From parasite biology to predictive metabolic models
Year: 2026
Type: review
Abstract: 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.
Source: Trends in Parasitology
Institutions: Fundação Oswaldo Cruz, Universidade de São Paulo, Hospital Universitário da Universidade de São Paulo, Lyon 1 Université, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université de Lyon, Laboratoire de Biométrie et Biologie Evolutive, Max Planck Institute for Chemical Ecology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $7,030
OpenAlex ID: W7164680860