Developing Pareto front models for the improved description of plant's dynamic root system architecture

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Wild tomato root stocks are used to enhance cultivated tomato productivity and environmental resilience. Visually distinct root shapes of wild tomato can be distinguished by evaluating the root architecture as a network in which lateral root tips, which absorb water and nutrients, need to be connected to the root base, which supports shoot growth. This network design considers two competing objectives: minimizing the building blocks of a network (cost) and minimizing the transport time from root tips to root base (speed). To improve the relevance of the model to the physiology of plants, the model will be expanded to include the effects of gravitational forces and account for anatomical differences between the main and lateral roots. To identify the genes underlying root network design, we will use genetic approaches and generate mutant plants that will be evaluated for network efficiency, as well as plant productivity and resilience. Understanding mathematical and genetic mechanisms underlying plant architecture will lead to designing better crops with improved productivity and stress resilience, thereby contributing to increased sustainability of food production. Additionally, an improved understanding of biological networks and how they grow can be applied to transportation networks, allowing transit systems to naturally scale with population growth.This project will develop methods for explaining and optimizing the structure of natural transportation networks, such as the root system architecture of wild tomato plants. Initial work will involve developing a numerical optimization algorithm for constructing minimal Euclidean Steiner trees that are subjected to non-linear constraints. We will apply the Euclidean Steiner tree algorithms towards quantifying how tomato root architectures optimize trade-offs between conserving material costs and ensuring efficient nutrient and water transport, especially when growth trajectories are constrained by gravitational forces and differential cost/transport qualities imposed by the differences in root anatomy. We will also study measurements of root architecture growth to reverse-engineer an algorithm for constructing optimal Steiner trees using purely distributed computation. Our goal is to use forward genetics to identify genetic components underlying the development of optimal architectures under non-stress and salt stress conditions. The identified algorithms, ideotypes and genetic mechanisms will serve as targets for plant breeding and genetically engineering stress-resilient crops. We anticipate that the methods we develop can be generalized towards explaining, designing, and optimizing transportation networks found in other natural and engineered systems. In the future, our work can provide insight into designing public transport networks that scale efficiently.This project is jointly funded by the Division of Mathematical Sciences, Mathematical Biology Program and the Division of Integrative Organismal Systems, Plant Genome Research Program (PGRP) in the Directorate for Biological Sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.