Systematic quantitative characterization of cellular responses induced by multiple signals.
Systematic quantitative characterization of cellular responses induced by multiple signals.
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DOI：
10.1186/1752-0509-5-88
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发表时间：
2011-05-30
影响因子：
--
通讯作者：
Sun R
中科院分区：
文献类型：
--
作者：
Al-Shyoukh I;Yu F;Feng J;Yan K;Dubinett S;Ho CM;Shamma JS;Sun R
细胞不断地感知许多内部和环境信号，并通过其复杂的信号网络做出反应，导致特定的生物学结果。然而，系统的表征和优化的多信号响应仍然是一个紧迫的挑战，传统的实验方法，由于不断增加的信号数量和它们的强度相关联的复杂性。我们建立并验证了一种数据驱动的数学方法来系统地表征信号-响应关系。我们的研究结果展示了数学学习算法如何能够系统地表征多信号诱导的生物活性。所提出的方法使得能够识别可以导致期望的生物反应的输入组合。回顾过去，结果表明，与单一药物不同，适当选择的药物组合可以导致不同细胞类型的反应存在显著差异，从而增加某些组合的差异靶向。已识别组合的成功验证证明了这种方法的强大功能。此外，该方法能够检查测试信号的所有低阶混合物的功效。该方法还使得能够识别所施加的信号之间的系统级信令交互。许多识别的信号相互作用与文献一致，并出现了其他未知的相互作用。这种方法可以促进系统生物学的发展和癌症和其他疾病的最佳药物组合疗法，以及了解在用多种信号治疗时细胞网络内的关键相互作用。
Cells constantly sense many internal and environmental signals and respond through their complex signaling network, leading to particular biological outcomes. However, a systematic characterization and optimization of multi-signal responses remains a pressing challenge to traditional experimental approaches due to the arising complexity associated with the increasing number of signals and their intensities. We established and validated a data-driven mathematical approach to systematically characterize signal-response relationships. Our results demonstrate how mathematical learning algorithms can enable systematic characterization of multi-signal induced biological activities. The proposed approach enables identification of input combinations that can result in desired biological responses. In retrospect, the results show that, unlike a single drug, a properly chosen combination of drugs can lead to a significant difference in the responses of different cell types, increasing the differential targeting of certain combinations. The successful validation of identified combinations demonstrates the power of this approach. Moreover, the approach enables examining the efficacy of all lower order mixtures of the tested signals. The approach also enables identification of system-level signaling interactions between the applied signals. Many of the signaling interactions identified were consistent with the literature, and other unknown interactions emerged. This approach can facilitate development of systems biology and optimal drug combination therapies for cancer and other diseases and for understanding key interactions within the cellular network upon treatment with multiple signals.
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影响因子：
11.2
作者：
Iadevaia S;Lu Y;Morales FC;Mills GB;Ram PT
通讯作者：
Ram PT
影响因子：
2.2
作者：
Jain, AK;Mao, JC;Mohiuddin, KM
通讯作者：
Mohiuddin, KM
影响因子：
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作者：
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Leuenberger, H
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作者：
Irish, JM;Kotecha, N;Nolan, GP
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Nolan, GP
影响因子：
21.3
作者：
Natarajan, Madhusudan;Lin, Keng-Mean;Ranganathan, Rama
通讯作者：
Ranganathan, Rama