@article{ArvidssonPerezRodriguezMuellerRoeber2011,
  author    = {Arvidsson, Samuel Janne and Perez-Rodriguez, Paulino and M{\"u}ller-R{\"o}ber, Bernd},
  title     = {A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects},
  series = {New phytologist : international journal of plant science},
  volume    = {191},
  journal   = {New phytologist : international journal of plant science},
  number    = {3},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {0028-646X},
  doi       = {10.1111/j.1469-8137.2011.03756.x},
  pages     = {895 -- 907},
  year      = {2011},
  abstract  = {To gain a deeper understanding of the mechanisms behind biomass accumulation, it is important to study plant growth behavior. Manually phenotyping large sets of plants requires important human resources and expertise and is typically not feasible for detection of weak growth phenotypes. Here, we established an automated growth phenotyping pipeline for Arabidopsis thaliana to aid researchers in comparing growth behaviors of different genotypes. The analysis pipeline includes automated image analysis of two-dimensional digital plant images and evaluation of manually annotated information of growth stages. It employs linear mixed-effects models to quantify genotype effects on total rosette area and relative leaf growth rate (RLGR) and ANOVAs to quantify effects on developmental times. Using the system, a single researcher can phenotype up to 7000 plants d(-1). Technical variance is very low (typically < 2\%). We show quantitative results for the growth-impaired starch-excessmutant sex4-3 and the growth-enhancedmutant grf9. We show that recordings of environmental and developmental variables reduce noise levels in the phenotyping datasets significantly and that careful examination of predictor variables (such as d after sowing or germination) is crucial to avoid exaggerations of recorded phenotypes and thus biased conclusions.},
  language  = {en}
}
@article{SchmidtSchippersMieuletetal.2013,
  author    = {Schmidt, Romy and Schippers, Jos H. M. and Mieulet, Delphine and Obata, Toshihiro and Fernie, Alisdair and Guiderdoni, Emmanuel and M{\"u}ller-R{\"o}ber, Bernd},
  title     = {Multipass, a rice R2R3-type MYB transcription factor, regulates adaptive growth by integrating multiple hormonal pathways},
  series = {The plant journal},
  volume    = {76},
  journal   = {The plant journal},
  number    = {2},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0960-7412},
  doi       = {10.1111/tpj.12286},
  pages     = {258 -- 273},
  year      = {2013},
  abstract  = {Growth regulation is an important aspect of plant adaptation during environmental perturbations. Here, the role of MULTIPASS (OsMPS), an R2R3-type MYB transcription factor of rice, was explored. OsMPS is induced by salt stress and expressed in vegetative and reproductive tissues. Over-expression of OsMPS reduces growth under non-stress conditions, while knockdown plants display increased biomass. OsMPS expression is induced by abscisic acid and cytokinin, but is repressed by auxin, gibberellin and brassinolide. Growth retardation caused by OsMPS over-expression is partially restored by auxin application. Expression profiling revealed that OsMPS negatively regulates the expression of EXPANSIN (EXP) and cell-wall biosynthesis as well as phytohormone signaling genes. Furthermore, the expression of OsMPS-dependent genes is regulated by auxin, cytokinin and abscisic acid. Moreover, we show that OsMPS is a direct upstream regulator of OsEXPA4, OsEXPA8, OsEXPB2, OsEXPB3, OsEXPB6 and the endoglucanase genes OsGLU5 and OsGLU14. The multiple responses of OsMPS and its target genes to various hormones suggest an integrative function of OsMPS in the cross-talk between phytohormones and the environment to regulate adaptive growth.},
  language  = {en}
}