Laubenbacher Reinhard, Hower Valerie, Jarrah Abdul, Torti Suzy V, Shulaev Vladimir, Mendes Pedro, Torti Frank M, Akman Steven
Virginia Bioinformatics Institute, Washington St. (0477), Blacksburg, VA 24061, USA.
Biochim Biophys Acta. 2009 Dec;1796(2):129-39. doi: 10.1016/j.bbcan.2009.06.001. Epub 2009 Jun 6.
In order to understand how a cancer cell is functionally different from a normal cell it is necessary to assess the complex network of pathways involving gene regulation, signaling, and cell metabolism, and the alterations in its dynamics caused by the several different types of mutations leading to malignancy. Since the network is typically complex, with multiple connections between pathways and important feedback loops, it is crucial to represent it in the form of a computational model that can be used for a rigorous analysis. This is the approach of systems biology, made possible by new -omics data generation technologies. The goal of this review is to illustrate this approach and its utility for our understanding of cancer. After a discussion of recent progress using a network-centric approach, three case studies related to diagnostics, therapy, and drug development are presented in detail. They focus on breast cancer, B-cell lymphomas, and colorectal cancer. The discussion is centered on key mathematical and computational tools common to a systems biology approach.
为了理解癌细胞在功能上如何不同于正常细胞，有必要评估涉及基因调控、信号传导和细胞代谢的复杂通路网络，以及由导致恶性肿瘤的几种不同类型突变所引起的其动态变化。由于该网络通常很复杂，通路之间存在多个连接以及重要的反馈回路，因此以可用于严格分析的计算模型形式来表示它至关重要。这就是系统生物学的方法，新的“组学”数据生成技术使其成为可能。本综述的目的是阐述这种方法及其对我们理解癌症的效用。在讨论了以网络为中心的方法的最新进展之后，详细介绍了与诊断、治疗和药物开发相关的三个案例研究。它们聚焦于乳腺癌、B细胞淋巴瘤和结直肠癌。讨论围绕系统生物学方法共有的关键数学和计算工具展开。
