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10-1089-cmb-2021-0361 |
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|a 10665277 (ISSN)
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|a A Mathematical Framework for Analyzing Wild Tomato Root Architecture
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|b Mary Ann Liebert Inc.
|c 2022
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|a 11
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|z View Fulltext in Publisher
|u https://doi.org/10.1089/cmb.2021.0361
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|a The root architecture of wild tomato, Solanum pimpinellifolium, can be viewed as a network connecting the main root to various lateral roots. Several constraints have been proposed on the structure of such biological networks, including minimizing the total amount of wire necessary for constructing the root architecture (wiring cost), and minimizing the distances (and by extension, resource transport time) between the base of the main root and the lateral roots (conduction delay). For a given set of lateral root tip locations, these two objectives compete with each other - optimizing one results in poorer performance on the other - raising the question how well S. pimpinellifolium root architectures balance this network design trade-off in a distributed manner. In this study, we describe how well S. pimpinellifolium roots resolve this trade-off using the theory of Pareto optimality. We describe a mathematical model for characterizing the network structure and design trade-offs governing the structure of S. pimpinellifolium root architecture. We demonstrate that S. pimpinellifolium arbors construct architectures that are more optimal than would be expected by chance. Finally, we use this framework to quantify structural differences between arbors grown in the presence of salt stress, classify arbors into four distinct architectural ideotypes, and test for heritability of variation in root architecture structure. © Copyright 2022, Mary Ann Liebert, Inc., publishers 2022.
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|a article
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|a heritability
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|a mathematical model
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|a networks
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|a nonhuman
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|a Pareto optimal
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|a pareto optimality
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|a quantitative analysis
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|a root architecture
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|a root morphology
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|a salt stress
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|a theoretical study
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|a tomato
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|a tomato plants
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|a Chandrasekhar, A.
|e author
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|a Julkowska, M.M.
|e author
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|t Journal of Computational Biology
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