PI: Edgar Spalding (University of Wisconsin) CoPIs: Amir H. Assadi and Nicola J. Ferrier (University of Wisconsin)
Plants serve critical functions in nature and society, which is why understanding how they work at a deep level is a major goal in modern biology. Specifically, plant biologists are endeavoring to learn what each of the many thousands of genes present in a plant's DNA contributes to the growth, development, physiology, and biochemistry of the plant. While some genes are known to contribute directly to seed yield, stress resistance, flowering time, and other agronomically important traits, most genes in any plant have no known function. One very effective way to learn the function of a gene is to determine what happens differently in a plant when the gene is mutated. This difference, or phenotype, often provides a clue about the function of the mutated gene. Large numbers of mutant plants have been produced in a variety of species, particularly rice, corn, Medicago (a relative of alfalfa) and Arabidopsis (a relative of canola). In many cases, researchers have not found phenotypes in these mutants, possibly because the phenotype is subtle, or because it affects an inconspicuous part of the plant, or because it is transiently expressed. The present project is a pilot version of an interdisciplinary effort to develop machine-vision technology for discovering phenotypes in mutant or naturally varying populations of plants. Plant biologists, engineers, and mathematicians are collaboratively developing a screening platform that employs electronic CCD cameras, robotic positioning devices, and custom computational tools to quantify and mathematically characterize (classify) the growth and development of structures such as roots, stems, and leaves. During this pilot phase, the best combination of hardware, custom algorithms, and data management will be assembled into a platform that will be tested by screening root growth and gravitropism in a selected set of Arabidopsis mutants and recombinant inbred lines. The results will be presented to the community in a standardized format via searchable databases linked to the World Wide Web through a project-specific website and TAIR. The raw data will be available to the image-analysis community to assist their development of new algorithms and classification techniques, fostering more collaborative tool development. Depending on the success of the pilot phase, a subsequent phase will establish a platform for the systematic, high throughput discovery and quantification of phenotypes in crop plants such as maize and rice and tomato and legumes.
Broader impacts of this project will come from the bringing together of engineers, biologists, computer scientists, and mathematicians. The personnel will be trained in a unique interdisciplinary environment, increasing the likelihood that such cross-cutting research becomes the norm rather than the exception. To increase the connections between computation and plant development at earlier educational stages, outreach activities will bring image-analysis to the high school classroom. Dynamic image sequences of plant structures undergoing development and tips on how to create image-analysis algorithms capable of extracting information from the images will be made available through a project-specific website so that high school computer science classes can grapple with the principles and see how computer science, engineering, and plant biology can interrelate. As this project develops, a host of classroom-ready bioimage/computation materials will be developed and personnel involved in the project including the PIs will assist in their integration into K-12 curricula.
