A Mathematical Framework for Analyzing Wild Tomato Root Architecture

Arjun Chandrasekhar, Magdalena M. Julkowska

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)306-316
Number of pages11
JournalJournal of Computational Biology
Issue number4
StatePublished - Apr 7 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-05-25
Acknowledgements: Dr. Julkowska’s study was financed by the King Abdullah University for Science and Technology and the Boyce Thompson Institute.
We thank the organizing committee for the 2021 Biological Distributed Algorithms (BDA) conference, as well as the reviewers for or BDA submission. We thank Guillaume Lobet for help with understanding the output from the plant imaging software. We thank Graham Zug, who is currently working with Dr. Chandrasekhar on mathematical optimization algorithms for constructing Pareto optimal plant arbors. Finally, we thank bioRxiv for hosting a preprint of our study (DOI: 10.1101/2021.08.12.456185).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Genetics
  • Modeling and Simulation
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Molecular Biology


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