Welcome to my page!
I am a PhD candidate in the Chemical and Biomolecular Engineering department at the University of Nebraska-Lincoln (UNL). Prior to joining UNL, I earned by BS in Chemical Engineering and Mathematics from Iowa State University.
I have been a part of the Systems and Synthetic Biology Laboratory since January 2017. Broadly, I develop and analyze linear and non-linear models of metabolism (the set of reactions which occurs in an organism, not the rate at which a human utilizes nutritional Calories) for the purposes of developing a systems-level understanding of an organism. These models are mathematical reconstructions of the metabolism of a target organism which can be put to a variety of applications including bioproduction, metabolic engineering, evolutionary studies, identifying as yet unknown metabolic functions, and many other applications. These applications have made metabolic models increasingly important tools in the processes of understanding, discovering, and redesigning biological systems. As already suggested, these models come in two broad types: linear models, which utilize the stoichiometry of metabolic reactions to predict metabolic behavior, and non-linear, which utilize the information from linear models in addition to accounting for reaction kinetics. While the latter type of model has the potential for greater accuracy, it has several limitations including the need for greater in vivo (in the organism) knowledge, which in many cases does not yet exist. My current projects make use of these concepts or attempt to address some limitations in model building.
My current projects involve…
i) development of an optimization-based tool (including related protocols) to increase the development speed of linear models of metabolism, particularly for under-studied organisms;
ii) development of a linear model of metabolism with utilizes all known genetic information (called a Genome-Scale Model, GSM) of the under-studied yet potential model organism Exophiala dermatitidis, a highly melanized fungus, to study defensive pigment production and cost;
iii) development of a new mathematical framework for studying metabolism across a series of time points which is both accurate and computationally tractable (applied to Arabidopsis thaliana);
iv) development of a new optimization-based tool which aids in the design of genetic circuits for synthetic biology interventions to accomplish some desired outcome;
v) development of a multi-tissue model of Zea mays (corn) which will integrate transcriptomic data to increase the accuracy and predictiveness of particularly the root model;
vi) development of an optimization-based tool to predict reaction kinetics from linear models of metabolism with the long-term goal of replacing lacking in vivo knowledge with in silico predictions to speed the development of non-linear models of metabolism.
Outside of work, I like to spend my time watching television (I love game shows), listening to audiobooks (my favorite author is Terry Pratchett), studying Catholic theology, playing video games, drinking coffee, cooking, wine tasting, and spending time with my wife and dog.
Connect with me via LinkedIn or Google Scholar.
Here is a list of my 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). Impact factor: 4.4.
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). Impact factor: 4.4.
W. L. Schroeder and R. Saha. “Introducing an optimization- and explicit Runge-Kutta- based approach to perform dynamic flux balance analysis”. Scientific Reports, vol. 10, no. 9241, Jun. 8, 2020. Available: https://www.nature.com/articles/s41598-020-65457-4 (doi: https://doi.org/10.1038/s41598-020-65457-4). Impact factor: 4.2.