated cytochrome OmcS (2-fold) and piliA (5-fold) in the SF-GS coculture that is suggested to be necessary for DIET. Furthermore, neither visible aggregates formed in the SF-GS coculture, nor the pili-E of SF (suggested as e-pili) were detected. These findings suggest that the IET mechanism is complex in the SF-GS coculture and can be mediated by several mechanisms rather than one discrete pathway. Our study can be further useful in understanding syntrophic propionate degradation in bioelectrochemical and anaerobic digestion systems.

Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism.

BACKGROUND: Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values. RESULTS: Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes. CONCLUSION: We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.

Low Toxicological Impact of Commercial Pristine Multi-Walled Carbon Nanotubes on the Yeast Saccharomyces cerevisiae.

Carbon nanotubes (CNTs) have attracted the attention of academy and industry due to their potential applications, being currently produced and commercialized at a mass scale, but their possible impact on different biological systems remains unclear. In the present work, an assessment to understand the toxicity of commercial pristine multi-walled carbon nanotubes (MWCNTs) on the unicellular fungal model Saccharomyces cerevisiae is presented. Firstly, the nanomaterial was physico-chemically characterized, to obtain insights concerning its morphological features and elemental composition. Afterwards, a toxicology assessment was carried out, where it could be observed that cell proliferation was negatively affected only in the presence of 800 mg L-1 for 24 h, while oxidative stress was induced at a lower concentration (160 mg L-1) after a short exposure period (2 h). Finally, to identify possible toxicity pathways induced by the selected MWCNTs, the transcriptome of S. cerevisiae exposed to 160 and 800 mg L-1, for two hours, was studied. In contrast to a previous study, reporting massive transcriptional changes when yeast cells were exposed to graphene nanoplatelets in the same exposure conditions, only a small number of genes (130) showed significant transcriptional changes in the presence of MWCNTs, in the higher concentration tested (800 mg L-1), and most of them were found to be downregulated, indicating a limited biological response of the yeast cells exposed to the selected pristine commercial CNTs.