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Architecture De Dataflow Pour Des Systèmes Modulaires Et Génériques De Simulation De Plante Christophe Pradal

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Architecture De Dataflow Pour Des Systèmes Modulaires Et Génériques De Simulation De Plante Christophe Pradal Architecture de dataflow pour des systèmes modulaires et génériques de simulation de plante Christophe Pradal To cite this version: Christophe Pradal. Architecture de dataflow pour des systèmes modulaires et génériques desim- ulation de plante. Modélisation et simulation. Université Montpellier, 2019. Français. NNT : 2019MONTS034. tel-02879776 HAL Id: tel-02879776 https://tel.archives-ouvertes.fr/tel-02879776 Submitted on 24 Jun 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THESE POUR OBTENIR LE GRADE DE DOCTEUR DE L’UNIVERSITE DE MONTPELLIER PAR LA VALIDATION DES ACQUIS DE L’EXPERIENCE Année universitaire 2018-2019 En Informatique École doctorale I2S – Information, Structures, Systèmes Unité de recherche UMR AGAP – Université de Montpellier SYNTHÈSE DES TRAVAUX DE RECHERCHE Architecture de Dataflow pour des systèmes modulaires et génériques de simulation de plante Présentée par Christophe PRADAL Le 17 juillet 2019 Référent : Christophe GODIN RAPPORT DE GESTION Devant le jury composé de Christophe GODIN, Directeur de recherche, Inria, Lyon Référent 2015 Marianne HUCHARD, Professeur des Universités, Université de Montpellier Représentante de l’École Doctorale Jean-Louis GIAVITTO, Directeur de recherche, CNRS, Ircam, Paris Rapporteur Gerhard BUCK-SORLIN, Professeur, AGROCAMPUS OUEST, INHP, Angers Rapporteur Sarah COHEN-BOULAKIA, Professeure des universités, Université Paris-Saclay Examinatrice Dennis SHASHA, Professor, New York University Examinateur Patrick Valduriez, Directeur de recherche, Inria, Montpellier Examinateur Bertrand Muller, Directeur de recherche, INRA, Montpellier Examinateur iii Table des matières 1 Introduction 1 1.1 Etat de l’art des réponses ....................... 5 1.1.1 La réutilisation logicielle vue par Krueger .......... 5 1.1.2 Les systèmes tableau noir .................. 13 1.1.3 Les systèmes de workflows scientifiques .......... 16 1.2 Les limites actuelles .......................... 22 1.3 Objectif et Plan de la thèse ...................... 24 2 OpenAlea : une plateforme logicielle pour la modélisation des plantes 27 3 PlantGL : Modélisation géométrique des plantes multi-échelles 39 4 Modèle de calcul d’ordre supérieur pour coupler analyse et simulation dans les workflows scientifiques 63 5 Application : Un modèle géométrique de feuilles de graminées en crois- sance 71 6 Application : Modélisation structure-fonction pour la simulation d’épi- démies foliaires 81 7 Discussion et Perspectives 101 7.1 Discussion ............................... 101 7.2 Perspectives .............................. 107 7.2.1 Modélisation structure-fonction de peuplements mixtes ... 107 7.2.2 Standardisation et annotation sémantique des modèles ... 109 7.2.3 Phénotypage haut-débit dirigé par les modèles ....... 110 Bibliographie 113 1 Chapitre 1 Introduction Les modèles de plantes structure-fonction (nommés aussi Functional-Structural Plant model ou FSPM) sont devenus un objet d’étude à part entière dans les années 90. Ils ont été développés pour comprendre la relation complexe entre l’architecture de la plante et les processus biologiques et physiques qui influencent la croissance (Go- din, Costes et Sinoquet, 2005). Ils se fondent sur une représentation explicite de la morphologie des plantes et sur une modélisation mécaniste des processus écophy- siologistes et bio-physiques du fonctionnement de la plante (Prusinkiewicz et Lin- denmayer, 1990; Sievanen, Makela et Nikinmaa, 1997; Godin, Costes et Sinoquet, 2005; Hanan et Prusinkiewicz, 2008; Fourcaud et al., 2008; Vos et al., 2010). Les échelles considérées par ces modèles sont multiples, allant de l’échelle de la cel- lule, des tissus, de l’organe, de l’organisme, jusqu’au peuplement (Godin et Cara- glio, 1998; Godin, Costes et Sinoquet, 2005; Bucksch et al., 2017; Balduzzi et al., 2017; Marshall-Colon et al., 2017a). De plus, une des propriétés remarquables des systèmes biologiques est leur organisation hiérarchique (Jacob, 1987; Lucas, Laplaze et Bennett, 2011). Les plantes sont composées d’organes, qui sont constitués de tis- sus, eux-mêmes contenant des cellules, composées d’organelles, qui sont formées de macro-molécules et ainsi de suite. L’étude de ces systèmes complexes devient de pre- mière importance en biologie pour prendre en compte la morphologie dans l’étude des interactions entre les éléments des systèmes biologiques, ainsi qu’avec l’environne- ment (Godin, Costes et Sinoquet, 2005; Lucas, Laplaze et Bennett, 2011; Vos et al., 2010; Bucksch et al., 2017). L’étude de la morphologie des plantes se retrouve à l’in- terface de nombreuses disciplines biologiques. En écologie végétale, par exemple, la morphologie des communautés définit le type de végétation et le biome, incluant leur relation à l’environnement. A contrario, la morphologie des plantes est modulée par le fonctionnement des plantes, étudié en physiologie végétale. Elle est aussi impactée par les réseaux de gènes et la synthèse des protéines, étudiés en biologie moléculaire (Kaplan, 2001). 