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Related Experiment Video

Updated: Apr 20, 2026

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves
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LEAF-E: a tool to analyze grass leaf growth using function fitting.

Wannes Voorend1, Peter Lootens2, Hilde Nelissen3

  • 1Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium ; Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium ; Plant Sciences Unit - Growth and Development, Institute for Agricultural and Fisheries Research (ILVO), Caritasstraat 21, 9090 Melle, Belgium.

Plant Methods
|December 2, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces LEAF-E, a user-friendly Excel macro for analyzing grass leaf growth. It accurately quantifies leaf elongation rate and duration, crucial for understanding plant development and environmental responses.

Keywords:
Cell lengthGrowth zoneLeaf elongation rateLeaf lengthNon-linear regression

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Area of Science:

  • Plant biology and phenotyping
  • Computational biology and bioinformatics

Background:

  • Leaf growth monitoring in grasses is vital for understanding biomass accumulation and response to environmental factors.
  • Leaf elongation rate (LER) and leaf elongation duration (LED) are key parameters for assessing plant growth dynamics.
  • Experimental variations in LER profiles complicate accurate quantification of growth timing and duration.

Purpose of the Study:

  • To develop a user-friendly tool for robust and automated extraction of leaf growth parameters from large datasets.
  • To implement non-linear regression modeling for fitting leaf length measurements using a beta sigmoid function.
  • To quantify the timing and duration of leaf growth for improved detection of inter-genotype differences.

Main Methods:

  • Development of LEAF-E, a Microsoft Excel-based macro for fitting leaf growth data.
  • Application of non-linear regression modeling using the beta sigmoid function.
  • Testing LEAF-E with diverse datasets from maize, Miscanthus spp., and Brachypodium distachyon.

Main Results:

  • LEAF-E enables robust and automated extraction of LER and LED from large leaf growth datasets.
  • The tool accurately quantifies the timing and duration of leaf growth, enhancing growth analysis.
  • LEAF-E demonstrated broad applicability across different grass species and experimental contexts.

Conclusions:

  • LEAF-E provides a user-friendly solution for analyzing sigmoidal growth patterns, particularly in grasses.
  • The macro facilitates accurate quantification of leaf growth timing and duration, aiding in growth studies.
  • LEAF-E is a valuable tool for routine analysis of plant growth processes exhibiting sigmoidal dynamics.