Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/90591
Título: | BioISO: an objective-oriented application for assisting the curation of genome-scale metabolic models |
Autor(es): | Cruz, Fernando João Pereira Capela, João Ferreira, Eugénio C. Rocha, Miguel Dias, Oscar |
Palavras-chave: | Biological system modeling Biochemistry Biology Bioinformatics Debugging Genomics Computational modeling BioISO Gap-finding algorithm Genome-scale metabolic models Open-source software Python |
Data: | Mar-2024 |
Editora: | IEEE |
Revista: | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Citação: | Cruz, Fernando; Capela, João; Ferreira, Eugénio C.; Rocha, Miguel; Dias, Oscar, BioISO: An Objective-Oriented Application for Assisting the Curation of Genome-Scale Metabolic Models. IEEE-ACM Transactions on Computational Biology and Bioinformatics, 21(2), 215-226, 2024 |
Resumo(s): | As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation ( BioISO ) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools ( Meneco and fastGapFill ). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/90591 |
DOI: | 10.1109/TCBB.2023.3339972 |
ISSN: | 1545-5963 |
e-ISSN: | 1557-9964 |
Versão da editora: | http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8857 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
document_57679_1.pdf | 1,83 MB | Adobe PDF | Ver/Abrir |