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Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems
BACKGROUND: Every phenotypic trait can be viewed as a "system" in which a group of interconnected components function synergistically to yield a unified whole. Once a system's components and their interactions have been delineated according to biological principles, we can manipulate...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3127792/ https://www.ncbi.nlm.nih.gov/pubmed/21615967 http://dx.doi.org/10.1186/1752-0509-5-84 |
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author | Wu, Rongling Cao, Jiguo Huang, Zhongwen Wang, Zhong Gai, Junyi Vallejos, Eduardo |
author_facet | Wu, Rongling Cao, Jiguo Huang, Zhongwen Wang, Zhong Gai, Junyi Vallejos, Eduardo |
author_sort | Wu, Rongling |
collection | PubMed |
description | BACKGROUND: Every phenotypic trait can be viewed as a "system" in which a group of interconnected components function synergistically to yield a unified whole. Once a system's components and their interactions have been delineated according to biological principles, we can manipulate and engineer functionally relevant components to produce a desirable system phenotype. RESULTS: We describe a conceptual framework for mapping quantitative trait loci (QTLs) that control complex traits by treating trait formation as a dynamic system. This framework, called systems mapping, incorporates a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function through genes, and provides a quantitative and testable platform for assessing the interplay between gene action and development. We applied systems mapping to analyze biomass growth data in a mapping population of soybeans and identified specific loci that are responsible for the dynamics of biomass partitioning to leaves, stem, and roots. CONCLUSIONS: We show that systems mapping implemented by design principles of biological systems is quite versatile for deciphering the genetic machineries for size-shape, structural-functional, sink-source and pleiotropic relationships underlying plant physiology and development. Systems mapping should enable geneticists to shed light on the genetic complexity of any biological system in plants and other organisms and predict its physiological and pathological states. |
format | Online Article Text |
id | pubmed-3127792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31277922011-07-01 Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems Wu, Rongling Cao, Jiguo Huang, Zhongwen Wang, Zhong Gai, Junyi Vallejos, Eduardo BMC Syst Biol Methodology Article BACKGROUND: Every phenotypic trait can be viewed as a "system" in which a group of interconnected components function synergistically to yield a unified whole. Once a system's components and their interactions have been delineated according to biological principles, we can manipulate and engineer functionally relevant components to produce a desirable system phenotype. RESULTS: We describe a conceptual framework for mapping quantitative trait loci (QTLs) that control complex traits by treating trait formation as a dynamic system. This framework, called systems mapping, incorporates a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function through genes, and provides a quantitative and testable platform for assessing the interplay between gene action and development. We applied systems mapping to analyze biomass growth data in a mapping population of soybeans and identified specific loci that are responsible for the dynamics of biomass partitioning to leaves, stem, and roots. CONCLUSIONS: We show that systems mapping implemented by design principles of biological systems is quite versatile for deciphering the genetic machineries for size-shape, structural-functional, sink-source and pleiotropic relationships underlying plant physiology and development. Systems mapping should enable geneticists to shed light on the genetic complexity of any biological system in plants and other organisms and predict its physiological and pathological states. BioMed Central 2011-05-27 /pmc/articles/PMC3127792/ /pubmed/21615967 http://dx.doi.org/10.1186/1752-0509-5-84 Text en Copyright ©2011 Wu et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Wu, Rongling Cao, Jiguo Huang, Zhongwen Wang, Zhong Gai, Junyi Vallejos, Eduardo Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems |
title | Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems |
title_full | Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems |
title_fullStr | Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems |
title_full_unstemmed | Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems |
title_short | Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems |
title_sort | systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3127792/ https://www.ncbi.nlm.nih.gov/pubmed/21615967 http://dx.doi.org/10.1186/1752-0509-5-84 |
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