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

Systems biology-guided identification of synthetic lethal gene pairs and its potential use to discover antibiotic combinations

Mathematical models of metabolism from bacterial systems biology have proven their utility across multiple fields, for example metabolic engineering, growth phenotype simulation, and biological discovery. The usefulness of the models stems from their ability to compute a link between genotype and phenotype, but their ability to accurately simulate gene-gene interactions has not been investigated extensively. Here we assess how accurately a metabolic model for Escherichia coli computes one particular type of gene-gene interaction, synthetic lethality, and find that the accuracy rate is between 25% and 43%. The most common failure modes were incorrect computation of single gene essentiality and biological information that was missing from the model. Moreover, we performed virtual and biological screening against several synthetic lethal pairs to explore whether two-compound formulations could be found that inhibit the growth of Gram-negative bacteria. One set of molecules was identified that, depending on the concentrations, inhibits E. coli and S. enterica serovar Typhimurium in an additive or antagonistic manner. These findings pinpoint specific ways in which to improve the predictive ability of metabolic models, and highlight one potential application of systems biology to drug discovery and translational medicine

Integration and Validation of the GenomeScale Metabolic Models of Pichia pastoris : a Comprehensive Update of Protein Glycosylation Pathways, Lipid and Energy Metabolism

Motivation: Genome-scale metabolic models (GEMs) are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is one of the most widely used organisms for heterologous protein expression. There are different GEMs for this methylotrophic yeast of which the most relevant and complete in the published literature are iPP668, PpaMBEL1254 and iLC915. However, these three models differ regarding certain pathways, terminology for metabolites and reactions and annotations. Moreover, GEMs for some species are typically built based on the reconstructed models of related model organisms. In these cases, some organism-specific pathways could be missing or misrepresented. Results: In order to provide an updated and more comprehensive GEM for P. pastoris, we have reconstructed and validated a consensus model integrating and merging all three existing models. In this step a comprehensive review and integration of the metabolic pathways included in each one of these three versions was performed. In addition, the resulting iMT1026 model includes a new description of some metabolic processes. Particularly new information described in recently published literature is included, mainly related to fatty acid and sphingolipid metabolism, glycosylation and cell energetics. Finally the reconstructed model was tested and validated, by comparing the results of the simulations with available empirical physiological datasets results obtained from a wide range of experimental conditions, such as different carbon sources, distinct oxygen availability conditions, as well as producing of two different recombinant proteins. In these simulations, the iMT1026 model has shown a better performance than the previous existing models

A community-driven reconstruction of the Aspergillus niger metabolic network

Background: Aspergillus niger is an important fungus used in industrial applications for enzyme and acid production. To enable rational metabolic engineering of the species, available information can be collected and integrated in a genome-scale model to devise strategies for improving its performance as a host organism. Results: In this paper, we update an existing model of A. niger metabolism to include the information collected from 876 publications, thereby expanding the coverage of the model by 940 reactions, 777 metabolites and 454 genes. In the presented consensus genome-scale model of A. niger iJB1325 , we integrated experimental data from publications and patents, as well as our own experiments, into a consistent network. This information has been included in a standardized way, allowing for automated testing and continuous improvements in the future. This repository of experimental data allowed the definition of 471 individual test cases, of which the model complies with 373 of them. We further re-analyzed existing transcriptomics and quantitative physiology data to gain new insights on metabolism. Additionally, the model contains 3482 checks on the model structure, thereby representing the best validated genome-scale model on A. niger developed until now. Strain-specific model versions for strains ATCC 1015 and CBS 513.88 have been created containing all data used for model building, thereby allowing users to adopt the models and check the updated version against the experimental data. The resulting model is compliant with the SBML standard and therefore enables users to easily simulate it using their preferred software solution. Conclusion: Experimental data on most organisms are scattered across hundreds of publications and several repositories.To allow for a systems level understanding of metabolism, the data must be integrated in a consistent knowledge network. The A. niger iJB1325 model presented here integrates the available data into a highly curated genome-scale model to facilitate the simulation of flux distributions, as well as the interpretation of other genome-scale data by providing the metabolic context

