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From parasite biology to predictive metabolic models
Lucas Gentil Azevedo1, Gabriela Torres Montanaro2, Bruno Ribeiro Pinto2
1Center for Data and Knowledge Integration for Health (CIDACS), Institute Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz), Salvador, Brazil.
Trends in Parasitology
|June 13, 2026
Summary
Genome-scale metabolic models (GEMs) offer insights into protist parasite metabolism and disease vulnerabilities. This review highlights key modeling choices and challenges in standardization for reproducible parasite research.
Area of Science:
- Parasitology
- Systems Biology
- Computational Biology
Background:
- Protist parasites cause significant global diseases, but their complex metabolic pathways are not fully understood.
- Genome-scale metabolic models (GEMs) are increasingly used to study parasite metabolism, predict gene essentiality, and identify therapeutic targets.
- Understanding host-parasite metabolic interactions is crucial for developing effective treatments.
Purpose of the Study:
- To review the current state of GEMs for protist pathogens.
- To analyze the impact of key modeling decisions on model behavior and predictive power.
- To identify challenges and propose solutions for improving standardization and reusability of protist GEMs.
Main Methods:
- Systematic review of existing literature on GEMs for protist pathogens.
- Analysis of critical modeling choices: objective functions, constraints, and compartmentalization.
- Evaluation of how modeling assumptions influence biological interpretability and model propagation.
Main Results:
- GEMs provide a framework for simulating protist metabolism and predicting essential genes and metabolic vulnerabilities.
- Key modeling decisions significantly influence model outcomes and the scope of predictions.
- Inconsistent annotation, validation, and lack of adherence to FAIR principles hinder standardization and reusability.
Conclusions:
- GEMs are valuable tools for understanding protist parasite metabolism and identifying potential drug targets.
- Standardization of modeling practices, annotation, and data sharing is essential for advancing the field.
- Adoption of FAIR data principles will enhance the interoperability and reproducibility of protist GEMs for future research.
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