调试
通量平衡分析
生物信息学
Python(编程语言)
计算机科学
自动化
系统生物学
代谢网络
SBML公司
计算生物学
数据挖掘
生物
程序设计语言
XML
标记语言
万维网
工程类
遗传学
机械工程
基因
作者
Fernando Cruz,João Capela,Eugénio C. Ferreira,Miguel Rocha,Óscar Dias
标识
DOI:10.1109/tcbb.2023.3339972
摘要
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. BioISO was implemented as Python™ package, and it is also available at https://bioiso.bio.di.uminho.pt as a web-service and in merlin as a plugin.
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