Improvement of in silico strain engineering methods in Saccharomyces cerevisiae

PhD thesis in BioengineeringThe buildup of knowledge about microbial metabolism and the development of genome engineering techniques gave rise to the rational modification of microorganisms in order to use them to biosynthesize chemicals of industrial interest. Recently, the construction of genome-scale metabolic models (GSMMs) allowed the design of strain engineering strategies in silico. This thesis focused on the study and improvement of in silico strain engineering methodologies using Saccharomyces cerevisiae as a case study organism. Firstly, in order to investigate the accuracy of the GSMMs available for S. cerevisiae, their capacity to simulate the intracellular fluxes in central metabolism was tested. The results revealed that the simulations contained relevant errors in important areas of the central metabolism. A careful manual curation of the feasibility of all reactions producing or consuming NADH / NADPH resulted in the improvement of many fluxes in central metabolic pathways when compared to fluxes measured experimentally. The lack of a simulation method that could predict in quantitative terms the phenotype of strains with complex engineered genotypes, led to the development of a novel simulation method called turnover dependent phenotypic simulation (TDPS). This method was designed with the goal of simulating the majority of the genetic modifications usually implemented in engineered strains. The assumption that the production turnover of a metabolite can be used as an indication of its abundance was used in the formulation of TDPS in order to take into account the availability of resources when modelling genetic modifications. TDPS was validated using metabolically engineered S. cerevisiae strains available in the literature by comparing the production yields of the target metabolite. TDPS was then applied to the optimization of the availability of cytosolic acetyl-CoA in S. cerevisiae, by using an evolutionary algorithm to search for sets of genetic alterations that could improve the production yield of 3-hydroxypropionic acid (3-HP) derived from acetyl-CoA. Although the yields obtained experimentally were considerably lower than the simulations suggested, a positive effect on the 3-HP yield was observed for the downregulation of the pyruvate dehydrogenase complex and the deletion of ACH1 (succinyl- CoA:acetate CoA-transferase).O progresso que tem sido feito na área da fisiologia microbiana, juntamente com o desenvolvimento de técnicas de engenharia genética, permitiu a criação de estirpes microbianas modificadas racionalmente com o intuito de optimizar a produção de compostos de interesse industrial. Mais recentemente, a construção de modelos metabólicos à escala genómica (MMEG) proporcionou o desenho de estirpes modificadas in silico. Esta tese focou-se no estudo e melhoramento de metodologias de manipulação de estirpes in silico, usando Saccharomyces cerevisiae como caso de estudo. De forma a investigar a precisão dos MMEG disponíveis para S. cerevisiae, a sua capacidade para simular os fluxos intracelulares foi testada. Os resultados mostraram que os fluxos simulados continham erros em áreas importantes do metabolismo central e que a curação manual das reacções envolvidas no metabolismo de NADH e NADPH resulta em melhorias significativas nos fluxos metabólicos centrais. A ausência de um método de simulação que conseguisse prever quantitativamente o fenótipo de estirpes com genótipos complexos, levou ao desenvolvimento de um método novo designado por turnover dependent phenotypic simulation (TDPS). Este método foi concebido com o objectivo de simular a maior parte das modificações genéticas normalmente implementadas em estripes modificadas. A formulação do TDPS teve como base o uso do nível de produção de um metabolito como indicador da sua abundancia, de forma a modelar as modificações genéticas em função da disponibilidade de recursos. A validação deste método foi feita usando dados da literatura sobre estirpes geneticamente modificadas de S. cerevisiae, através da comparação dos rendimentos simulados e reais. O método de simulação TDPS foi posteriormente aplicado na optimização da produção de acetil-CoA no citosol de S. cerevisiae, usando um algoritmo evolucionário para procurar conjuntos de alterações genéticas que aumentassem a produção de ácido 3- hidroxipropiónico derivado de acetil-CoA. Apesar dos rendimentos experimentais serem mais baixos que as simulações sugeriam, observou-se um efeito positivo da sub-regulação do complexo da piruvato desidrogenase e da eliminação do gene ACH1 (succinil- CoA:acetato CoA-transferase).Esta investigação foi financiada pela Fundação para a Ciência e Tecnologia através da concessão de uma bolsa de doutoramento (SFRH/BD/51111/2010), co-financiada pelo POPH - QREN - Tipologia 4.1 -Formação Avançada - e comparticipados pelo Fundo Social Europeu (FSE) e por fundos nacionais do Ministério da Ciência, Tecnologia e Ensino Superior (MCTES)

Version 6 of the consensus yeast metabolic network refines biochemical coverage and improves model performance

Updates to maintain a state-of-the art reconstruction of the yeast metabolic network are essential to reflect our understanding of yeast metabolism and functional organization, to eliminate any inaccuracies identified in earlier iterations, to improve predictive accuracy and to continue to expand into novel subsystems to extend the comprehensiveness of the model. Here, we present version 6 of the consensus yeast metabolic network (Yeast 6) as an update to the community effort to computationally reconstruct the genome-scale metabolic network of Saccharomyces cerevisiae S288c. Yeast 6 comprises 1458 metabolites participating in 1888 reactions, which are annotated with 900 yeast genes encoding the catalyzing enzymes. Compared with Yeast 5, Yeast 6 demonstrates improved sensitivity, specificity and positive and negative predictive values for predicting gene essentiality in glucose-limited aerobic conditions when analyzed with flux balance analysis. Additionally, Yeast 6 improves the accuracy of predicting the likelihood that a mutation will cause auxotrophy. The network reconstruction is available as a Systems Biology Markup Language (SBML) file enriched with Minimium Information Requested in the Annotation of Biochemical Models (MIRIAM)-compliant annotations. Small- and macromolecules in the network are referenced to authoritative databases such as Uniprot or ChEBI. Molecules and reactions are also annotated with appropriate publications that contain supporting evidence. Yeast 6 is freely available at http://yeast.sf.net/ as three separate SBML files: a model using the SBML level 3 Flux Balance Constraint package, a model compatible with the MATLAB® COBRA Toolbox for backward compatibility and a reconstruction containing only reactions for which there is experimental evidence (without the non-biological reactions necessary for simulating growth)

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided