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Visual FTBL Generator: Visual Generation Tool for 13C-FLUX File BABT
Yu,Guojun; Rui,Bin; Fan,Xinyue; Xie,Xiaoyao.
Abstract 13C metabolic flux analysis (13C-MFA) has achieved increasing significance in quantitative metabolic system analysis in recent years. In 13C metabolic flux analysis, 13C-FLUX software is a major analytical tool. The software’s input script is primarily expressed in textual form without visual presentation of the structure of the entire metabolic network, thus error-prone in manual input. To solve this problem, we have developed a visual FTBL generator (VFG, available at http://47.100.98.220/vfg/index.jsp in a Google or Firefox browser)for MFA that eliminates the tedious, error-prone text entry mode and provides a user-friendly graphical interface and simple visual reaction generation functions.
Tipo: Info:eu-repo/semantics/article Palavras-chave: Metabolic network; Visualization; 13C-FLUX; Database.
Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132019000100610
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Comparisons Between Different Methods in Measuring Enzyme Similarity for Metabolic Network Alignment BABT
Zhou,Wenwei; Liu,Jiangtao; Xing,Hiu; Zhang,Zhengdong; Xie,Xiaoyao; Jing,Fengxuan.
Metabolic network alignments enable comparison of the similarities and differences between pathways in two metabolic networks and help to uncover the conserved sub-blocks therein. Such analysis is important in the understanding of metabolic networks and species evolution. The fundamental parts of metabolic network alignment algorithms all involve comparisons of the similarity between two enzymes as a similarity measure of network nodes. As a result, the study of methods for measuring enzyme similarity becomes highly relevant. Currently, two approaches are mainly used to measure enzyme similarity. One of the methods is based on similarity measures of gene or protein sequences; the other is based on enzyme classification. In this study, multiple metabolic...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Metabolic network alignment; Enzyme; Similarity measure; Classification numbering; Sequence similarity.
Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000100405
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