From parasite biology to predictive metabolic models
Protist parasites cause devastating diseases worldwide, yet their complex metabolism remains poorly understood. Genome-scale metabolic models (GEMs) have emerged as powerful tools to systematically represent and simulate parasite metabolism, enabling the prediction of gene essentiality, metabolic vulnerabilities, and host–parasite interactions. This review examines the current landscape of GEMs for protist pathogens, focusing on the key modeling decisions (objective functions, constraints, and compartmentalization) that govern model behavior and predictive scope. We discuss how these choices shape biological interpretability and how inherited assumptions from early reconstructions propagate across successive models. Ongoing challenges in standardization and reusability highlight the need for consistent annotation, validation, and adherence to FAIR data principles to build interoperable and reproducible resources.
