Search

Your search keyword '"CONTROLE DE CROISSANCE"' showing total 4 results
Start Over Descriptor "CONTROLE DE CROISSANCE" Topic 0106 biological sciences
4 results on '"CONTROLE DE CROISSANCE"'

1. Modelling the influence of dimerisation sequence dissimilarities on the auxin signalling network

Author

Teva Vernoux, Jonathan Legrand, Stéphane Robin, Jean-Benoist Leger, Yann Guédon, Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, iSAM transnational ERASysBio+ Grant, École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-École normale supérieure - Lyon (ENS Lyon), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
0106 biological sciences, 0301 basic medicine, développement végétal, Arabidopsis thaliana, Transcription, Genetic, Arabidopsis, Normal Distribution, F62 - Physiologie végétale - Croissance et développement, Bioinformatics, 01 natural sciences, F30 - Génétique et amélioration des plantes, Interactions biologiques, Protein sequencing, Séquence d'ADN, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Structural Biology, Transcriptional regulation, Expression des gènes, Cluster Analysis, Auxin signalling network, [SDV.BDD]Life Sciences [q-bio]/Development Biology, chemistry.chemical_classification, biology, U10 - Informatique, mathématiques et statistiques, Applied Mathematics, régulation transcriptionnelle, Physiologie végétale, Computer Science Applications, Signalling, Linear regression model, Modeling and Simulation, Binary and valued-graph clustering, Transcription, Modèle mathématique, Développement biologique, Signal Transduction, Research Article, Systems biology, Contrôle de croissance, Computational biology, Models, Biological, Arabidospsis thaliana, 03 medical and health sciences, Auxin, modèle de régression linéaire, Modelling and Simulation, Plant development, Protein Structure, Quaternary, Molecular Biology, modélisation, Clustering coefficient, Méthode statistique, Indoleacetic Acids, Arabidopsis Proteins, Gene Expression Profiling, arabidospsis thaliana, auxin signalling network, transcriptional regulation, linear regression model, mixture model for random graphs, plant development, binary and valued-graph clustering, Mixture model for random graphs, biology.organism_classification, Auxine, Gene expression profiling, 030104 developmental biology, chemistry, Protein Multimerization, Protéine végétale, 010606 plant biology & botany
Abstract

Background Auxin is a major phytohormone involved in many developmental processes by controlling gene expression through a network of transcriptional regulators. In Arabidopsis thaliana, the auxin signalling network is made of 52 potentially interacting transcriptional regulators, activating or repressing gene expression. All the possible interactions were tested in two-way yeast-2-hybrid experiments. Our objective was to characterise this auxin signalling network and to quantify the influence of the dimerisation sequence dissimilarities on the interaction between transcriptional regulators. Results We applied model-based graph clustering methods relying on connectivity profiles between transcriptional regulators. Incorporating dimerisation sequence dissimilarities as explanatory variables, we modelled their influence on the auxin network topology using mixture of linear models for random graphs. Our results provide evidence that the network can be simplified into four groups, three of them being closely related to biological groups. We found that these groups behave differently, depending on their dimerisation sequence dissimilarities, and that the two dimerisation sub-domains might play different roles. Conclusions We propose here the first pipeline of statistical methods combining yeast-2-hybrid data and protein sequence dissimilarities for analysing protein-protein interactions. We unveil using this pipeline of analysis the transcriptional regulator interaction modes. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0254-7) contains supplementary material, which is available to authorized users.

Published
2016
Full Text
View/download PDF

2. Evaluation of the OILCROP-SUN model for sunflower hybrids under different agro-meteorological conditions of Punjab-Pakistan

