Analyse de l'étalement temporel de la floraison et influence sur la variabilité intra-arbre de la chute et de la croissance précoce des pêches

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An ontology to structure biological data: the contribution of mathematical models

The biology is a research field well known for its huge quantity and diversity of data. Today, these data are still recognized as heterogeneous and fragmented. Despite the fact that several initiatives of biological knowledge representation have been realized, biologists and bioinformaticians do not have a formal representation that, at the level of the entire organism, can help them to organize such a diversity and quantity of data. Recently, in the context of the whole cell modeling approach, the systemic mathematical models have proven to be a powerful tool for understanding the bacterial cell behavior. We advocate that an ontology built on the principles that govern the design of such models, can help to organize the biological data. In this article, we describe the first step in the conception of an ontology dedicated to biological data organization at the level of the entire organism and for molecular scales i.e., the choice of concepts and relations compliant with principles at work in the systemic mathematical models

Une ontologie pour organiser les données de processus biologiques: la contribution des modèles mathématiques

International audienceLa biologie est un domaine connu pour sa production massive de données. Ces données sont souvent qualifiées d'hétérogènes et de fragmentées, et les biologistes ne disposent pas d'une représentation formelle, qui, à l'échelle de l'organisme, permettrait de les représenter et de les organiser. Depuis une dizaine d'années les modèles mathématiques systémiques se sont révélés être des outils utiles pour comprendre le comportement de la cellule. Nous montrons dans ce travail qu'une ontologie construite sur les principes qui régissent la conception de ces modèles peut aider à organiser les données biologiques. Nous présentons ici un choix de concepts et relations compatibles avec les principes à l'oeuvre dans les modèles systémiques

An ontology to structure biological data: the contribution of mathematical models

The biology is a research field well known for its huge quantity and diversity of data. Today, these data are still recognized as heterogeneous and fragmented. Despite the fact that several initiatives of biological knowledge representation have been realized, biologists and bioinformaticians do not have a formal representation that, at the level of the entire organism, can help them to organize such a diversity and quantity of data. Recently, in the context of the whole cell modeling approach, the systemic mathematical models have proven to be a powerful tool for understanding the bacterial cell behavior. We advocate that an ontology built on the principles that govern the design of such models, can help to organize the biological data. In this article, we describe the first step in the conception of an ontology dedicated to biological data organization at the level of the entire organism and for molecular scales i.e., the choice of concepts and relations compliant with principles at work in the systemic mathematical models

Accurate Deep Learning-aided Density-free Strategy for Many-Body Dispersion-corrected Density Functional Theory

Using a Deep Neuronal Network model (DNN) trained on the large ANI-1 data set of small organic molecules, we propose a transferable density-free many-body dispersion model (DNN-MBD). The DNN strategy bypasses the explicit Hirshfeld partitioning of the Kohn-Sham electron density required by MBD models to obtain the atom-in-molecules volumes used by the Tkatchenko-Scheffler polarizability rescaling. The resulting DNN-MBD model is trained with minimal basis iterative Stockholder atomic volumes and, coupled to Density Functional Theory (DFT), exhibits comparable (if not greater) accuracy to other approaches based on different partitioning schemes. Implemented in the Tinker-HP package, the DNN-MBD model decreases the overall computational cost compared to MBD models where the explicit density partitioning is performed. Its coupling with the recently introduced Stochastic formulation of the MBD equations (J. Chem. Theory. Comput., 2022, 18, 3, 1633-1645) enables large routine dispersion-corrected DFT calculations at preserved accuracy. Furthermore, the DNN electron density-free features extend MBD's applicability beyond electronic structure theory within methodologies such as force fields and neural networks

BiPOm: a rule-based ontology to represent and infer molecule knowledge from a biological process-centered viewpoint

International audienceBackground: Managing and organizing biological knowledge remains a major challenge, due to the complexity of living systems. Recently, systemic representations have been promising in tackling such a challenge at the whole-cell scale. In such representations, the cell is considered as a system composed of interlocked subsystems. The need is now to define a relevant formalization of the systemic description of cellular processes. Results: We introduce BiPOm (Biological interlocked Process Ontology for metabolism) an ontology to represent metabolic processes as interlocked subsystems using a limited number of classes and properties. We explicitly formalized the relations between the enzyme, its activity, the substrates and the products of the reaction, as well as the active state of all involved molecules. We further showed that the information of molecules such as molecular types or molecular properties can be deduced by automatic reasoning using logical rules. The information necessary to populate BiPOm can be extracted from existing databases or existing bio-ontologies. Conclusion: BiPOm provides a formal rule-based knowledge representation to relate all cellular components together by considering the cellular system as a whole. It relies on a paradigm shift where the anchorage of knowledge is rerouted from the molecule to the biological process

Estimating vegetation dynamics based on assimilation of SVAT, growth, and radiative transfer models [Japonais]

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Coupling STICS to the ISBA SVAT model to assess crop canopy processes from remote sensing data

National audienc

The journey of a team of engineers in learning packaging technology

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Estimating canopy processes based on a coupled crop-SVAT model with remote sensing information

* Abstracts of the papersInternational audienc