A Systems View of Nitrogen Signaling Interactions
Autor: | Krouk, Gabriel |
---|---|
Přispěvatelé: | Biochimie et Physiologie Moléculaire des Plantes (BPMP), Université de Montpellier (UM)-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 National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-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), Université de Montpellier (UM) |
Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
root development
Fast Randomizing Algorithm for Network Knowledge [SDV]Life Sciences [q-bio] nitrogène education FRANK food and beverages hormones action mécanisme de signalisation arabidopsis develop stable GRNs gene expression [SDV.BV]Life Sciences [q-bio]/Vegetal Biology plant Nitrogen TARGET1 machine-learning algorithms |
Zdroj: | The Keystone Symposia: Plant Signaling, Molecular Pathways and Network Integration The Keystone Symposia: Plant Signaling, Molecular Pathways and Network Integration, Jan 2018, Tahoe City, California, United States International Congress "Nitrogen 2016" EMBO Conference: The Nitrogen Nutrition of Plant International Congress "Nitrogen 2016" EMBO Conference: The Nitrogen Nutrition of Plant, Aug 2016, Montpellier, France Life Sciences [q-bio]. 2015 |
Popis: | A drastic change in plant Nitrogen (N) nutrition results in systematic adaptations ranging from metabolic to growth changes. Interestingly, experimental evidences support the idea that it exists dedicated signaling pathways involved in the tuning of growth in response to nutritional status of the plant. On the other hand, growth can influence nutrition partly through hormones action. This constitutes a feed-forward loop that entangles nutrition and growth 1. This is our biological model. We aim at getting deeper insights into such signaling interactions. To this purpose, two experimental approaches will be presented. First, genome wide investigations have been made to understand the effect of combinatorial interactions between nitrogen and hormone treatments in the control of i) gene expression and ii) root development. Multi- dimensional networks have been built and functional validations of the predicted roles for the genes belonging to these networks are currently made. Second, by studying the genome wide effect of nitrate regulated transcription factors [technique named TARGET2], we yielded several insights into i) gene regulatory network complexity in Arabidopsis (unpublished), ii) transcription factor dynamics 3, iii) potential connections between nitrate and phosphate signaling in the control of root growth control4. All these aspects will be presented and discussed. Finally, since we now have some cues about the Arabidopsis gene regulatory network topology, I will introduce FRANK: a Fast Randomizing Algorithm for Network Knowledge. FRANK generates in silico, very large GRNs having the known characteristics of Arabidopsis transcriptional network (and very likely to other eukaryotic genomes) and simulates gene expression (experiments) at a genome-wide scale. In our endeavour to develop stable GRNs ("stable" means: gene expression should be constant or in oscillation) we have defined basic mathematical rules that find echo in network biology. FRANK now helps to train supervised machine-learning algorithms in order to build GRNs on real transcriptomic data. 1 Krouk, G. et al. Trends Plant Sci,(2011) 16, 178-182 2 Bargmann, B. O. et al. Mol Plant,(2013) 6, 978-980 3 Para, A. et al. Proc Natl Acad Sci U S A,(2014) 111, 10371-10376 4 Medici, A. et al. Nature communications,(2015) 6, 6274 |
Databáze: | OpenAIRE |
Externí odkaz: |