The Center for Root and Rhizobiome Innovation (CRRI) will establish and develop tools and technologies for more rapid, precise, and predictable crop genetic improvement that complement methods currently used by biotechnologists and plant breeders. These innovations are needed because of the urgency and enormity of challenges facing global agriculture, including the need to feed a rapidly growing population in the face of extreme climate variations and limitations in water and soil vitality.
CRRI research will be structured around a systems and synthetic biology core to generate and iteratively improve network models of plant metabolism for predictable outcomes from genetic modifications. CRRI’s systems and synthetic biology research will be applied to the study of root metabolism and its influence on root-interactions with soil microbes for improved plant health.
Research will focus on root metabolism in maize, a plant genetic model and important crop species, but findings will be broadly applicable to other plants and crop species. CRRI will develop and use fundamental knowledge to create translational products with far-reaching impact on plant and microbial biology and global agriculture.
Competitive stipend: $5,000
Suite-style room and meal plan
Travel expenses to and from Lincoln
Campus parking and/or bus pass
Full access to the Campus Recreation Center and campus library system
The Glowacka Lab (https://www.glowackalab.com/) investigates the most fundamental process for life on earth, photosynthesis. We seek to understand how we can modify photosynthesis by genome alteration or/and breeding for improving plant growth under abiotic stresses to secure food and bioenergy production in the future. Particularly, we focus on the mechanism of photoprotection. However, photoprotection protects plants from excessive light energy by its harmless dissipation as heat, it can also compete for energy with photosynthesis when the light is limited (e.g. overcast day). Since the speed of induction and relaxation of photoprotection can have a significant effect on plant growth, we are interested in defining desired characteristics of photoprotection under varied growth conditions.
Project Overview: The Glowacka Lab seeks assistance from a student in the project on investigating the photoprotection characteristics under control and stress conditions. The project will include measuring plants photoprotection based on chlorophyll fluorescence, analyzing images and investigating the biochemical changes in the leaves. It is expected that, over time, the student will master basic skills and be able to take on more responsibility and independence.
Utilizing Computational Modeling to Understand Plant Systems
Students would work with multiscale computational models of plant systems can enable better understanding of metabolic processes, relationships between genotype and phenotype, and impact of cellular and environmental factors. We have developed a comprehensive multi-scale metabolic model of maize root and its mutualistic soil microbe community. Simulation results predict the uptake of maize root secretions by soil microbes and vice versa. Transcriptomic analyses of maize root under varying nutrient conditions and root regions with highly expressed genes unveil more flux carrying reactions than lowly expressed genes. The REU student(s) will work with our team to 1) use the model to design novel hypotheses about manipulating microbial metabolism to improve the maize growth, and 2) develop computational models of additional soil microbes.
Using Mathematics to Design Genetic Circuits to Modify Root-Microbe Interactions for Increased Yield
The interactions which occur between the root of a plant and the microbes which inhabit the root and the surrounding soil are critical to the well-being of a plant as these microbes (collectively called the rhizobiome) perform a wide range of beneficial functions for their associated plant, such as the provision of nutrients, the retention of water, pathogen resistance, and generally increased plant health and growth. More recently, it has been discovered that this interaction proceeds in both directions, and that the plant provides nutrients, called exudates, to support its microbial community. These interactions are, yet, an untapped method of improving crop yield, and can be described mathematically. The goal of this project is that by using mathematical descriptions of these interactions which occur in maize (corn) and a tool for the design of genetic circuits, multiple plasmids might be designed through experiments performed in computers (in silico) which show in silico potential to increase plant growth by the manipulation of root-rhizobiome interactions. Candidate plasmids will be designed using bioparts (promotors, transcripts, terminators, etc.) identified during the course of the REU, with the design being performed using an in silico tool for genetic circuit design. These designed plasmids will be evaluated using a Genome Scale Model (GSM) of model of metabolism. Plasmids showing in silico at the end of the REU will then be presented to collaborating researchers for potential incorporation into in-organism maize root experiments.
This branch of Dr. Saha’s lab is focused on developing in silico tools and methods of predicting the outcomes of in vivo biological experiments to identify biological interventions, such as gene additions or knockouts, which have the greatest potential to result in desired outcomes. In this project, that desired outcome is to increase maize growth. To this end, we use a branch of mathematics called optimization, which essentially finds the best solution to a problem given certain criteria and a definition of what is “best”. In this REU, optimization will be used both in in silico plasmid design and evaluation. It is expected that along the way the REU student will learn about the maize (corn) genetics, the central dogma of biology, principles of optimization, and develop skills necessary for in silico experiments including some basic programming knowledge.
Optimizing in silico Plasmid Design and Evaluation
While individuals specialize, each member of the Schnable lab gets at least some experience writing computer code, employing molecular biology techniques, working with living plants in the greenhouse, and conducting fieldwork. As a result of the diverse set of collaborators we work with – applied plant breeders, biochemists, engineers, computer scientists, food scientists, and statisticians – each member of the lab also gains experience communicating both within and across scientific disciplines, as well as to diverse non-scientific audiences. This cross training produces scientists who are equipped to both understand and address the complex and far-reaching problems our world will face in coming decades.
Research projects available in the Schnable Lab in summer of 2020 include A) using machine learning to teach computers how to measure different characteristics of plants using photos taken in greenhouses and from drones flying over agricultural fields (no prior computer coding experience required) B) using genome wide association study techniques (GWAS) to identify specific genes controlling variation in plants traits which have already been measured by hand or by computer and C) using comparative genomics and RNA-seq analysis to identify promoters which turn genes off or on when plants are experiencing shortages of particular essential nutrients.
The Molecular Mechanisms of RNA Metabolism and Functions
Student would work to understand the molecular mechanisms underlying small RNA metabolism and function. RNA silencing is a process triggered by ~21-24 nucleotide RNAs to repress gene expression. The Yu lab is interested in understanding of the mechanisms governing RNA silencing and development of RNA silencing based-technologies that can be used to improve crop traits.