In silico models of cancer.
In silico models of cancer.
复制标题
DOI：
10.1002/wsbm.75
复制
发表时间：
2010-07
影响因子：
7.9
通讯作者：
Price, Nathan D.
中科院分区：
文献类型：
--
作者：
Edelman, Lucas B.;Eddy, James A.;Price, Nathan D.
癌症是一种复杂的疾病，涉及多种类型的生物相互作用，跨越不同的物理，时间和生物尺度。这种复杂性对癌症生物学的表征提出了重大挑战，并激发了在分子、细胞和生理系统背景下对癌症的研究。正在开发癌症的计算模型，以帮助生物发现和临床医学。快速发展的实验和分析工具促进了这些计算机模型的发展，这些工具可以生成信息丰富的高通量生物数据。在基因组、转录组和途径水平上的癌症统计模型已被证明在开发诊断和预后分子特征以及鉴定干扰途径方面是有效的。统计推断的网络模型可以证明在可以避免数据过拟合的环境中是有用的，并为生物发现提供了重要手段。基于机制的信号和代谢模型应用从实验中获得的生化过程的先验知识，也可以在数据可用的情况下重建，并且可以提供关于这些系统的动态行为的洞察力和预测能力。在较长的长度尺度上，肿瘤微环境和其他组织水平相互作用的连续体和基于试剂的模型能够对癌细胞群和肿瘤进展进行建模。尽管癌症是使用系统方法研究最多的人类疾病之一，但在计算机癌症生物学的巨大潜力完全实现之前，仍然存在重大挑战。
Cancer is a complex disease that involves multiple types of biological interactions across diverse physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Statistical models of cancer at the genomic, transcriptomic, and pathway levels have proven effective in developing diagnostic and prognostic molecular signatures, as well as in identifying perturbed pathways. Statistically-inferred network models can prove useful in settings where data overfitting can be avoided, and provide an important means for biological discovery. Mechanistically-based signaling and metabolic models that apply a priori knowledge of biochemical processes derived from experiments can also be reconstructed where data are available, and can provide insight and predictive ability regarding the dynamical behavior of these systems. At longer length scales, continuum and agent-based models of the tumor microenvironment and other tissue-level interactions enable modeling of cancer cell populations and tumor progression. Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized.
登录
查看更多内容
影响因子：
64.5
作者：
Bonneau, Richard;Facciotti, Marc T.;Baliga, Nitin S.
通讯作者：
Baliga, Nitin S.
影响因子：
64.8
作者：
Alizadeh, AA;Eisen, MB;Staudt, LM
通讯作者：
Staudt, LM
影响因子：
50.3
作者：
Allinen, M;Beroukhim, R;Polyak, K
通讯作者：
Polyak, K
影响因子：
9.9
作者：
通讯作者：
--
影响因子：
2
作者：
Alarcón, T;Byrne, HM;Maini, PK
通讯作者：
Maini, PK