UC San Diego

The human red blood cell (RBC) has historically been considered a relatively simple cell type whose main function was in transport of respiratory gasses. The RBC has served as the ideal cell type for the development of mathematical models due to the significant amount of available experimental data, its relative metabolic simplicity, and its physiological robustness over the course of its lifespan. With the advent of high throughput omic technologies, the RBC can now be viewed in a new light, showing that its functionality may be more complex and essential than previously believed. The RBC has thus played an important role in the history of systems biology and the quest to construct whole-cell simulators. The work of this dissertation provides a modern foundation for network-level characterization of RBC metabolism through computational modeling of the proteome. First, we develop the Mass Action Stoichiometric Simulation Python package (MASSpy), a versatile computational framework designed to facilitate construction of dynamic models from genome-scale metabolic reconstructions. Using models of the human RBC and E. coli glycolysis, we demonstrate how MASSpy is applied to explore mechanisms of enzyme regulation and characterize functional states of the proteome. Next, we develop RBC-GEM, the most extensive and curated metabolic reconstruction of the human RBC. Through workflow-guided manual curation and meta-analysis of an abundance of proteomic data published over the past two decades, we developed the most comprehensive view of the RBC proteome to-date. Lastly, we demonstrate how metabolic modeling is used to characterize proteins within the low-abundance RBC proteome. Using both dynamic models and proteome-constrained models of RBC energy metabolism, we provide one of the first in silico characterizations of the phosphofructokinase platelet isozyme in mature human erythrocytes. Collectively, these results comprehensively define the human erythrocyte proteome and provide the necessary tools for its subsequent metabolic characterization.