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癌症系统生物学:网络建模视角。

Cancer systems biology: a network modeling perspective.

机构信息

Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.

出版信息

Carcinogenesis. 2010 Jan;31(1):2-8. doi: 10.1093/carcin/bgp261. Epub 2009 Oct 27.

DOI:10.1093/carcin/bgp261
PMID:19861649
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2802670/
Abstract

Cancer is now appreciated as not only a highly heterogenous pathology with respect to cell type and tissue origin but also as a disease involving dysregulation of multiple pathways governing fundamental cell processes such as death, proliferation, differentiation and migration. Thus, the activities of molecular networks that execute metabolic or cytoskeletal processes, or regulate these by signal transduction, are altered in a complex manner by diverse genetic mutations in concert with the environmental context. A major challenge therefore is how to develop actionable understanding of this multivariate dysregulation, with respect both to how it arises from diverse genetic mutations and to how it may be ameliorated by prospective treatments. While high-throughput experimental platform technologies ranging from genomic sequencing to transcriptomic, proteomic and metabolomic profiling are now commonly used for molecular-level characterization of tumor cells and surrounding tissues, the resulting data sets defy straightforward intuitive interpretation with respect to potential therapeutic targets or the effects of perturbation. In this review article, we will discuss how significant advances can be obtained by applying computational modeling approaches to elucidate the pathways most critically involved in tumor formation and progression, impact of particular mutations on pathway operation, consequences of altered cell behavior in tissue environments and effects of molecular therapeutics.

摘要

癌症不仅在细胞类型和组织来源方面具有高度异质性,而且还是一种涉及调控控制细胞基本过程(如死亡、增殖、分化和迁移)的多个途径的疾病,现在人们已经认识到这一点。因此,通过信号转导调节这些过程的代谢或细胞骨架过程的分子网络的活性,会通过多种遗传突变与环境背景协同以复杂的方式发生改变。因此,主要的挑战是如何发展对这种多变量失调的可操作理解,既要了解它如何由多种遗传突变引起,又要了解它如何通过预期的治疗来改善。虽然从基因组测序到转录组、蛋白质组和代谢组分析等高通量实验平台技术现在常用于肿瘤细胞和周围组织的分子水平特征描述,但就潜在治疗靶点或干扰的影响而言,所得数据集在直观解释方面存在很大的困难。在这篇综述文章中,我们将讨论通过应用计算建模方法来阐明肿瘤形成和进展中最关键的途径、特定突变对途径操作的影响、改变的细胞行为在组织环境中的后果以及分子治疗的效果,可以取得哪些重大进展。

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