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How plants grow like human brains
Being immobile, plants develop creative strategies to adjust their architecture to environmental challenges, like a neighbor's shade. The diversity in plant form, from towering redwoods to creeping thyme, is a visible sign of these strategies. But Saket Navlakha PhD, assistant professor in the Jonas Salk Institute's Center for Integrative Biology, and senior author of the paper, wondered if there was a universal organizing principle at work. Using high-precision 3D scanning technology, his team measured the architecture of young plants over time to quantify their growth in ways that could be analyzed mathematically.
"Our project was motivated by the question of whether, despite all the diversity we see in plant forms, there is some form or structure they all share. We discovered there is. Surprisingly, the variation in how branches are distributed in space can be described mathematically by a Gaussian Function, also known as a bell curve."
The work is published in the journal Current Biology, July 7, 2017.
"This collaboration arose from a conversation that Saket and I had shortly after his arrival at Salk," says Professor and Director of the Plant Molecular and Cellular Biology Laboratory Joanne Chory, who, along with being the Howard H. and Maryam R. Newman Chair in Plant Biology, is also a Howard Hughes Medical Investigator and one of the paper's coauthors. "We were able to fund our studies thanks to Salk's innovation grant program and the Howard Hughes Medical Institute."
The team began with three agriculturally valuable crops: sorghum, tomato and tobacco. Researchers grew the plants from seeds under various conditions plants might experience growing in nature (shade, ambient light, high light, high heat and drought). Every few days for a month, first author Adam Conn PhD, scanned each plant to digitally capture its growth. In all, Conn scanned almost 600 plants.
"We basically scanned the plants like you would scan a piece of paper," says Conn, a Salk research assistant. "But in this case the technology is 3D and allows us to capture a complete form — the full architecture of how the plant grows and distributes branches in space."
Each plant's digital representation is called a point cloud, a set of 3D coordinates in space that can be analyzed computationally. With this new data, the team built a statistical description of theoretically possible plant shapes by studying the function of a plant's branch density. Branch density function depicts the likelihood of finding a branch at any point in the space surrounding a plant.
This model revealed three properties of growth: (1) separability, (2) self-similarity and (3) Gaussian branch density function.
The level of evolutionary efficiency suggested by these properties is surprising. Even though it would be inefficient for plants to evolve different growth rules for every type of environmental condition, the researchers did not expect to find plants to be so efficient as to develop only a single functional form. The properties identified by their work may help researchers evaluate new strategies for genetically engineering crops.
Previous work by one of the authors, Charles Stevens, a professor in Salk's Molecular Neurobiology Laboratory, found the same three mathematical properties at work in brain neurons.
The next challenge for the team is to identify what might be some of the mechanisms at the molecular level driving these changes. Navlakha adds: "We could see whether these principles deviate in other agricultural species and maybe use that knowledge in selecting plants to improve crop yields."
• We analyzed 557 3D plant architectures to study how branches distribute in space
• Branch density was separable, self-similar, and described by a truncated Gaussian
• These three properties are shared by dendritic and axonal morphologies in the brain
Plant architectures can be characterized statistically by their spatial density function, which specifies the probability of finding a branch at each location in the territory occupied by a plant. Using high-precision 3D scanning, we analyzed 557 plant shoot architectures, representing three species, grown across three to five environmental conditions, and through 20–30 developmental time points. We found two elegant properties in the spatial density functions of these architectures: all functions could be nearly modified in one direction without affecting the density in orthogonal directions (called “separability”), and all functions shared the same underlying shape, aside from stretching and compression (called “self-similarity”). Surprisingly, despite their striking visual diversity, we discovered that all architectures could be described as variations on a single underlying function: a Gaussian density function truncated at roughly two SDs. We also observed systematic variation in the spatial density functions across species, growth conditions, and time, which suggests functional specialization despite following the same general design form.
Keywords: plant architectures, 3D scanning, branch density, separability, self-similarity, growth principles
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Plants use the same rules to grow under widely different conditions (for example, cloudy
versus sunny), and the density of their branches in space follows a Gaussian ('bell curve')
distribution. All of which is equally true of neurons as they branch in the brain.
Image Credit: Salk Institute