A systems biology view of cancer

被引:72
作者
Laubenbacher, Reinhard [1 ,6 ]
Hower, Valerie [2 ]
Jarrah, Abdul [1 ]
Torti, Suzy V. [3 ,4 ]
Shulaev, Vladimir [1 ,6 ]
Mendes, Pedro [1 ,5 ,6 ]
Torti, Frank M. [4 ,6 ]
Akman, Steven [4 ,6 ]
机构
[1] Virginia Bioinformat Inst, Blacksburg, VA 24061 USA
[2] Georgia Inst Technol, Sch Math, Atlanta, GA 30332 USA
[3] Wake Forest Univ, Sch Med, Dept Biochem, Winston Salem, NC 27157 USA
[4] Wake Forest Univ, Sch Med, Ctr Comprehens Canc, Winston Salem, NC 27157 USA
[5] Univ Manchester, Sch Comp Sci, Manchester, Lancs, England
[6] Wake Forest Univ, Sch Med, Dept Canc Biol, Winston Salem, NC 27157 USA
来源
BIOCHIMICA ET BIOPHYSICA ACTA-REVIEWS ON CANCER | 2009年 / 1796卷 / 02期
基金
英国生物技术与生命科学研究理事会;
关键词
Systems biology; Cancer; Mathematical modeling; PROTEIN-PROTEIN INTERACTIONS; MATHEMATICAL-MODEL; C-MYC; BREAST; AKT; SURVIVAL; NETWORKS; PATHWAYS; METASTASIS; ACTIVATION;
D O I
10.1016/j.bbcan.2009.06.001
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:129 / 139
页数:11
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