Network trade-offs and homeostasis in Arabidopsis shoot architectures

Adam Conn, Arjun Chandrasekhar, Martin van Rongen, Ottoline Leyser, Joanne Chory and Saket Navlakha

PLOS Computational Biology, 2019, vol. 15, issue 9, 1-19

Abstract: Understanding the optimization objectives that shape shoot architectures remains a critical problem in plant biology. Here, we performed 3D scanning of 152 Arabidopsis shoot architectures, including wildtype and 10 mutant strains, and we uncovered a design principle that describes how architectures make trade-offs between competing objectives. First, we used graph-theoretic analysis to show that Arabidopsis shoot architectures strike a Pareto optimal that can be captured as maximizing performance in transporting nutrients and minimizing costs in building the architecture. Second, we identify small sets of genes that can be mutated to shift the weight prioritizing one objective over the other. Third, we show that this prioritization weight feature is significantly less variable across replicates of the same genotype compared to other common plant traits (e.g., number of rosette leaves, total volume occupied). This suggests that this feature is a robust descriptor of a genotype, and that local variability in structure may be compensated for globally in a homeostatic manner. Overall, our work provides a framework to understand optimization trade-offs made by shoot architectures and provides evidence that these trade-offs can be modified genetically, which may aid plant breeding and selection efforts.Author summary: In both engineered and biological systems, there is often no single structure that performs optimally on all tasks. For example, a transport system that can very quickly shuttle people to and from work will often not be very cheap to build, and vice-versa. Thus, trade-offs are born, and it is natural to ask how well evolution has resolved trade-offs between competing tasks. Here, we use 3D laser scanning and network analysis to show that Arabidopsis plant architectures make Pareto optimal trade-offs, which means that improving upon one task requires a sacrifice in the other task. In other words, an architecture that performs better on both tasks cannot be built. We also identify a small set of genes that can change how the architecture prioritizes one task versus the other, which may allow for better crop design in the future. Finally, we show that two replicate architectures that look visually diverse (e.g., variation in size, number of leaves, number of branches, etc.) often prioritize each task similarly. This suggests that despite local variability in the architecture, there may be a homeostatic drive to maintain globally balanced trade-offs.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007325

DOI: 10.1371/journal.pcbi.1007325

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