Robust signal processing in living cells.
Robust signal processing in living cells.
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DOI：
10.1371/journal.pcbi.1002218
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发表时间：
2011-11
影响因子：
4.3
通讯作者：
Kollmann M
中科院分区：
文献类型：
--
作者：
Steuer R;Waldherr S;Sourjik V;Kollmann M
细胞信号网络已经进化出一种惊人的能力，可以在不确定的环境中可靠地、高保真地发挥作用。许多信号电路所表现出的高精度的一个关键先决条件是它们能够将活性信号化合物的浓度保持在严格限定的范围内，尽管拷贝数和其他有害影响存在强烈的随机波动。基于一个简单的数学形式主义，我们确定的拓扑组织原则，方便这种强大的控制胞内浓度面对五花八门的扰动。我们的框架使我们能够判断多输入多输出反应网络是否对网络参数的大扰动具有鲁棒性，并能够预测设计完美鲁棒的合成网络架构。利用大肠杆菌趋化性途径作为一个标志性的例子，我们提供的实验证据表明，我们的框架确实可以让我们解开强大的信号的拓扑组织。我们表明，特定的组织的途径，使系统能够保持全球浓度的鲁棒性的扩散响应调节器CheY相对于几个占主导地位的扰动。我们的框架提供了一个对立的假设，细胞功能依赖于广泛的机制微调或控制细胞内参数。相反，我们建议，对于一大类扰动，存在一个适当的拓扑结构，使网络输出不变的各自的扰动。蜂窝信令网络必须在不确定的环境中可靠地、高保真地运行。在本文中，我们调查的拓扑原理，以实现这种强大的信号处理在活细胞。具体来说，我们确定的拓扑组织原则，使信号网络保持固定的细胞内浓度的某些分子，如活性信号化合物，在严格定义的范围内-尽管条件的不确定性和面对多重扰动。我们证明，一个适当的拓扑组织呈现的输出路径不变对一大类可能的有害波动，如能量状态或总蛋白质浓度的变化。此外，我们表明，强大的信号处理的拓扑要求可以正式的线性向量空间，表示为不变的扰动空间，预测网络的鲁棒性。构建这个不变的扰动空间的大肠杆菌趋化性途径表明，该途径确实是不变的最占主导地位的扰动，否则会显着阻碍信息传输。我们的框架提供了一个对立的假设，细胞功能依赖于广泛的机制微调或控制细胞内参数。
Cellular signaling networks have evolved an astonishing ability to function reliably and with high fidelity in uncertain environments. A crucial prerequisite for the high precision exhibited by many signaling circuits is their ability to keep the concentrations of active signaling compounds within tightly defined bounds, despite strong stochastic fluctuations in copy numbers and other detrimental influences. Based on a simple mathematical formalism, we identify topological organizing principles that facilitate such robust control of intracellular concentrations in the face of multifarious perturbations. Our framework allows us to judge whether a multiple-input-multiple-output reaction network is robust against large perturbations of network parameters and enables the predictive design of perfectly robust synthetic network architectures. Utilizing the Escherichia coli chemotaxis pathway as a hallmark example, we provide experimental evidence that our framework indeed allows us to unravel the topological organization of robust signaling. We demonstrate that the specific organization of the pathway allows the system to maintain global concentration robustness of the diffusible response regulator CheY with respect to several dominant perturbations. Our framework provides a counterpoint to the hypothesis that cellular function relies on an extensive machinery to fine-tune or control intracellular parameters. Rather, we suggest that for a large class of perturbations, there exists an appropriate topology that renders the network output invariant to the respective perturbations. Cellular signaling networks have to function reliably and with high fidelity in an uncertain environment. In this paper, we investigate the topological principles to achieve such robust signal processing in living cells. Specifically, we identify the topological organizing principles that enable a signaling network to keep the stationary intracellular concentrations of certain molecules, such as active signaling compounds, within tightly defined bounds – despite conditions of uncertainty and in the face of multiple perturbations. We demonstrate that an appropriate topological organization renders the output of the pathway invariant against a large class of possible detrimental fluctuations, such as changes in energy states or total protein concentrations. Furthermore, we show that the topological requirements for robust signal processing can be formalized in terms of a linear vector space, denoted as invariant perturbation space, that predicts the robustness properties of the network. Constructing this invariant perturbation space for the Escherichia coli chemotaxis pathway reveals that the pathway is indeed invariant with respect to most dominant perturbations that would otherwise significantly hamper information transmission. Our framework provides a counterpoint to the hypothesis that cellular function relies on an extensive machinery to fine-tune or control intracellular parameters.
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影响因子：
64.8
作者：
Yu, Richard C.;Pesce, C. Gustavo;Colman-Lerner, Alejandro;Lok, Larry;Pincus, David;Serra, Eduard;Holl, Mark;Benjamin, Kirsten;Gordon, Andrew;Brent, Roger
通讯作者：
Brent, Roger
影响因子：
4.3
作者：
Shinar G;Rabinowitz JD;Alon U
通讯作者：
Alon U
影响因子：
64.5
作者：
Muzzey D;Gómez-Uribe CA;Mettetal JT;van Oudenaarden A
通讯作者：
van Oudenaarden A
影响因子：
3.2
作者：
Thakor, Hemang;Nicholas, Sarah;Stewart, Richard C.
通讯作者：
Stewart, Richard C.
影响因子：
9.9
作者：
Shimizu, Thomas S.;Tu, Yuhai;Berg, Howard C.
通讯作者：
Berg, Howard C.