ChiMera: an easy to use pipeline for bacterial genome based metabolic network reconstruction, evaluation and visualization

• Autor(a) principal: Tamasco, Gustavo
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
• Texto Completo: https://www.teses.usp.br/teses/disponiveis/17/17136/tde-08112022-173558/
• topic: Análise de fluxo de balanço de metabólitos; Engenharia metabólica; Flux balance analysis; Genome-scale metabolic reconstruction; GSMR; GSMR; Metabolic engineering; Metabolic network visualization; Reconstrução de metabolismo em escala genômica; Visualização de metabolismo

Several genome-scale metabolic reconstruction tools have been developed in the last decades. They have helped to reconstruct various metabolic models, which have contributed to a variety of fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a high level of bioinformatic skills, and most of them are not scalable for multiple genomes. Moreover, the functionalities required to build models are generally scattered through multiple tools, requiring knowledge of their utilization.Here, we present ChiMera, which combines the most efficient tools in model reconstruction, prediction, and visualization. ChiMera uses CarveMe top-down approach, based on genomic evidence, to prune a global model with a high level of curation, generating a draft genome able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules for metabolic network visualization. The first module generates maps for the most important pathways, e.g., core-metabolism, fatty acid oxidation and biosynthesis, nucleotides and amino acids biosynthesis, and glycolysis. The second module produces a genome-wide metabolic map, which can be used to retrieve KEGG pathway information for each compound in the model. A module to investigate gene essentiality and knockout is also present.Overall, ChiMera combines model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization to create a simulation-ready genome-scale model, helping genetic engineering projects, prediction of phenotypes, and other model-driven discoveries without requiring high-level of bioinformatic skills.