Thiele2013 - Gall bladder glandular cells

Thiele2013 - Gall bladder glandular cells

The model of gall bladder glandular cells metabolism is derived from the community-driven global reconstruction of human metabolism (version 2.02, MODEL1109130000
).

This model is described in the article:

A community-driven global reconstruction of human metabolism.

Thiele I, et al
.
Nature Biotechnology
Abstract:

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven,
consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared
with its predecessors, the reconstruction has improved topological and functional features, including ~2x more reactions and ~1.7x more unique metabolites. Using
Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic
data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically
generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will
facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

This model is hosted on BioModels Database
and identified by: MODEL1310110042
.
To cite BioModels Database, please use: BioModels Database: An enhanced,
curated and annotated resource for published quantitative kinetic models
.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer
to CC0 Public Domain Dedication
for more information.

Thiele等人2013年研究——胆囊腺细胞

本胆囊腺细胞代谢模型源自人类代谢组社区驱动的全局重构（版本2.02，编号MODEL1109130000）。

本模型的相关描述见于下述论文：《人类代谢组的社区驱动全局重构》，作者Thiele I等，发表于《自然·生物技术》（Nature Biotechnology）。

摘要：
过往已重构多种人类代谢模型，但均仅覆盖部分已知知识。本文介绍Recon 2——一款社区驱动的共识性代谢重构模型，也是目前可用于计算建模的最全面的人类代谢组表征。相较于前代模型，该重构在拓扑与功能特性上均有提升：反应数量提升约2倍，独特代谢物数量提升约1.7倍。借助Recon 2，我们对49种先天性代谢错误的代谢生物标志物变化进行了预测，与实验数据对比准确率达77%。将代谢组学数据与药物信息映射至Recon 2，展现了其整合与分析多类数据的潜力。结合蛋白质表达数据，我们自动生成了包含65种细胞类型特异性模型的纲要，为手动校正或细胞特异性代谢特性研究提供了基础。Recon 2将助力诸多未来生物医学研究，可通过http://humanmetabolism.org/免费获取。

本模型托管于BioModels数据库（BioModels Database），编号为MODEL1310110042。
引用BioModels数据库时，请参考以下表述：《BioModels数据库：面向已发表定量动力学模型的增强、经人工校验与注释的资源》。

在法律允许的最大范围内，本编码模型的全部版权及相关邻接权利已奉献至全球公共领域。更多信息请参阅CC0公共领域贡献协议（CC0 Public Domain Dedication）。