12 research outputs found

    Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

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    International audienceIncreasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system

    Étude de la coopération hôte-microbiote par des problèmes d'optimisation basés sur la complétion de réseaux métaboliques

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    Systems biology relies on computational biology to integrate knowledge and data, for a better understanding of organisms’ physiology. Challenges reside in the applicability of methods and tools to non-model organisms, for instance in marine biology. Sequencing advances and the growing importance of elucidating microbiotas’ roles, have led to an increased interest into these organisms. This thesis focuses on the modeling of the metabolism through networks, and of its functionality using graphs and constraints semantics. In particular, a first part presents work on gap-filling metabolic networks in the context of non-model organisms. A graph-based method is benchmarked and validated and a hybrid one is developed using Answer Set Programming (ASP) and linear programming. Such gap-filling is applied on algae and extended to decipher putative interactions between Ectocarpus siliculosus and a symbiotic bacterium. In this direction, the second part of the thesis aims at proposing formalisms and implementation of a tool for selecting and screening communities of interest within microbiotas. It enables to scale to large microbiotas and, with a two-step approach, to suggest symbionts that fit the desired objective. The modeling supports the computation of exchanges, and solving can cover the whole solution space. Applications are presented on the human gut microbiota and the selection of bacterial communities for a brown alga. Altogether, this thesis proposes modeling, software and biological applications using graph-based semantics to support the elaboration of hypotheses for elucidating the metabolism of organisms.La biologie des systèmes intègre données et connaissances par des méthodes bioinformatiques, afin de mieux appréhender la physiologie des organismes. Une problématique est l’applicabilité de ces techniques aux organismes non modèles, au centre de plus en plus d’études, grâce aux avancées de séquençage et à l’intérêt croissant de la recherche sur les microbiotes. Cette thèse s’intéresse à la modélisation du métabolisme par des réseaux, et de sa fonctionnalité par diverses sémantiques basées sur les graphes et les contraintes stoechiométriques. Une première partie présente des travaux sur la complétion de réseaux métaboliques pour les organismes non modèles. Une méthode basée sur les graphes est validée, et une seconde, hybride, est développée, en programmation par ensembles réponses (ASP). Ces complétions sont appliquées à des réseaux métaboliques d’algues en biologie marine, et étendues à la recherche de complémentarité métabolique entre Ectocarpus siliculosus et une bactérie symbiotique. En s’appuyant sur les méthodes de complétion, la seconde partie de la thèse vise à proposer et implémenter une sélection de communautés à l’échelle de grands microbiotes. Une approche en deux étapes permet de suggérer des symbiotes pour l’optimisation d’un objectif donné. Elle supporte la modélisation des échanges et couvre tout l’espace des solutions. Des applications sur le microbiote intestinal humain et la sélection de bactéries pour une algue brune sont présentées. Dans l’ensemble, cette thèse propose de modéliser, développer et appliquer des méthodes reposant sur des sémantiques de graphe pour élaborer des hypothèses sur le métabolisme des organismes

    ReCodLiver0.9: Overcoming Challenges in Genome-Scale Metabolic Reconstruction of a Non-model Species

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    The availability of genome sequences, annotations, and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale metabolic models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.publishedVersio

    LGEM+^\text{+}: a first-order logic framework for automated improvement of metabolic network models through abduction

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    Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation. We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure. We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics.Comment: 15 pages, one figure, two tables, two algorithm

    The genome of Ectocarpus subulatus highlights unique mechanisms for stress tolerance in brown algae

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    Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling.

