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The Spring of Systems Biology-Driven Breeding
Jérémy Lavarenne1, Soazig Guyomarc'h2, Christophe Sallaud3
1UMR DIADE, Université de Montpellier, IRD, 911 Avenue Agropolis, 34394 Montpellier cedex 5, France; Biogemma, Centre de Recherches de Chappes, Route d'Ennezat, 63720 Chappes, France.
Trends in Plant Science
|May 17, 2018
Summary
Genetics and molecular biology advance plant breeding. Gene regulatory network (GRN) modeling aids in identifying key genes for targeted trait improvement and efficient breeding strategies.
Area of Science:
- Genetics and Molecular Biology
- Systems Biology
- Bioinformatics
Background:
- Genetics and molecular biology have enabled rationalized plant breeding.
- High-throughput analyses and in silico processing allow for studying entire gene regulatory networks (GRNs).
Purpose of the Study:
- To apply GRN models for identifying candidate genes in plant breeding.
- To utilize dynamic modeling for understanding network behavior and genetic modification.
Main Methods:
- Analysis of high-throughput experimental data.
- In silico processing and computational modeling of gene regulatory networks.
- Dynamic modeling using time-series datasets.
Main Results:
- GRN models can identify topological features to shortlist candidate genes.
- Dynamic modeling enhances comprehension of network behavior.
- Identification of key genetic elements for phenotype modification.
Conclusions:
- Systems biology-based approaches, utilizing GRN modeling, offer more efficient plant breeding strategies.
- Understanding gene regulatory networks is crucial for designing targeted genetic improvements in plants.
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