在癌症中开发网络模型的原则和策略
1Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, New York, NY 10027, USA. dpeer@biology.columbia.edu
Cell
|March 19, 2011
概括
生物学家现在可以使用全基因组数据重建细胞分子网络. 本研究详细介绍了推断这些网络的原则和策略,包括用于癌症研究和药物反应预测的差异网络.
科学领域:
- 基因组学就是基因组学.
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 高通量技术产生大量的全基因组数据.
- 了解功能分子网络对于细胞生物学至关重要.
- 癌症研究需要识别基因和驱动疾病表型的过程.
研究的目的:
- 概述从基因组数据推断网络模型的原则和策略.
- 描述推断癌症差异网络的方法.
- 讨论网络层面的理解在癌症治疗中的应用.
主要方法:
- 从基因组数据推断网络模型,使用概述的原则和策略.
- 采用实验和计算方法推断差异网络.
- 分析癌症数据以识别疾病驱动的基因和过程.
主要成果:
- 建立了网络推理的三个基本原则和六个策略.
- 用癌症作为模型推断差异网络的证明方法.
- 通过网络分析识别了驱动癌症表型的关键基因和过程.
结论:
- 从基因组数据的网络推断为生物研究提供了强大的工具.
- 不同网络分析对于理解癌症等疾病机制是有效的.
- 网络层面的视角可以指导个性化药物反应预测和癌症治疗.
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