Literature DB >> 29764727

The Spring of Systems Biology-Driven Breeding.

Jérémy Lavarenne1, Soazig Guyomarc'h2, Christophe Sallaud3, Pascal Gantet4, Mikaël Lucas2.   

Abstract

Genetics and molecular biology have contributed to the development of rationalized plant breeding programs. Recent developments in both high-throughput experimental analyses of biological systems and in silico data processing offer the possibility to address the whole gene regulatory network (GRN) controlling a given trait. GRN models can be applied to identify topological features helping to shortlist potential candidate genes for breeding purposes. Time-series data sets can be used to support dynamic modelling of the network. This will enable a deeper comprehension of network behaviour and the identification of the few elements to be genetically rewired to push the system towards a modified phenotype of interest. This paves the way to design more efficient, systems biology-based breeding strategies.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Keywords:  dynamic network analysis; gene discovery; gene regulatory networks; network engineering; plant breeding

Mesh:

Year:  2018        PMID: 29764727     DOI: 10.1016/j.tplants.2018.04.005

Source DB:  PubMed          Journal:  Trends Plant Sci        ISSN: 1360-1385            Impact factor:   18.313


  10 in total

1.  Identification of Transcription Factors Regulating Senescence in Wheat through Gene Regulatory Network Modelling.

Authors:  Philippa Borrill; Sophie A Harrington; James Simmonds; Cristobal Uauy
Journal:  Plant Physiol       Date:  2019-05-07       Impact factor: 8.340

Review 2.  Future Challenges in Plant Systems Biology.

Authors:  Mikaël Lucas
Journal:  Methods Mol Biol       Date:  2022

3.  RNA Sequencing Analyses for Deciphering Potato Molecular Responses.

Authors:  Živa Ramšak; Marko Petek; Špela Baebler
Journal:  Methods Mol Biol       Date:  2021

Review 4.  5Gs for crop genetic improvement.

Authors:  Rajeev K Varshney; Pallavi Sinha; Vikas K Singh; Arvind Kumar; Qifa Zhang; Jeffrey L Bennetzen
Journal:  Curr Opin Plant Biol       Date:  2020-01-28       Impact factor: 7.834

5.  A Computational Model for Inferring QTL Control Networks Underlying Developmental Covariation.

Authors:  Libo Jiang; Hexin Shi; Mengmeng Sang; Chenfei Zheng; Yige Cao; Xuli Zhu; Xiaokang Zhuo; Tangren Cheng; Qixiang Zhang; Rongling Wu; Lidan Sun
Journal:  Front Plant Sci       Date:  2019-12-18       Impact factor: 5.753

Review 6.  Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits.

Authors:  Pallavi Sinha; Vikas K Singh; Abhishek Bohra; Arvind Kumar; Jochen C Reif; Rajeev K Varshney
Journal:  Theor Appl Genet       Date:  2021-05-20       Impact factor: 5.699

Review 7.  Genetic Improvement in Sunflower Breeding-Integrated Omics Approach.

Authors:  Milan Jocković; Siniša Jocić; Sandra Cvejić; Ana Marjanović-Jeromela; Jelena Jocković; Aleksandra Radanović; Dragana Miladinović
Journal:  Plants (Basel)       Date:  2021-06-04

8.  Haplotype analysis of key genes governing grain yield and quality traits across 3K RG panel reveals scope for the development of tailor-made rice with enhanced genetic gains.

Authors:  Ragavendran Abbai; Vikas Kumar Singh; Vishnu Varthini Nachimuthu; Pallavi Sinha; Ramchander Selvaraj; Abhilash Kumar Vipparla; Arun Kumar Singh; Uma Maheshwar Singh; Rajeev K Varshney; Arvind Kumar
Journal:  Plant Biotechnol J       Date:  2019-02-15       Impact factor: 9.803

Review 9.  Genomic interventions for sustainable agriculture.

Authors:  Abhishek Bohra; Uday Chand Jha; Ian D Godwin; Rajeev Kumar Varshney
Journal:  Plant Biotechnol J       Date:  2020-09-22       Impact factor: 9.803

10.  TB1: from domestication gene to tool for many trades.

Authors:  Ernesto Igartua; Bruno Contreras-Moreira; Ana M Casas
Journal:  J Exp Bot       Date:  2020-08-06       Impact factor: 6.992

  10 in total

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