• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

龙猫：识别代谢扰动瞬态期间的活性反应。

Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations.

作者信息

Galvão Ferrarini Mariana, Ziska Irene, Andrade Ricardo, Julien-Laferrière Alice, Duchemin Louis, César Roberto Marcondes, Mary Arnaud, Vinga Susana, Sagot Marie-France

机构信息

Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.

Univ Lyon, INRAE, INSA-Lyon, BF2I, UMR 203, Villeurbanne, France.

出版信息

Front Genet. 2022 Feb 21;13:815476. doi: 10.3389/fgene.2022.815476. eCollection 2022.

DOI:10.3389/fgene.2022.815476
PMID:35281848
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8905348/
Abstract

The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data. In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks. Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.

摘要

代谢组学数据的可得性不断提高及其分析方法的发展，正增进我们对细胞机制以及生物系统如何响应不同扰动的理解。当前，需要新颖的计算方法来促进对日益增多的可用数据的分析与整合。在本文中，我们展示了Totoro，这是一种基于约束的新方法，它将两种不同代谢状态的定量非靶向代谢组学数据整合到全基因组代谢模型中，并预测在瞬态期间最可能活跃的反应。我们将Totoro应用于来自三个不同生长实验（葡萄糖、丙酮酸、琥珀酸脉冲实验）的真实数据，并且能够预测已知的活跃途径，并获得与每种底物相关的不同代谢的新见解。我们使用核心模型和iJO1366模型来证明我们的方法适用于较小和较大的网络。Totoro是一种开源方法（可在https://gitlab.inria.fr/erable/totoro获取），适用于任何具有可用代谢模型的生物体。它用C++实现，依赖于可免费用于学术目的的IBM CPLEX。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8fd/8905348/228ce4fea1a2/fgene-13-815476-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8fd/8905348/ec6c7c6ff146/fgene-13-815476-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8fd/8905348/edad2ca1c8d6/fgene-13-815476-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8fd/8905348/82845a4b1173/fgene-13-815476-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8fd/8905348/228ce4fea1a2/fgene-13-815476-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8fd/8905348/ec6c7c6ff146/fgene-13-815476-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8fd/8905348/edad2ca1c8d6/fgene-13-815476-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8fd/8905348/82845a4b1173/fgene-13-815476-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8fd/8905348/228ce4fea1a2/fgene-13-815476-g004.jpg

相似文献

1
Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations.龙猫：识别代谢扰动瞬态期间的活性反应。
Front Genet. 2022 Feb 21;13:815476. doi: 10.3389/fgene.2022.815476. eCollection 2022.
2
Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification头部损伤的转化代谢组学：基于体外核磁共振波谱的代谢物定量分析探索脑代谢功能障碍
3
Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model.整合定量蛋白质组学和代谢组学与基因组规模代谢网络模型。
Bioinformatics. 2010 Jun 15;26(12):i255-60. doi: 10.1093/bioinformatics/btq183.
4
iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models.iReMet-flux：将相对代谢物水平整合到化学计量代谢模型中的基于约束的方法。
Bioinformatics. 2016 Sep 1;32(17):i755-i762. doi: 10.1093/bioinformatics/btw465.
5
TRFBA: an algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data.TRFBA：一种算法，可将基因组规模的代谢和转录调控网络与表达数据的整合相结合。
Bioinformatics. 2017 Apr 1;33(7):1057-1063. doi: 10.1093/bioinformatics/btw772.
6
MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks.MetExplore：一个将代谢组学实验和基因组尺度代谢网络联系起来的网络服务器。
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W132-7. doi: 10.1093/nar/gkq312. Epub 2010 May 5.
7
Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions.用于发现代谢功能的iJO1366大肠杆菌代谢网络重建的缺口填充分析。
BMC Syst Biol. 2012 May 1;6:30. doi: 10.1186/1752-0509-6-30.
8
Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments.基于转录组的简约通量分析提高了在复杂环境下代谢网络的预测能力。
PLoS Comput Biol. 2020 Apr 16;16(4):e1007099. doi: 10.1371/journal.pcbi.1007099. eCollection 2020 Apr.
9
A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data.一个满足多组突变体通量数据的大肠杆菌核心代谢动力学模型。
Metab Eng. 2014 Sep;25:50-62. doi: 10.1016/j.ymben.2014.05.014. Epub 2014 Jun 10.
10
Flux imbalance analysis and the sensitivity of cellular growth to changes in metabolite pools.通量失衡分析和细胞生长对代谢物池变化的敏感性。
PLoS Comput Biol. 2013;9(8):e1003195. doi: 10.1371/journal.pcbi.1003195. Epub 2013 Aug 29.

本文引用的文献

1
Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation.基于网络的代谢组学数据分析和解释策略：从分子网络到生物学解释。
Expert Rev Proteomics. 2020 Apr;17(4):243-255. doi: 10.1080/14789450.2020.1766975. Epub 2020 Jun 4.
2
From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data.从样本到代谢洞察：揭示液相色谱-高分辨质谱代谢组学数据中的生物学相关信息
Metabolites. 2019 Dec 17;9(12):308. doi: 10.3390/metabo9120308.
3
The metaRbolomics Toolbox in Bioconductor and beyond.
生物导体及其他领域中的代谢组学工具箱。
Metabolites. 2019 Sep 23;9(10):200. doi: 10.3390/metabo9100200.
4
MOOMIN - Mathematical explOration of 'Omics data on a MetabolIc Network.MOOMIN - 代谢网络中 'Omics 数据的数学探索。
Bioinformatics. 2020 Jan 15;36(2):514-523. doi: 10.1093/bioinformatics/btz584.
5
Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models.通过将相对表达量和相对代谢物丰度整合到热力学一致的代谢模型中，提高通量预测的准确性。
PLoS Comput Biol. 2019 May 13;15(5):e1007036. doi: 10.1371/journal.pcbi.1007036. eCollection 2019 May.
6
MetaboRank: network-based recommendation system to interpret and enrich metabolomics results.MetaboRank：一种基于网络的推荐系统，用于解释和丰富代谢组学结果。
Bioinformatics. 2019 Jan 15;35(2):274-283. doi: 10.1093/bioinformatics/bty577.
7
MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.MetaboAnalyst 4.0：迈向更透明、更综合的代谢组学分析。
Nucleic Acids Res. 2018 Jul 2;46(W1):W486-W494. doi: 10.1093/nar/gky310.
8
MetExplore: collaborative edition and exploration of metabolic networks.MetExplore：代谢网络的协作编辑和探索。
Nucleic Acids Res. 2018 Jul 2;46(W1):W495-W502. doi: 10.1093/nar/gky301.
9
From correlation to causation: analysis of metabolomics data using systems biology approaches.从相关性到因果关系：运用系统生物学方法分析代谢组学数据
Metabolomics. 2018;14(4):37. doi: 10.1007/s11306-018-1335-y. Epub 2018 Feb 27.
10
Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data.代谢组学数据富集分析的生物信息学工具评估与比较。
BMC Bioinformatics. 2018 Jan 2;19(1):1. doi: 10.1186/s12859-017-2006-0.