Models from experiments: combinatorial drug perturbations of cancer cells.
Models from experiments: combinatorial drug perturbations of cancer cells.
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DOI：
10.1038/msb.2008.53
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发表时间：
2008
影响因子：
9.9
通讯作者：
Sander, Chris
中科院分区：
文献类型：
--
作者：
Nelander, Sven;Wang, Weiqing;Nilsson, Bjoern;She, Qing-Bai;Pratilas, Christine;Rosen, Neal;Gennemark, Peter;Sander, Chris
我们提出了一种新的方法来推导网络模型的分子配置文件的扰动细胞系统。网络模型旨在预测组合扰动的定量结果，例如药物对治疗或多种遗传改变。在数学上，我们表示系统的一组节点，代表分子浓度或细胞过程，扰动矢量和相互作用矩阵。扰动后，系统根据具有内置非线性的微分方程随时间演化，类似于Hopfield网络，能够表示上位性和饱和效应。对于一组特定的实验，我们通过最小化复合误差函数推导出相互作用矩阵，旨在提高预测的准确性和网络结构的简单性。为了评估该方法的预测潜力，我们在人乳腺癌细胞系（MCF7）中进行了21个药物对治疗实验，观察磷酸化蛋白和细胞周期标志物。最好的网络模型重新发现了已知的相互作用，并包含有趣的预测。可能的应用包括发现调控相互作用、设计靶向联合治疗和分子生物学网络工程。
We present a novel method for deriving network models from molecular profiles of perturbed cellular systems. The network models aim to predict quantitative outcomes of combinatorial perturbations, such as drug pair treatments or multiple genetic alterations. Mathematically, we represent the system by a set of nodes, representing molecular concentrations or cellular processes, a perturbation vector and an interaction matrix. After perturbation, the system evolves in time according to differential equations with built-in nonlinearity, similar to Hopfield networks, capable of representing epistasis and saturation effects. For a particular set of experiments, we derive the interaction matrix by minimizing a composite error function, aiming at accuracy of prediction and simplicity of network structure. To evaluate the predictive potential of the method, we performed 21 drug pair treatment experiments in a human breast cancer cell line (MCF7) with observation of phospho-proteins and cell cycle markers. The best derived network model rediscovered known interactions and contained interesting predictions. Possible applications include the discovery of regulatory interactions, the design of targeted combination therapies and the engineering of molecular biological networks.
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影响因子：
4.3
作者：
Hart, Christopher E.;Mjolsness, Eric;Wold, Barbara J.
通讯作者：
Wold, Barbara J.
DOI：
10.1016/j.bbrc.2006.03.087
发表时间：
2006-05-19
影响因子：
3.1
作者：
Kim, KH;Kim, HC;Park, KK
通讯作者：
Park, KK
DOI：
10.1007/b137845
发表时间：
2005-01-01
期刊：
STATISTICAL METHODS IN BIOINFORMATICS: AN INTRODUCTION
影响因子：
--
作者：
Ewens, W.;Grant, G.
通讯作者：
Grant, G.
影响因子：
30.8
作者：
Deutscher, David;Meilijson, Isaac;Ruppin, Eytan
通讯作者：
Ruppin, Eytan
影响因子：
2.9
作者：
Edwards, JS;Palsson, BO
通讯作者：
Palsson, BO