Posted on Oct. 12, 2016
Seeds are an important source of proteins for both humans and livestock. However, major staple crop seeds are deficient in several essential amino acids (EAA) which mammals must obtain from their diet. Lack of balanced EAA in grains can lead to a syndrome of serious symptoms including lower disease resistance, anemia, and retarded mental and physical development. The World Health Organization estimated that 30% of the population in developing countries suffers from this syndrome. Despite past intensive research efforts, there have been only a few successes in improving EAA levels and composition in staple crop seeds, which did not result in significant yield or growth penalties. The latter strongly suggests that there is a lack of fundamental understanding of the amino acids (AA) level and composition regulation and its relevance to fitness.
To enhance our knowledge on this issue we intend to unravel the relationships between the morphological attributes of the vegetative phase of the plant, which is the main source for amino acid synthesis, with the final composition of amino acids in dry seeds, using the vast genetic resources for the Arabidopsis plant model system. Uncovering these relationships will enhance our understanding of the adaptation, diversity, constraints, and genetic regulation of these two sink- source tissues in the context of seed amino acids composition. This knowledge can help advance breeding programs and seed amino acids biofortification.
The overall goal of this project is to create a high throughput phenotypic platform that will facilitate the genomic approach needed to unravel the genetic relationships between whole-plant morphological traits (e.g., rosette shape, color and thermal status) and specific biochemical traits of seeds (i.e., protein and amino acid content and composition). The project scope will include building a mobile data acquisition station, and developing a data interpretation software with data export capabilities. The phenotypic data acquisition station will harbor cameras, as well as thermal and moisture sensors. Data acquisition and interpretation will be performed using a combination of LabView and Matlab. The physiological parameters measured will be soil moisture, rosette, thermal readouts, size, shape, and color. The mobile data acquisition station will be developed on a standard laboratory cart that can be easily transported between growth chambers and fit within chamber walk-ways. Students will be involved in the design, prototyping, automation, and image analysis process. We wish to create an interdisciplinary team, so we prefer students from different backgrounds, not just Bioengineering.
Background in coding, robotics, OR instrumentation.
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