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从信号网络中的组件到调控基序

From components to regulatory motifs in signalling networks.

作者信息

Ma'ayan Avi, Iyengar Ravi

机构信息

Department of Pharmacology and Biological Chemistry, Mount Sinai School of Medicine, 1 Gustave Levy Place, New York, NY 10029, USA.

出版信息

Brief Funct Genomic Proteomic. 2006 Mar;5(1):57-61. doi: 10.1093/bfgp/ell004. Epub 2006 Feb 20.

Abstract

The developments in biochemistry and molecular biology over the past 30 years have produced an impressive parts list of cellular components. It has become increasingly clear that we need to understand how components come together to form systems. One area where this approach has been growing is cell signalling research. Here, instead of focusing on individual or small groups of signalling proteins, researchers are now using a more holistic perspective. This approach attempts to view how many components are working together in concert to process information and to orchestrate cellular phenotypic changes. Additionally, the advancements in experimental techniques to measure and visualize many cellular components at once gradually grow in diversity and accuracy. The multivariate data, produced by experiments, introduce new and exciting challenges for computational biologists, who develop models of cellular systems made up of interacting cellular components. The integration of high-throughput experimental results and information from legacy literature is expected to produce computational models that would rapidly enhance our understanding of the detail workings of mammalian cells.

摘要

在过去30年里,生物化学和分子生物学的发展已经产生了一份令人印象深刻的细胞成分清单。越来越明显的是,我们需要了解这些成分是如何组合在一起形成系统的。细胞信号研究就是这种方法不断发展的一个领域。在这里,研究人员不再专注于单个或一小群信号蛋白,而是采用更全面的视角。这种方法试图观察有多少成分协同工作来处理信息并协调细胞表型变化。此外,能够同时测量和可视化许多细胞成分的实验技术的进步在多样性和准确性方面逐渐提高。实验产生的多变量数据给计算生物学家带来了新的、令人兴奋的挑战,他们构建由相互作用的细胞成分组成的细胞系统模型。高通量实验结果与传统文献信息的整合有望产生计算模型,从而迅速增进我们对哺乳动物细胞详细运作机制的理解。

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