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Comparative analysis of nonlinear growth curve models for Arabidopsis thaliana rosette leaves

Jiao, Xiang (författare)
College of Mechanical and Electronic Engineering, Nanjing Forestry University
Zhang, Huichun (författare)
College of Mechanical and Electronic Engineering, Nanjing Forestry University
Zheng, Jiaqiang (författare)
College of Mechanical and Electronic Engineering, Nanjing Forestry University
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Yin, Yue (författare)
College of Mechanical and Electronic Engineering, Nanjing Forestry University
Wang, Guosu (författare)
College of Mechanical and Electronic Engineering, Nanjing Forestry University
Chen, Ying (författare)
College of Forestry, Nanjing Forestry University
Ge, Yufeng (författare)
Department of Biological Systems Engineering, University of Nebraska-Lincoln
Yu, Jun, 1962- (författare)
Umeå universitet,Institutionen för matematik och matematisk statistik,Mathematical Statistics
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 (creator_code:org_t)
2018-05-21
2018
Engelska.
Ingår i: Acta Physiologiae Plantarum. - : Springer. - 0137-5881 .- 1861-1664. ; 40:6
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • As a model organism, modeling and analysis of the phenotype of Arabidopsis thaliana (A. thaliana) leaves for a given genotype can help us better understand leaf growth regulation. A. thaliana leaves growth trajectories are to be nonlinear and the leaves contribute most to the above-ground biomass. Therefore, analysis of their change regulation and development of nonlinear growth models can better understand the phenotypic characteristics of leaves (e.g., leaf size) at different growth stages. In this study, every individual leaf size of A. thaliana rosette leaves was measured during their whole life cycle using non-destructive imaging measurement. And three growth models (Gompertz model, logistic model and Von Bertalanffy model) were analyzed to quantify the rosette leaves growth process of A. thaliana. Both graphical (plots of standardized residuals) and numerical measures (AIC, R2 and RMSE) were used to evaluate the fitted models. The results showed that the logistic model fitted better in describing the growth of A. thaliana leaves compared to Gompertz model and Von Bertalanffy model, as it gave higher R2 and lower AIC and RMSE for the leaves of A. thaliana at different growth stages (i.e., early leaf, mid-term leaf and late leaf).

Ämnesord

LANTBRUKSVETENSKAPER  -- Bioteknologi med applikationer på växter och djur -- Växtbioteknologi (hsv//swe)
AGRICULTURAL SCIENCES  -- Agricultural Biotechnology -- Plant Biotechnology (hsv//eng)
NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

A. thaliana
Growth model
Leaf area
Akaike’s information criterion
Non-destructive imaging measurement
biomechanics
biomekanik

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