2 Chapitre 1. Introduction Mais la morphologie est plus qu’un attribut contrôlant l’organisation structurelle des plantes. La plante est un organisme en croissance, qui se développe dans l’espace et le temps (Barthélémy et Caraglio, 2007). Le développement des plantes est programmé génétiquement et piloté par des processus biochimiques. De plus, les forces externes modulent le développement de la plante, comme l’hétérogénéité de la densité du sol qui affecte la croissance racinaire, ainsi que les flux d’air, d’eau ou la gravité qui modifient la courbure des branches et des feuilles (Moulia et Fournier, 2009). Les systèmes biologiques végétaux sont donc des systèmes complexes de grande taille, dynamiques, hiérarchiques et très structurés qui ne peuvent plus être étudiés uniquement par des approches directes, où chaque composante du système peut-être isolée et son comportement observé par une approche expérimentale. La modélisa- tion est un moyen pour comprendre leurs structures et leurs comportements (Lucas, Laplaze et Bennett, 2011). Ces dernières années, de nombreux formalismes ont été proposés pour la représenta- tion, la modélisation et la simulation de ces systèmes. Ces formalismes sont issus de différentes disciplines, comme les mathématiques, l’informatique, la physique ou la biologie. Leur hétérogénéité montre la grande variété des points de vues que peuvent avoir des chercheurs de différentes disciplines. En mathématique et en informatique, plusieurs formalismes ont été proposés pour décrire la forme et la morphologie des plantes. Plusieurs représentations mathéma- tiques existent, comme les graphes, pour représenter la structure de la plante. En par- ticulier, les formalismes les plus utilisés sont les arborescences, les arborescences quotientées, les arborescences multi-échelles, les graphes (Godin, 2000) ainsi que les graphes multi-échelles (Ong et al., 2014). D’autres représentations plus spécifiques existent cependant pour représenter la topologie ou la géométrie des plantes comme les séquences (Guédon et al., 2001), les surfaces ou les volumes (Pradal et al., 2009). Le choix de la représentation dépend grandement de l’application visée mais aussi de contraintes liées à la mesure, par exemple. Pour l’analyse et la recherche de motifs dans ces structures végétales, des méthodes de nature combinatoire (Ferraro, Godin et Prusinkiewicz, 2005), statistique (Guédon et Costes, 1998; Guédon, Barthélémy et Caraglio, 1999; Guédon et al., 2001; Guédon, Heuret et Costes, 2003; Guédon et al., 2007) ou topologique (Li et al., 2017) ont été utilisées. De façon similaire, différents formalismes ont été utilisés pour caractériser la géométrie des plantes, comme l’analyse fractale (Oppelt et al., 2000; Halley et al., 2004; Boudon et al., 2006; Da Silva et al., 2006, ou l’analyse spectrale (Kendall, 1984; Mebatsion et al., 2011; Chitwood et al., 2012). Chapitre 1. Introduction 3 De même, pour simuler la croissance des plantes, différents formalismes informa- tiques ont été utilisés, comme les grammaires formelles et les languages de réécri- ture (Lindenmayer-Systems Prusinkiewicz et Lindenmayer, 1990, Relational Growth Grammar Kurth, Kniemeyerand et Buck-Sorlin, 2005), les systèmes à évenements discrets (formalisme Discrete Events System Specification Zeigler, Praehofer et Kim, 2000), mais aussi les équations différentielles, très utilisées en épidémiologie, ou en- core les équations aux différences, formalisme prépondérant utilisé dans les modèles de culture ou pour simuler des processus de flux (eau, hormones) dans la plante (e.g. Doussan, Pagès et Vercambre, 1998; Renton et al., 2012; Pallas, Costes et Hanan, 2016). On observe cette même diversité de formalisme en biologie, pour modéliser les pro- cessus qui gouvernent le fonctionnement des plantes. Par exemple, un des processus essentiel pour la croissance et le fonctionnement est l’environnement lumineux perçu par les organes de la plante. Plusieurs modèles permettent de modéliser la lumière ab- sorbée comme le lancer de rayon (ray casting, Monte Carlo Ray Tracing,
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