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    Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.This work was partially funded by a Teesside University doctoral scholarship, EPSRC, and the EU grant MIMOMICS

    Probabilistic metabolic modeling of microbial communities

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    Microbial communities (microbiomes) comprise a vast component of life on our planet. They are involved in many fundamental processes, ranging from balancing global biogeochemical cycles to influencing human health. Recently, advances in genome sequencing technologies have allowed us to explore the genetic diversity of microbiomes in high-throughput, cataloging hundreds of thousands of microbial species and millions of genes. As genomic data is accumulating, the challenge remains: to translate genome sequences into functional predictions of relevant phenotypes. A promising approach to address this challenge is the annotation of genomic data to a metabolic network (referred to as genome-scale metabolic model reconstruction), which can then be analyzed to simulate metabolic phenotypes. Although this approach has provided valuable insight into microbial phenotypes, there are many sources of uncertainty in both reconstruction and analysis of genome-scale metabolic networks that currently limit their application. The development of improved reconstruction and analysis methods, and additional sources of data, that further address this uncertainty would facilitate our understanding of microbial community function. The first section of this dissertation is a review that outlines the major uncertainties along a general pipeline for genome-scale metabolic model reconstruction and analysis, and highlights existing approaches for addressing them. An emphasis is placed on probabilistic and ensemble based methods that can be used to formally represent uncertainty and facilitate the crystallization of metabolic network knowledge. The second section of this dissertation introduces a new probabilistic genome-scale metabolic model analysis method, inspired by percolation theory, to quantify the biosynthetic capabilities of microbial organisms in uncertain environments. This method was applied to microbial organisms from the human oral microbiome, providing broad insight into the structure of this microbial community. The third section of this dissertation describes the development of an experimental device to facilitate the collection of data related to metabolic interactions between microbes. The data collected with this device was probabilistically integrated with a mechanistic metabolic model to gain quantitative insight into the syntrophic interaction between an engineered E. coli auxotroph pair. Together, the work described in this dissertation introduces several novel probabilistic methods for metabolic modeling of microbial communities, and sets the stage for future work that can further improve our understanding of these important biological systems.2021-09-28T00:00:00

    In silico analysis of microbial communities through constraint-based metabolic modelling

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    Microbial communities are involved in many vital biological processes from elemental cycles to sustaining human health. The bacterial assemblages are remarkably under-studied as they are reluctant to grow in the laboratory conditions. Therefore, alternative omics-based approaches and computational modelling methods have been an active area of research to investigate microbial communities physiologically, ecologically and biochemically. In this thesis different microbial consortia involved in food production and also the human gut microbiota have been modelled and investigated. In the case of the human gut microbiota, the effects of malnutrition on the overall health of children from three different countries, namely, Malawi and Bangladesh, and Sweden have been studied. In each of the first two countries, a group of malnourished children going through food therapy as well as a healthy cohort were monitored to investigate the effect of food intervention on malnutrition, with their gut microbiota being the focal point. In this project, using metagenomics data we identified the dominant strains in each cohort, reconstructed genome-scale metabolic models (GEMs) for the most abundant ones and used our models to predict diet-microbe, microbe-microbe, and microbe- host interactions. Based on our results in this project, in addition to being less diverse, the gut microbiota of malnourished children showed a lower potency regarding the production of valuable metabolites. The second investigated microbial consortia were the ones used in fermented milk products. Based on the genome sequence and also experimental data for five selected strains, we reconstructed GEMs, curated the models and performed community modelling to predict their metabolic interactions. Using the simulation outcomes, we could predict a ratio for bacterial strains used in yogurt starter culture to maximise the production of acetaldehyde which is a key contributor to yogurt’s unique taste and aroma. GEMs are powerful tools to model an organism’s metabolic capabilities, and although numerous GEMs have been reconstructed, their quality control has not gained enough attention. Evaluation of a repository of semi-automatically reconstructed GEMs related to the human gut microbiota and another repository of manually curated ones was performed comparatively. Assessing these models from topological and functional aspects, it was shown that semi-automatically reconstructed models required extensive manual curation before they could be used for target-specific simulations. In constraint-based modelling, an objective function is usually optimised under particular environmental conditions, however, in case of the microbial communities, there is no distinct and relevant objective function. Therefore, an unbiased uniform randomised sampling algorithm was implemented for microbial communities. The samples acquired from the solution space were analysed statistically to see clustering patterns of the reactions and commensalistic relationships between the community members were identified. Overall, computational modelling paves the way towards gaining a mechanistic understanding of microbial communities and provides us with testable hypotheses and insight
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