Généralisation de modèles métaboliques par connaissances

Genome-scale metabolic models describe the relationships between thousands of reactions and biochemical molecules, and are used to improve our understanding of organism’s metabolism. They found applications in pharmaceutical, chemical and bioremediation industries.The complexity of metabolic models hampers many tasks that are important during the process of model inference, such as model comparison, analysis, curation and refinement by human experts. The abundance of details in large-scale networks can mask errors and important organism-specific adaptations. It is therefore important to find the right levels of abstraction that are comfortable for human experts. These abstract levels should highlight the essential model structure and the divergences from it, such as alternative paths or missing reactions, while hiding inessential details.To address this issue, we defined a knowledge-based generalization that allows for production of higher-level abstract views of metabolic network models. We developed a theoretical method that groups similar metabolites and reactions based on the network structure and the knowledge extracted from metabolite ontologies, and then compresses the network based on this grouping. We implemented our method as a python library, that is available for download from metamogen.gforge.inria.fr.To validate our method we applied it to 1 286 metabolic models from the Path2Model project, and showed that it helps to detect organism-, and domain-specific adaptations, as well as to compare models.Based on discussions with users about their ways of navigation in metabolic networks, we defined a 3-level representation of metabolic networks: the full-model level, the generalized level, the compartment level. We combined our model generalization method with the zooming user interface (ZUI) paradigm and developed Mimoza, a user-centric tool for zoomable navigation and knowledgebased exploration of metabolic networks that produces this 3-level representation. Mimoza is available both as an on-line tool and for download atmimoza.bordeaux.inria.fr.Les réseaux métaboliques à l’échelle génomique décrivent les relations entre milliers de réactions et molécules biochimiques pour améliorer notre compréhension du métabolisme. Ils trouvent des applications dans les domaines chimiques, pharmaceutiques, et dans la biorestauration.La complexité de modèles métaboliques mets des obstacles á l’inférence des modèles, à la comparaison entre eux, ainsi que leur analyse, curation et amélioration par des experts humains. Parce que l’abondance des détailles dans les réseaux à grande échelle peut cacher des erreurs et des adaptations importantes de l’espèce qui est étudié, c’est important de trouver les correct niveaux d’abstraction qui sont confortables pour les experts humains : on doit mettre en évidence la structure essentiel du modèle ainsi que les divergences de celle-là (par exemple les chemins alternatives et les réactions manquantes), tout en masquant les détails non significatifs.Pour répondre a cette demande nous avons défini une généralisation des modèles métaboliques, fondée sur les connaissances, qui permet la création des vues abstraites de réseaux métaboliques. Nous avons développé une méthode théorétique qui regroupe les métabolites en classes d’équivalence et factorise les réactions reliant ces classes d’équivalence. Nous avons réalisé cette méthode comme une bibliothèque Python qui peut être téléchargée depuis metamogen.gforge.inria.fr.Pour valider l’intérêt de notre méthode, nous l’avons appliquée à 1 286 modèles métaboliques que nous avons extraits de la ressource Path2Model. Nous avons montré que notre méthode aide l’expert humain à relever de façon automatique les adaptations spécifiques de certains espèces et à comparer les modèles entre eux.Après en avoir discuté avec des utilisateurs, nous avons décidé de définir trois niveaux hiérarchiques de représentation de réseaux métaboliques : les compartiments, les modules et les réactions détaillées. Nous avons combiné notre méthode de généralisation et le paradigme des interfaces zoomables pour développer Mimoza, un système de navigation dans les réseaux métaboliques qui crée et visualise ces trois niveaux. Mimoza est accessible en ligne et pour le téléchargement depuis le site mimoza.bordeaux.inria.fr

Metabolic Model Generalization

International audienceGenome-scale metabolic models for new organisms include thousands of reactions that are generated automatically: by inferring them from databases of reactions and pathways, existing models for similar organisms, etc. This process includes several iterations of the draft model analysis, error detection, and improvement; starting from more general issues and going deeper into details. Especially in the first iterations model evaluation by a human expert is important. But genome-scale models are targeted for computer simulation and analysis, and are too detailed and complicated to be easily understood by a human. For example, in the beta-oxidation of fatty acids pathway, a reaction missing for a particular fatty-acyl-CoA type (e.g. decanoyl-CoA) is a more specific issue than missing a whole enoyl-CoA hydratase step (which could happen if the corresponding enzyme is not found). But the abundance of reactions in the model (e.g. those corresponding to other beta-oxidation steps and presented for each of the different types of fatty-acyls-CoA) may hide this fact from the human. That is why we developed a method for knowledge-based scaling of metabolic models, providing a higher-level view of a model, keeping its essential structure and omitting the details

Professionalism as a Generalized Typical Model of a Professional in the Higher Education System

The purpose of the article is to consider the impact of professionalism and professional activities on creating a generalized model of the professional. The thesis stating that professionalism is a generalized typical model (image) of the professional, prevailing in mentality of a particular society and including normative and actual models, was substantiated. Generalized typical model of the professional in the higher education system was considered in terms of normative and actual professionalism. Normative model serves a benchmark in organizing professional activities and includes the requirements of the profession, education and human being

SPECIAL ASPECTS OF THE SECOND FOREIGN LANGUAGE TEACHING AT THE BASIC COURSE

The aim of this work is to define special aspects of the second foreign language (SFL) grammar teaching at the basic course, because at Ukrainian universities the number of course hours for studying of the SFL is less than for the first one. This leads to more superficial learning of grammar material. In addition, it was believed that the grammar of the SFL can be taught in the context of the general course. The integration of vocabulary and grammar teaching hampered the thorough understanding of grammatical constructions by the students, as the study of the SFL came against the background of the first foreign language enhanced studying. For the scientific analysis of this issue, the methods of causality analysis and generalization were applied, which helped identify the priority of the communicative approach in the SFL grammar teaching, as this approach develops the oral and written skills of students. Nevertheless, there are some factors that impede the fluency of grammar material at the basic course of education. The article reveals problems and ways to solve them during the training. The authors consider that the communicative approach in the SFL grammar teaching will help master the language more quickly and efficiently, which meets the needs of modern society

