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Related Experiment Video

Updated: May 11, 2025

Identification of the Genes Involved in Stomatal Development via Epidermal Phenotype Scoring

Identification of the Genes Involved in Stomatal Development via Epidermal Phenotype Scoring

Published on: January 20, 2023

Cell Wall-Based Machine Learning Models to Predict Plant Growth Using Onion Epidermis.

Celia Khoulali^1,2, Juan Manuel Pastor^3,4, Javier Galeano^3,4

¹Department of Biotechnology-Plant Biology, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

International Journal of Molecular Sciences

|April 17, 2025

Summary

This summary is machine-generated.

Understanding plant cell wall (CW) growth dynamics is key for crop yields. This study used onion epidermis and machine learning to link CW composition, enzyme activity, and growth stages for enhanced crop productivity.

Keywords:

Allium cepa L.cell wall composition cell wall enzymes machine learning modeling onion epidermis plant growth

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Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Area of Science:

Plant Biology
Biochemistry
Agricultural Science

Background:

The plant cell wall (CW) provides structural support and influences growth.
Understanding CW dynamics is vital for optimizing crop yields.

Purpose of the Study:

Investigate plant cell wall growth dynamics using onion epidermis.
Correlate CW composition, enzyme activity, and gene expression with growth stages.
Develop a predictive framework for enhancing crop productivity.

Main Methods:

Microscopic analysis of onion epidermis cell size variations.
Fourier transform infrared spectroscopy (FTIR) for CW component analysis.
Biochemical assays for cellulose, sugars, and antioxidants.
RT-qPCR for cell wall enzyme (CWE) gene expression analysis.
Machine learning models (SVM, kNN, Neural Networks) for data integration and prediction.

Main Results:

Identified 11 spectral intervals linked to CW components and structural modifications.
Observed variations in cellulose, soluble sugars, and antioxidant content across developmental layers.
Found significant correlations between CWE gene expression and CW composition.
Pectin methylesterase and fucosidase gene expression linked to cellulose, sugar, and antioxidant levels.
Machine learning models accurately predicted growth stages based on integrated data.

Conclusions:

Plant cell wall composition, enzymatic activity, and growth are intricately linked.
A predictive framework integrating molecular and biochemical data can guide crop improvement.
Findings offer insights for enhancing crop productivity and sustainability through CW manipulation.