Publicação
Development and integration of topology-based methods for gap-filling of metabolic networks
| Resumo: | The usage of Genome-scale Metabolic models (GEMs) spans various applications across diverse fields. These models depict a metabolic network that represents the complete metabolism of a specific organism. The automated reconstruction of these models can be facilitated using tools like merlin. However, the draft models generated often contain gaps, primarily due to knowledge limitations in the databases that enable the reconstruction of these models (e.g., KEGG, ModelSEED), and due to wrong and faulty genome annotations. Certain tools, such as BioISO, aim at identifying metabolites that cannot be produced by a metabolic network. Other tools like Meneco try to discern a set of reactions that can be integrated into the model, aiming to rectify the existing gaps in the metabolic network. The execution time for these tools increases proportionally with the complexity of the metabolic network under scrutiny, potentially leading to prolonged execution times to address the possible gaps present in the model. Thus, there arises a need to develop a workflow that is both efficient and offers reliable solutions. In this study, a workflow integrating BioISO and Meneco (BioMeneco) was developed, coupled with the development of pertinent methods, with the aim of automating the process as much as possible, reducing the search space, and optimising the gap-filling process. The outcomes of the developed workflow were promising. It not only offered reduced execution times but also provided the capability for better refinement for various models when compared to a typical Meneco workflow. Regardless, while the developed workflow demonstrated efficiency, it also highlighted the challenges of relying on a single database and the complexities of metabolic networks, paving for further improvements and research in this domain. |
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| Autores principais: | Moura, José Diogo Cruz de |
| Assunto: | Gap-filling Genome-scale metabolic model Metabolic model reconstruction Optimisation Biologia de sistemas Desenvolvimento de workflow Genome-scale metabolic model Implementação em Python Otimização Reconstrução de modelos metabólicos Redes metabólicas |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | The usage of Genome-scale Metabolic models (GEMs) spans various applications across diverse fields. These models depict a metabolic network that represents the complete metabolism of a specific organism. The automated reconstruction of these models can be facilitated using tools like merlin. However, the draft models generated often contain gaps, primarily due to knowledge limitations in the databases that enable the reconstruction of these models (e.g., KEGG, ModelSEED), and due to wrong and faulty genome annotations. Certain tools, such as BioISO, aim at identifying metabolites that cannot be produced by a metabolic network. Other tools like Meneco try to discern a set of reactions that can be integrated into the model, aiming to rectify the existing gaps in the metabolic network. The execution time for these tools increases proportionally with the complexity of the metabolic network under scrutiny, potentially leading to prolonged execution times to address the possible gaps present in the model. Thus, there arises a need to develop a workflow that is both efficient and offers reliable solutions. In this study, a workflow integrating BioISO and Meneco (BioMeneco) was developed, coupled with the development of pertinent methods, with the aim of automating the process as much as possible, reducing the search space, and optimising the gap-filling process. The outcomes of the developed workflow were promising. It not only offered reduced execution times but also provided the capability for better refinement for various models when compared to a typical Meneco workflow. Regardless, while the developed workflow demonstrated efficiency, it also highlighted the challenges of relying on a single database and the complexities of metabolic networks, paving for further improvements and research in this domain. |
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