| 1. |
- Hervieux, Nathan, et al.
(författare)
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Mechanical Shielding of Rapidly Growing Cells Buffers Growth Heterogeneity and Contributes to Organ Shape Reproducibility
- 2017
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Ingår i: Current Biology. - : Elsevier BV. - 0960-9822 .- 1879-0445. ; 27:22, s. 3468-3479.e4
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Tidskriftsartikel (refereegranskat)abstract
- A landmark of developmental biology is the production of reproducible shapes, through stereotyped morphogenetic events. At the cell level, growth is often highly heterogeneous, allowing shape diversity to arise. Yet, how can reproducible shapes emerge from such growth heterogeneity? Is growth heterogeneity filtered out? Here, we focus on rapidly growing trichome cells in the Arabidopsis sepal, a reproducible floral organ. We show via computational modeling that rapidly growing cells may distort organ shape. However, the cortical microtubule alignment along growth-derived maximal tensile stress in adjacent cells would mechanically isolate rapidly growing cells and limit their impact on organ shape. In vivo, we observed such microtubule response to stress and consistently found no significant effect of trichome number on sepal shape in wild-type and lines with trichome number defects. Conversely, modulating the microtubule response to stress in katanin and spiral2 mutant made sepal shape dependent on trichome number, suggesting that, while mechanical signals are propagated around rapidly growing cells, the resistance to stress in adjacent cells mechanically isolates rapidly growing cells, thus contributing to organ shape reproducibility.
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| 2. |
- Tavakoli, Meysam, et al.
(författare)
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Single Molecule Data Analysis : An Introduction
- 2017
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Ingår i: Advances in Chemical Physics. - Hoboken, USA : John Wiley & Sons. - 1934-4791. - 9781119324577 - 9781119324560 ; , s. 205-305
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Bokkapitel (refereegranskat)abstract
- This chapter considers statistical data-driven analysis methods, and focuses on parametric as well as more recent information theoretic and nonparametric statistical approaches to biophysical data analysis with an emphasis on single-molecule applications. It then reviews simpler parametric approaches starting from an assumed model with unknown parameters. Model selection criteria are widely used in biophysical data analysis from image deconvolution to single-molecule step detection and continue to be developed by statisticians. The goal of successful model selection criteria is to pick models whose complexity is penalized, in a principled fashion, to avoid overfitting and that convincingly fit the data provided (the training set). The chapter summarizes both information theoretic as well as Bayesian model selection criteria. Finally, the chapter discusses efforts to use information theory in experimental design and ends with some considerations on the broader applicability of information theory.
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