The Next Generation of Training for Arabidopsis Researchers: Bioinformatics and Quantitative Biology (original) (raw)

Transdisciplinary Graduate Training in Predictive Plant Phenomics

Agronomy

Novel methods to increase crop productivity are required to meet anticipated demands for food, feed, fiber, and fuel. It is becoming feasible to use modern sensors and data analysis techniques for predicting plant growth and productivity based on genomic, phenotypic, and environmental data. To design and construct crops that deliver desired traits requires trained personnel with scientific and engineering expertise as well as a variety of “soft” skills. To address these needs at Iowa State University, we developed a graduate specialization called “Predictive Plant Phenomics” (P3). Although some of our experiences may be unique, many of the specialization’s principles are likely to be broadly applicable to others interested in developing graduate training programs in plant phenomics. P3 involves transdisciplinary training and activities designed to develop communication, teambuilding, and management skills. To support students in this demanding and unique intellectual environment, we...

Undergraduates Phenotyping Arabidopsis Knockouts in a Course-Based Undergraduate Research Experience: Exploring Plant Fitness and Vigor Using Quantitative Phenotyping Methods †

Journal of Microbiology & Biology Education

We present a curriculum description, an initial student outcome investigation, and sample scientific results for a representative Course-Based Undergraduate Research Experience (CURE) that is part of the “Undergraduates Phenotyping Arabidopsis Knockouts” (unPAK) network. CUREs in the unPAK network characterize quantitative phenotypes of the model plant Arabidopsis from across environments to uncover connections between genotype and phenotype. Students in unPAK CUREs grow plants in a replicated block design and make quantitative measurements throughout the semester. This CURE enables students to answer plant science questions that draw from fields such as environmental science, genetics, ecology, and evolution. Findings indicate that this experience provides students with opportunities to make relevant scientific discoveries. Eighty percent of student datasets produced from the CURE met criteria for inclusion in the project database, indicative of student learning in data collection ...