(3gt) Toward Interventions Aiding the Future Agricultural Viability of Important Crops through Systems Biology

My research is in the field of systems biology, where mathematical models, particularly genome-scale models (e.g. models encompassing all metabolic functions known to exist in the genome), are used to generate in silico hypotheses related to in vivo behavior of a particular biological system (tissue, species, or community).

Background: As the climate of our world continues to change, several agricultural species are left more vulnerable to its effects, particularly droughts (which increase in frequency and severity) and changes in temperature. As an example of a vulnerable crop, consider the primary source of coffee, Coffea arabica (Arabic coffee), which is particularly vulnerable to the ill-effects of climate change, with projections showing that the area suitable for coffee production may decrease by as much as 50% by the year 2050. Coffee is the second most traded commodity in the world, with a combined export value of $39.3 billion USD. The vast majority of coffee is grown in developing countries or regions by small-scale producers and is then sold for consumption by relatively wealthy nations. This trade has been largely beneficial for the countries concerned in that research show that coffee exports have a significant positive effect on total economic growth and provides income for the support of approximately 26 million coffee farming families. However, the primary source of coffee, Coffea arabica, is particularly vulnerable to the ill-effects of climate change, with projections showing that the area suitable for coffee production may decrease by as much as 50% by the year 2050, in addition to increased frequency and severity natural disasters, such as drought. To sustain coffee as an important agricultural commodity for the future, strategies need to be designed and implemented which maintains or increases the viability of C. arabica as an agricultural crop for the foreseeable future, and is particularly urgent in that the generational time for C. arabica is between three and four years. The newfound importance of such research is highlighted by the Sustainable Agricultural Systems (SAS) program as part of the Agriculture and Food Research Initiatives (AFRI) of the United State Department of Agriculture (USDA) which began in 2018 and will be funded at the level of $77.8 million USD for FY 2020.

Motivation: There are multiple strategies for increasing or maintaining the viability of C. arabica as an agricultural product. One promising strategy use successfully for other crop species is genetic and metabolic interventions. As a prerequisite for developing these adaptation strategies, detailed understanding of C. arabica metabolism and its regulation must be cultivated, and is necessary as this organism has not been well-studied. For example, the first complete genome was not published until 2018. Metabolic studies may be done through genome-scale modeling of C. arabica metabolism. The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last two decades, yet no efforts have thus far attempted to model this important agricultural species. Further, no efforts have yet been made toward engineering a more resilient C. arabica plant for agricultural use. Though dynamic genome-scale modeling and enzyme regulatory predictions, in silico hypotheses may be created concerning how best to proceed with in vivo efforts which may be most successful. An in silico approach such as this is particularly useful given the long generational time (compared to other agricultural species).

My Research: My doctoral research at the Systems and Synthetic Biology Laboratory (University of Nebraska-Lincoln) involved the creation and application of novel optimization-based tools for a variety of biological modeling purposes. Such purposes include increasing genome-scale model reconstruction speed and quality; increasing the accuracy and stability of biomass, concentration, and reaction flux predictions; predict enzyme kinetics and regulation from reaction flux and concentration predictions; and the in silico design of eukaryotic genetic circuits. This presentation will focus on three broad topics of my research that can be widely applied toward the goal of increasing the understanding of C. arabica metabolism and its regulation:

• Tool Development: Overview of the development and formulation of tools including OptFill (for increasing model curation speed and quality), the ORKA for dynamic Flux Balance Analysis (dFBA, for increasing solution accuracy and stability), KOPTIC (prediction of enzyme kinetics and regulation from dFBA), and EuGeneCiD (in silico design of eukaryotic genetic circuits).
• Modeling Under-studied Organisms: The OptFill tool has been applied to create a genome-scale model of the under-studied organism Exophiala determaitidis (iEde2091), with a focus on melanogenesis and carotenogensis. The model was used to study pigment metabolic cost and similarity in melanogenesis between dermatitidis and humans.
• Dynamic Modeling of Multi-Tissue Plant-Scale Systems: The ORKA and the KOPTIC tool have been applied to a multi-tissue model of Arabidopsis thaliana to study plant-scale behavior and predict enzyme regulation. This model is the first full lifecycle model of thaliana and shows strong agreement with in vivo plant-scale behavior at various points in the A. thaliana lifecycle. Further, the EuGeneCiD tool is applied to Zea mays (maize) in order to increase agricultural productivity and viability.

Teaching Interests

As a graduate student, I have sought to prepare myself as a future college-level teacher, both through teaching experiences and the College STEM Education graduate minor offered at the University of Nebraska-Lincoln.

Teaching Experiences: My teaching experiences began as an undergraduate student, including one and a half years of tutoring at Iowa State University (where I completed my bachelor degree) and two semesters as a Supplemental Instruction leader at Iowa State University. As a graduate student, I have served as a teaching assistant for two courses, the undergraduate capstone course and process controls, taking a very active role in the former course. Through my coursework and knowledge of teaching techniques, I also served as a teaching assistant and co-instructor for the graduate-level course entitled “STEM Teaching” which is part of the Graduate Student Teaching Fellowship Program at the University of Nebraska-Lincoln and the CIRTL (Center for Integration of Research, Teaching, and Learning) associate-level certification program.

As an Educator: My current views of teaching and learning have been influenced by my own experiences as a student (what has worked for me), as a teacher (what has worked for others), and my knowledge of pedagogy. Generally, my views and practices align with Social Cognitive Theory and Constructivist Theory. Social Cognitive Theory states that learning occurs through observing the behavior of others from which the observer builds mental models. Constructivist Theory essentially states that students build new knowledge from current knowledge and students view new learning through the lens of previous experiences. In my teaching, I have designed classroom activities tailored toward both of these theories, generally I have built activities aligned with Constructivist Theory for diagnostic assessments, direct instruction, and summative assessments, while I often on Social Cognitive Theory for formative assessments such as peer instruction. As a teacher, I would be interested in conducting Discipline-Based Education Research, specifically research related to how to evolve student perceptions of the roles and uses of personal electronic devices in the classroom with the aim of integrating these devices in a way that contributes positively to instruction.

Selected Publications

W. L. Schroeder and R. Saha. “OptFill: a tool for infeasible cycle-free gapfilling of stoichiometric metabolic models”. iScience, vol. 23 no. 1, pp. 1-14, Jan. 24, 2020. Available: https://www.cell.com/iscience/fulltext/

S2589-0042(19)30528-0 (doi: https://doi.org/10.1016/j.isci.2019.100783)

W. L. Schroeder, S. D. Harris, and R. Saha. “Computation-driven analysis of model polyextremotolerant fungus Exophiala dermatitidis: defensive pigment metabolic costs and human applications”. iScience, vol. 23, no. 4, Apr. 24, 2020. Available: https://www.cell.com/iscience/fulltext/S2589-0042(20)30164-4 (doi: https://doi.org/10.1016/j.isci/2020.100980)

W. L. Schroeder and R. Saha. “KOPTIC: a novel approach for in silico prediction of enzyme kinetics and regulation” BioRxiv, Oct. 17, 2019. Available: https://www.biorxiv.org/content/10.1101/807628v1 (doi: https://doi.org/10.1101/807628)

W. L. Schroeder and R. Saha. “Introducing and Optimization- and Explicit Runge-Kutta- based Approach to Perform Dynamic Flux Balance Analysis”. BioRxiv, Apr. 29, 2020. Available: https://www.biorxiv.org/content/10.1101/761189v2 (doi: https://doi.org/10.1101/761189)