Literature DB >> 12523994

Virtual plants: modelling as a tool for the genomics of tolerance to water deficit.

François Tardieu1.   

Abstract

Modelling can simulate the responses of virtual plants carrying diverse combinations of alleles under different scenarios of abiotic stress. The main difficulty is mathematically expressing the genetic variability of responses to environmental conditions. Modelling via gene regulatory networks is not feasible for such complex systems, but plants can be modelled using response curves to environmental conditions that are 'meta mechanisms' at plant level. Each genotype is represented by a set of response parameters that are valid under a wide range of conditions. Transgenesis of one function experimentally affected one response parameter only. Transgenic plants or plants carrying any combination of quantitative trait loci might therefore be simulated and tested under different climatic scenarios, before genetic manipulations are performed.

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Year:  2003        PMID: 12523994     DOI: 10.1016/s1360-1385(02)00008-0

Source DB:  PubMed          Journal:  Trends Plant Sci        ISSN: 1360-1385            Impact factor:   18.313


  44 in total

1.  Maximum likelihood inference and bootstrap methods for plant organ growth via multi-phase kinetic models and their application to maize.

Authors:  Jonathan Hillier; David Makowski; Bruno Andrieu
Journal:  Ann Bot       Date:  2005-05-23       Impact factor: 4.357

2.  QTL methodology for response curves on the basis of non-linear mixed models, with an illustration to senescence in potato.

Authors:  M Malosetti; R G F Visser; C Celis-Gamboa; F A van Eeuwijk
Journal:  Theor Appl Genet       Date:  2006-05-20       Impact factor: 5.699

3.  Parameter stability of the functional-structural plant model GREENLAB as affected by variation within populations, among seasons and among growth stages.

Authors:  Yuntao Ma; Baoguo Li; Zhigang Zhan; Yan Guo; Delphine Luquet; Philippe de Reffye; Michael Dingkuhn
Journal:  Ann Bot       Date:  2006-12-07       Impact factor: 4.357

4.  Quantitative genetics and functional-structural plant growth models: simulation of quantitative trait loci detection for model parameters and application to potential yield optimization.

Authors:  Véronique Letort; Paul Mahe; Paul-Henry Cournède; Philippe de Reffye; Brigitte Courtois
Journal:  Ann Bot       Date:  2007-08-31       Impact factor: 4.357

5.  Using a model-based framework for analysing genetic diversity during germination and heterotrophic growth of Medicago truncatula.

Authors:  S Brunel; B Teulat-Merah; M-H Wagner; T Huguet; J M Prosperi; C Dürr
Journal:  Ann Bot       Date:  2009-02-27       Impact factor: 4.357

6.  Quantitative trait loci and crop performance under abiotic stress: where do we stand?

Authors:  Nicholas C Collins; François Tardieu; Roberto Tuberosa
Journal:  Plant Physiol       Date:  2008-06       Impact factor: 8.340

Review 7.  Post-GWAS: where next? More samples, more SNPs or more biology?

Authors:  P Marjoram; A Zubair; S V Nuzhdin
Journal:  Heredity (Edinb)       Date:  2013-06-12       Impact factor: 3.821

Review 8.  Role of chromatin in water stress responses in plants.

Authors:  Soon-Ki Han; Doris Wagner
Journal:  J Exp Bot       Date:  2013-12-03       Impact factor: 6.992

9.  Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a "gene-to-phenotype" modeling approach.

Authors:  Karine Chenu; Scott C Chapman; François Tardieu; Greg McLean; Claude Welcker; Graeme L Hammer
Journal:  Genetics       Date:  2009-09-28       Impact factor: 4.562

10.  A functional-structural model of rice linking quantitative genetic information with morphological development and physiological processes.

Authors:  Lifeng Xu; Michael Henke; Jun Zhu; Winfried Kurth; Gerhard Buck-Sorlin
Journal:  Ann Bot       Date:  2011-01-18       Impact factor: 4.357

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