Computational modeling of mammalian signaling networks.
Computational modeling of mammalian signaling networks.
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DOI：
10.1002/wsbm.52
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发表时间：
2010-03
影响因子：
7.9
通讯作者：
Covert, Markus W.
中科院分区：
文献类型：
--
作者：
Hughey, Jacob J.;Lee, Timothy K.;Covert, Markus W.
信号转导研究中最令人兴奋的发展之一是通过计算模型启动生物学发现的研究的激增。在这里，我们回顾了促成此类研究的主要努力。首先，我们描述了通常用于识别感兴趣网络中的分子成分和相互作用以及所表现出的动态行为的实验技术。接下来，我们回顾用于建模信号网络行为的数学方法。最后，我们关注“模型驱动发现”的三个具体实例：信号网络的计算建模带来了经过实验验证的新见解。信号转导网络是极其复杂的细胞外环境和精心策划的细胞反应之间的桥梁。这些网络主要由蛋白质组成，这些蛋白质可以相互作用、移动到特定的细胞位置、或者被修改或降解。这些事件的整合通常会导致转录因子的激活或失活，然后诱导或抑制数千个基因的表达。由于在将环境线索转化为细胞行为方面发挥着关键作用，信号网络故障可能导致多种病理。一个例子是癌症，其中许多与癌症发生和发展有关的关键基因都是信号通路的组成部分。详细了解此类疾病背后的细胞信号网络可能对于开发新疗法非常有用。然而，信令网络的复杂性使得它们的综合功能无法通过计算模拟来确定。近年来，信号转导的数学模型带来了一些令人兴奋的新发现和生物学发现。在这里，我们回顾了哺乳动物信号网络计算建模的工作，以及这种建模所展示的价值。我们首先回顾与模型构建工作相关的实验技术，包括映射网络交互以及确定动态网络对扰动的响应。然后我们讨论建模策略，最后重点关注三个案例，它们生动地说明了模型发现新生物学的力量。
One of the most exciting developments in signal transduction research has been the proliferation of studies in which a biological discovery was initiated by computational modeling. Here we review the major efforts that enable such studies. First, we describe the experimental technologies that are generally used to identify the molecular components and interactions in, and dynamic behavior exhibited by, a network of interest. Next, we review the mathematical approaches that are used to model signaling network behavior. Finally, we focus on three specific instances of “model-driven discovery”: cases in which computational modeling of a signaling network has led to new insights which have been verified experimentally. Signal transduction networks are the bridge between the extraordinarily complex extracellular environment and a carefully orchestrated cellular response. These networks are largely composed of proteins which can interact, move to specific cellular locations, or be modified or degraded. The integration of these events often leads to the activation or inactivation of transcription factors, which then induce or repress the expression of thousands of genes. Because of this critical role in translating environmental cues to cellular behaviors, malfunctioning signaling networks can lead to a variety of pathologies. One example is cancer, in which many of the key genes found to be involved in cancer onset and development are components of signaling pathways. A detailed understanding of the cellular signaling networks underlying such diseases would likely be extremely useful in developing new treatments. However, the complexity of signaling networks is such that their integrated functions cannot be determined without computational simulation. In recent years, mathematical modeling of signal transduction has led to some exciting new findings and biological discoveries. Here, we review the work that has enabled computational modeling of mammalian signaling networks, as well as the demonstrated value of such modeling. We begin by reviewing the experimental techniques commonly associated with model-building efforts, in terms of mapping network interactions as well as determining the dynamic network response to perturbation. We then discuss modeling strategies, and finally focus on three cases that dramatically illustrate the power of models to discover new biology.
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影响因子：
46.9
作者：
Einav, Shirit;Gerber, Doron;Bryson, Paul D.;Sklan, Ella H.;Elazar, Menashe;Maerkl, Sebastian J.;Glenn, Jeffrey S.;Quake, Stephen R.
通讯作者：
Quake, Stephen R.
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
56.9
作者：
Amonlirdviman, K;Khare, NA;Tomlin, CJ
通讯作者：
Tomlin, CJ
影响因子：
64.8
作者：
Chang, Hannah H.;Hemberg, Martin;Huang, Sui
通讯作者：
Huang, Sui
影响因子：
2
作者：
Collier, JR;Monk, NAM;Lewis, JH
通讯作者：
Lewis, JH