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Comparative analysis of nonlinear growth curve models for Arabidopsis thaliana rosette leaves
College of Mechanical and Electronic Engineering, Nanjing Forestry University.
College of Mechanical and Electronic Engineering, Nanjing Forestry University.
College of Mechanical and Electronic Engineering, Nanjing Forestry University.
College of Mechanical and Electronic Engineering, Nanjing Forestry University.
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2018 (Engelska)Ingår i: Acta Physiologiae Plantarum, ISSN 0137-5881, E-ISSN 1861-1664, Vol. 40, nr 6, artikel-id 114Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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).

Ort, förlag, år, upplaga, sidor
Springer, 2018. Vol. 40, nr 6, artikel-id 114
Nyckelord [en]
A. thaliana, Growth model, Leaf area, Akaike’s information criterion, Non-destructive imaging measurement
Nationell ämneskategori
Växtbioteknologi Sannolikhetsteori och statistik
Forskningsämne
biomekanik
Identifikatorer
URN: urn:nbn:se:umu:diva-147921DOI: 10.1007/s11738-018-2686-8ISI: 000451233600001PubMedID: 26811110Scopus ID: 2-s2.0-85047359291OAI: oai:DiVA.org:umu-147921DiVA, id: diva2:1209198
Tillgänglig från: 2018-05-22 Skapad: 2018-05-22 Senast uppdaterad: 2018-12-12Bibliografiskt granskad

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Yu, Jun

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Institutionen för matematik och matematisk statistik
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