New forms of coal industry management

The article studies progress and problems of development of coal industry in modern Russia and views regulatory basis and perspectives of use of new forms of coal industry management, which include public-private partnership and formation of territorial clusters.peer-reviewe

Knowledge-based generalization of metabolic networks: a practical study

International audienceThe complex process of genome-scale metabolic network reconstruction involves semi- automatic reaction inference, analysis, and refinement through curation by human experts. Unfortunately, decisions by experts are hampered by the complexity of the network, which can mask errors in the inferred network. In order to aid an expert in making sense out of the thousands of reactions in the organism's metabolism, we developed a method for knowledge-based generalization that provides a higher-level view of the network, highlighting the particularities and essential structure, while hiding the details. In this study, we show the application of this generalization method to 1286 metabolic networks of organisms in Path2Models that describe fatty acid metabolism. We compare the generalized networks and show that we successfully highlight the aspects that are important for their curation and comparison

Knowledge-based generalization of metabolic models

International audienceGenome-scale metabolic model reconstruction is a complicated process beginning with (semi-)automatic inference of the reactions participating in the organism's metabolism, followed by many iterations of network analysis and improvement. Despite advances in automatic model inference and analysis tools, reconstruction may still miss some reactions or add erroneous ones. Consequently, a human expert's analysis of the model will continue to play an important role in all the iterations of the reconstruction process. This analysis is hampered by the size of the genome-scale models (typically thousands of reactions), which makes it hard for a human to understand them. To aid human experts in curating and analyzing metabolic models, we have developed a method for knowledge-based generalization that provides a higher-level view of a metabolic model, masking its inessential details while presenting its essential structure. The method groups biochemical species in the model into semantically equivalent classes based on the ChEBI ontology, identifies reactions that become equivalent with respect to the generalized species, and factors those reactions into generalized reactions. Generalization allows curators to quickly identify divergences from the expected structure of the model, such as alternative paths or missing reactions, that are the priority targets for further curation. We have applied our method to genome-scale yeast metabolic models and shown that it improves understanding by helping to identify both specificities and potential errors

Knowledge-based generalization of metabolic networks: An applicational study

International audienceGenome-scale metabolic networks are complex systems that describe thousands of reactions participating in the organism's metabolism. During the process of genome network reconstruction these reactions are automatically inferred from pathway and reaction databases, and existing models for similar organisms, using genomic data [1]. The inferred draft network is then refined during several iterations of error detection, gap filling, analysis and improvement [2]. Although automatic tools for model inference and analysis are becoming more and more powerful, they may still miss some reactions or add those ones that should not belong to the network of the target organism. That is why the analysis by a human expert is needed during the network refinement process. However, being tailored for a computer simulation, and thus including all the reactions thought to participate in the organism's metabolism, genome-scale networks can be too complicated and detailed for a human. The errors may be hidden in the multitude of reactions. To help a human expert understanding these detailed networks, we developed a method for knowledge-based generalization that focusses on the higher-level relationships in the network, while omitting the details [3]. The generalization process groups chemical species present in the network into semantically equivalent classes, based on their hierarchical relationships in the ChEBI ontology [4], and merges them into a generalized chemical species. For instance, butyryl-CoA, hexanoyl-CoA and octanoyl-CoA species can be generalized into fatty acyl-CoA. After the species generalization, reactions that share the same generalized reactants and the same generalized products, are factored together into a generalized reaction. This provides a higher-level view of the network. In this poster, we show the application of this generalization method to the network of the yeast Y. lypolitica [5]. We analyze the generalized network, and illustrate how it can be used for easier error detection: We show the changes that both initial and generalized networks undergo if the catalyzing enzyme for some of the reactions is missing. The application of the generalization procedure also facilitates network comparison, which we show by comparing the generalized Y. lypolitica network to the networks of several other organisms

Three-level representation of metabolic networks

National audienceThe complexity of genome-scale metabolic models makes them quite difficult for human users to read, since they contain thousands of reactions that must be included for accurate computer simulation. The web-based navigation system Mimoza allows a human expert to explore metabolic network models in a semantically zoomable manner: The most general view represents the compartments of the model; the next view shows the generalized versions of reactions and metabolites in each compartment; and the most detailed view represents the initial network with the generalization-based layout (where similar metabolites and reactions are placed next to each other). It allows a human expert to grasp the general structure of the network and analyze it in a top-down manner

Kinetic Simulation Algorithm Ontology

To enable the accurate and repeatable execution of a computational simulation task, it is important to identify both the algorithm used and the initial setup. These minimum information requirements are described by the MIASE guidelines. Since the details of some algorithms are not always publicly available, and many are implemented only in a limited number of simulation tools, it is crucial to identify alternative algorithms with similar characteristics that may be used to provide comparable results in an equivalent simulation experiment. The Kinetic Simulation Algorithm Ontology (KiSAO) was developed to address this issue by describing existing algorithms and their inter-relationships through their characteristics and parameters. The use of KiSAO in conjunction with simulation descriptions, such as SED-ML, will allow simulation software to automatically choose the best algorithm available to perform a simulation. The availability of algorithm parameters, together with their type may permit the automatic generation of user-interfaces to configure simulators. To enable making queries to KiSAO programmaticaly, from simulation experiment description editors and simulation tools, a java library libKiSAO was implemented