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检测化学物质暴露引起的代谢变化的策略,从大量特定条件的代谢模型中检测,这些代谢模型是使用计数技术计算的
Louison Fresnais1,2, Olivier Perin3, Anne Riu3
1UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France. louison.fresnais@loreal.com.
BMC bioinformatics
|July 11, 2024
概括
本研究引入了一种计算方法来分析omics数据,减少信息过载,通过建模代谢网络来识别化学作用机制 (MoA). 这种方法有助于毒理学家了解化学物质对细胞代谢的影响.
科学领域:
- 计算毒理学计算毒理学
- 系统生物学 系统生物学
- 代谢建模 代谢建模
背景情况:
- 越来越多的体外化学数据需要先进的计算方法来评估化学安全.
- 奥米克斯数据提供了对生物过程和化学作用机制 (MoA) 的见解.
- 由于复杂的生物调制,分析大型奥米克数据集具有挑战性.
研究的目的:
- 开发一种计算策略,以减少omics数据的复杂性.
- 识别反映化学品代谢影响的代谢子网络.
- 为了将转录组学数据转化为MoA分析的可能反应活动.
主要方法:
- 集成转录组学数据与基因组规模的代谢网络.
- 列出特定条件的代谢模型来表示转录组学数据.
- 应用图形算法来提取用户可读代谢子网络 (mMoA).
- 一个三步工作流:模型构建,差异反应分析和mMoA集群.
主要成果:
- 该策略成功地确定了阿米奥达龙和酸等化学物质的代谢MoA (mMoA).
- 它甚至提供了一些差异表达基因 (DEGs) 或众多DEGs的见解.
- 结果与文献中已知的MOA一致,并表明尚未探索的途径.
结论:
- 该策略使毒理学家能够解读化学物质对细胞代谢的影响.
- 结合基于约束和图形建模,用于对omics数据的新解释.
- 该方法作为一个Python模块和Jupyter笔记本免费提供.
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