Infectious diseases continue to be major health concerns worldwide. Although major advances have led to accumulation of genomic data about human pathogens, there clearly exists a gap between genome information and studies aiming at identifying potential drug targets. Here, constraint-based modeling (CBM) was deployed to integrate disparate data types with genome-scale metabolic models (GEMs) to advance our understanding of the pathogenesis of infectious agents with respect to identifying and prioritizing drug targets. Specifically, genome-scale metabolic modeling of multiple stages and species of Plasmodium, the causative agent of malaria, was used to prioritize potential drug targets that could be used to simultaneously treat (anti-malarials) and block transmission of the parasite. In addition, species-specific metabolic models were used to guide translation of findings from non-human experimental disease models to human-infecting species. Further, comparative analysis of the essentiality of metabolic genes for V. cholerae, the causative agent of cholera, growth and survival in single and co-infections with other enteric pathogens led to prioritizing conditionally independent essential genes that would be potential drug targets in both single and co-infection scenarios. Taken together, our findings highlight the utility of using genome-scale metabolic models to prioritize druggable targets that would be of broader spectrum against human pathogens.
| Date of Award | Aug 2019 |
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| Original language | English (US) |
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| Awarding Institution | - Biological, Environmental Sciences and Engineering
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| Supervisor | Takashi Gojobori (Supervisor) |
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- Metabolism
- Modelling
- Pathogens
- Computational Modelling