Author

Hatem Belhouchette, Shah Fahad, Ashfaq Ahmad, Gerrit Hoogenboom, Wajid Nasim, Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM), COMSATS Institute of Information Technology [Islamabad] (CIIT), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), University of Agriculture Faisalabad - UAF (PAKISTAN), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Huazhong Agricultural University, AgWeatherNet Program, Washington State University (WSU), Chercheur Nord Chercheur Sud UMR System, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
0106 biological sciences, CONDUITE DE LA CULTURE, Soil Science, MODELE DE SIMULATION, engineering.material, 01 natural sciences, SIMULATION MODEL, PAKISTAN, [SHS]Humanities and Social Sciences, Crop, SUSTAINABILITY, EVALUATION, Yield (wine), PERFORMANCE DE CULTURE, NITROGEN CONTENT, Dry matter, Leaf area index, PLANTE OLEAGINEUSE, HUILE DE TOURNESOL, Hybrid, 2. Zero hunger, SUNFLOWER OIL, [SDV.SA.AEP]Life Sciences [q-bio]/Agricultural sciences/Agriculture, economy and politics, 04 agricultural and veterinary sciences, DURABILITE, 15. Life on land, HELIANTHUS ANNUUS, Sunflower, Arid, CROP PERFORMANCE, Agronomy, GROWTH CONTROL, TENEUR EN AZOTE, 040103 agronomy & agriculture, engineering, AGROCLIMATIC ZONES, 0401 agriculture, forestry, and fisheries, Environmental science, CONTROLE DE CROISSANCE, Fertilizer, CROP MANAGEMENT, Agronomy and Crop Science, OIL CROPS, ZONE AGROCLIMATIQUE, 010606 plant biology & botany
Abstract

International audience; Crop models and decision support system can be useful tools for researchers, teachers, scientists, extension educators, policy makers and planners to help or support the application and evaluation of sustainable and long term alternative management practices. Sunflower is an important oilseed crop that has the potential to bridge the gap between the consumption and domestic production of edible oil in developing countries such as Pakistan. Limited research has been conducted on the application of the OILCROP-SUN model for the simulation of growth and development of sunflower under different environmental conditions, especially in developing countries. Field experiments were conducted in three environments to study the impact of nitrogen (N) fertilizer on grain yield and quality. The objective of the present study was to evaluate the performance of the OILCROP-SUN model and to determine the effect of N on sunflower for three contrasting environments. The results showed that OILCROP-SUN model simulated well for growth, development and yield of sunflower hybrids in these three contrasting environments and under different management practices that included several genotypes and different nitrogen fertilizer application rates. The d values (index of agreement) for leaf area index (LAI) ranged from 88 to 97 under arid, 78 to 85 under semi- arid and 71 to 81 under sub- humid conditions. Similarly, d values for total dry matter (TDM) ranged between 90 and 94 under arid, 81 and 88 under semi-arid and 90 and 96 under sub-humid conditions, respectively while the percentage difference for grain yield ranged from 8.5 to 21% for different ecological conditions. The model also simulated LAI, TDM, grain yield and oil quality well under different environmental conditions of Pakistan and further studies should focus on application of the OILCROP-SUN model for variable agro-climatic regions where sunflower is an important crop.

Published
2016
Full Text
View/download PDF

3. Using plant growth modeling to analyse C source-sink relations under drought: inter and intra specific comparison

Author

Delphine Luquet, Maria Camila Rebolledo, Jean-Christophe Soulie, Benoît Pallas, Anne Clément-Vidal, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Luquet, Delphine, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Graine, Plant growth, C source and sink regulation, huile de palme, contrainte environnementale, Stockage, Ingénierie des aliments, F62 - Physiologie végétale - Croissance et développement, Plant Science, drought, Elaeis guineensis, modèle, 01 natural sciences, modélisation des plantes, oil palm, Sink (geography), Water balance, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Biomasse, Original Research Article, 2. Zero hunger, 0303 health sciences, Source–sink dynamics, geography.geographical_feature_category, Vegetal Biology, U10 - Informatique, mathématiques et statistiques, croissance des plantes, non structural carbohydrate, food and beverages, Agricultural sciences, phénotype, Alimentation et Nutrition, non-structural carbohydrate, plant biomass accumulation, Modèle mathématique, source and sink regulation, functional structural plan tmodel, rice, Contrôle de croissance, Oryza sativa, functional structural plant model, Biology, lcsh:Plant culture, Intraspecific competition, 03 medical and health sciences, Sécheresse, Botany, Palm oil, Food and Nutrition, Food engineering, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, lcsh:SB1-1110, Glucide, Croissance, 030304 developmental biology, geography, fungi, 15. Life on land, biology.organism_classification, Agronomy, Gène, stress hydrique, carbone, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Sciences agricoles, Biologie végétale, 010606 plant biology & botany
Abstract

