梯度感应的定量分析：构建癌症趋化性预测模型。
Quantitative analysis of gradient sensing: towards building predictive models of chemotaxis in cancer.
机构信息
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
出版信息
Curr Opin Cell Biol. 2012 Apr;24(2):284-91. doi: 10.1016/j.ceb.2012.01.001. Epub 2012 Jan 26.
Abstract
Chemotaxis of tumor cells in response to a gradient of extracellular ligand is an important step in cancer metastasis. The heterogeneity of chemotactic responses in cancer has not been widely addressed by experimental or mathematical modeling techniques. However, recent advancements in chemoattractant presentation, fluorescent-based signaling probes, and phenotypic analysis paradigms provide rich sources for building data-driven relational models that describe tumor cell chemotaxis in response to a wide variety of stimuli. Here we present gradient sensing, and the resulting chemotactic behavior, in a 'cue-signal-response' framework and suggest methods for utilizing recently reported experimental methods in data-driven modeling ventures.
摘要
肿瘤细胞对外源配体梯度的趋化作用是癌症转移的重要步骤。实验或数学建模技术尚未广泛解决癌症趋化反应的异质性。然而，最近在趋化因子呈现、基于荧光的信号探针和表型分析范例方面的进展为构建数据驱动的关系模型提供了丰富的资源，这些模型描述了肿瘤细胞对各种刺激物的趋化反应。在这里，我们在“线索-信号-反应”框架中呈现梯度感应和由此产生的趋化行为，并提出了利用最近报道的实验方法进行数据驱动建模的方法。
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