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Related Experiment Video

Updated: Jul 19, 2026

Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems

Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems

Published on: August 17, 2022

Software tools that facilitate kinetic modelling with large data sets: an example using growth modelling in

L Uys¹, J H S Hofmeyr, J L Snoep

¹Triple-J Group for Molecular Cell Physiology, Department of Biochemistry, Stellenbosch University, Private Bag X1, ZA-7602 Matieland, South Africa.

Systems Biology

|September 22, 2006

Summary

Managing large datasets from sugarcane sucrose metabolism models is simplified using a spreadsheet. This approach integrates kinetic modeling with the Python scripting language and PySCeS within the Gnumeric spreadsheet program.

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Area of Science:

Biochemistry
Computational Biology
Agricultural Science

Background:

Large-scale modeling projects generate substantial datasets, posing challenges for analysis.
Predicting sucrose accumulation and metabolism in sugarcane requires complex kinetic models.
Sugarcane internode maturity influences sucrose metabolism, necessitating detailed data analysis.

Purpose of the Study:

To present a novel solution for managing and analyzing large datasets from sugarcane kinetic modeling.
To integrate computational modeling with spreadsheet-based data handling for improved efficiency.

Main Methods:

Utilized a kinetic model of sucrose accumulation in sugarcane.
Simulated plant growth by reassigning maximal activity values to model object parameters for each internode.
Performed data storage, manipulation, and analysis within the Gnumeric spreadsheet program.
Leveraged the Python scripting language and PySCeS modeling software via an embedded Python interpreter.

Main Results:

A single model definition file is sufficient for the simulation software.
The approach effectively manages increasing input and output data volumes as more internodes are modeled.
Facilitated streamlined analysis of complex kinetic modeling outputs.

Conclusions:

The integration of PySCeS, Python, and Gnumeric provides an efficient solution for managing large datasets in sugarcane modeling.
This method simplifies the analysis of sucrose metabolism and internode maturity effects.
Enhances the accessibility and usability of complex biological modeling data.