Publis013-agap-074; The ability to assimilate C and allocate non-structural carbohydrates (NSCs) to the most appropriate organs is crucial to maximize plant ecological or agronomic performance. Such C source and sink activities are differentially affected by environmental constraints. Under drought, plant growth is generally more sink than source limited as organ expansion or appearance rate is earlier and stronger affected than C assimilation. This favors plant survival and recovery but not always agronomic performance as NSC are stored rather than used for growth due to a modified metabolism in source and sink leaves. Such interactions between plant C and water balance are complex and plant modeling can help analyzing their impact on plant phenotype. This paper addresses the impact of trade-offs between C sink and source activities and plant production under drought, combining experimental and modeling approaches. Two contrasted monocotyledonous species (rice, oil palm) were studied. Experimentally, the sink limitation of plant growth under moderate drought was confirmed as well as the modifications in NSC metabolism in source and sink organs. Under severe stress, when C source became limiting, plant NSC concentration decreased. Two plant models dedicated to oil palm and rice morphogenesis were used to perform a sensitivity analysis and further explore how to optimize C sink and source drought sensitivity to maximize plant growth. Modeling results highlighted that optimal drought sensitivity depends both on drought type and species and that modeling is a great opportunity to analyze such complex processes. Further modeling needs and more generally the challenge of using models to support complex trait breeding are discussed.

Published
2013
Full Text
View/download PDF

4. Genetic control of fruit shape acts prior to anthesis in melon (Cucumis melo L.)

Author

N. Giovinazzo, Catherine Dogimont, D. Besombes, Michel Pitrat, Christophe Périn, L. S. Hagen, Génétique et Amélioration des Fruits et Légumes (GAFL), and Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, Melon, [SDV]Life Sciences [q-bio], Population, Ovary (botany), F62 - Physiologie végétale - Croissance et développement, Contrôle de croissance, Évaluation génétique des pères, Biology, Genes, Plant, 01 natural sciences, Cucumis, F30 - Génétique et amélioration des plantes, 03 medical and health sciences, Quantitative Trait, Heritable, Anthesis, Cucumis melo, Floraison, Botany, Genetics, Plant reproductive morphology, Stade de développement végétal, education, Molecular Biology, Gene, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 2. Zero hunger, 0303 health sciences, education.field_of_study, Chromosome Mapping, Genetic Variation, food and beverages, General Medicine, biology.organism_classification, Qtl analysis, Phenotype, Fruit, Carte génétique, 010606 plant biology & botany
Abstract

Genetic control of fruit shape in Cucumis melo was studied using QTL analysis in two Recombinant Inbred (RI) populations consisting of 163 and 63 individuals, respectively, obtained by crossing the same round-fruited parent with two different elongated-fruit lines. Fruit shape is mainly explained by fruit length in these two populations. Most QTLs for fruit shape and ovary shape detected were found to co-segregate, thus demonstrating early control of fruit shape during ovary development. A high level of correlation between fruit shape and ovary shape was also found in 14 unrelated genetic lines, a finding which suggests that control of fruit shape by gene(s) active early in the ovary is a general feature in C. melo. Two major flower genes, a ( monoecious) and p ( pentamerous), were shown to have major effects on fruit shape. Major tightly linked QTLs for fruit and ovary shape were found close to the a and p genes, probably reflecting their pleiotropic effect on fruit shape. Moreover, one of the two QTLs detected in the Védrantais x PI 414723 population was also found in the Védrantais x PI 161375 population. Variation of fruit shape in melon could be due to variations having quantitative effects on a large set of genes that are probably involved in ovary development.

Published
2002

Catalog

Books, media, physical & digital resources
See catalog results