Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems.
Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems.
复制标题
DOI：
10.1371/journal.pcbi.1005849
复制
发表时间：
2017-11
影响因子：
4.3
通讯作者：
Piermarocchi C
中科院分区：
文献类型：
--
作者：
Szedlak A;Sims S;Smith N;Paternostro G;Piermarocchi C
现代时间序列基因表达和其他组学数据集使细胞过程的动力学，如细胞周期和药物化合物的反应前所未有的分辨率。在不久的将来，时间序列数据集的增殖预期，我们使用Hopfield模型，一个基于自旋眼镜的递归神经网络，在HeLa（人宫颈癌）和S。酿酒酵母细胞我们研究了这些循环Hopfield系统的一些丰富的动力学性质，包括模拟细胞的群体重新创建实验表达数据和噪声对动力学的影响的能力。接下来，我们使用遗传算法来识别基因组，当被代表基因沉默化合物（如激酶抑制剂）的局部外部场选择性抑制时，这些基因组会破坏编码的细胞周期。例如，我们发现，抑制四种激酶AURKB、NEK 1、TTK和WEE 1的集合会导致模拟的HeLa细胞在M期积累。最后，我们提出了可能的改进和扩展我们的模型。细胞周期是一个母细胞复制其DNA并分裂成两个子细胞的过程，在许多癌症中是一个上调的过程。识别基因抑制靶点以调节细胞周期对于开发有效的治疗方法是重要的。虽然现代高通量技术提供了前所未有的分辨率的生物过程的分子细节，如细胞周期，分析大量的实验数据和提取可操作的信息仍然是一项艰巨的任务。在这里，我们使用Hopfield模型（一种递归神经网络）和来自人宫颈癌细胞和酵母细胞的基因表达数据创建细胞周期过程的动力学模型。我们发现，该模型再现了实验数据中观察到的振荡。将噪声水平（代表基因表达和调控中固有的随机性）调整到“混沌边缘”对于系统的正确行为至关重要。然后，我们使用这个模型来确定潜在的基因靶破坏细胞周期的过程。该方法可以应用于其他时间序列数据集，并用于预测未经测试的目标扰动的影响。
Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases AURKB, NEK1, TTK, and WEE1 causes simulated HeLa cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model. Cell cycle—the process in which a parent cell replicates its DNA and divides into two daughter cells—is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.
登录
查看更多内容
影响因子：
4.8
作者：
Bruck, Irina;Kaplan, Daniel L.
通讯作者：
Kaplan, Daniel L.
影响因子：
64.5
作者：
Ferrell, James E., Jr.;Tsai, Tony Yu-Chen;Yang, Qiong
通讯作者：
Yang, Qiong
DOI：
10.1209/0295-5075/4/2/007
发表时间：
1987-07-15
期刊：
EUROPHYSICS LETTERS
影响因子：
--
作者：
DERRIDA, B;GARDNER, E;ZIPPELIUS, A
通讯作者：
ZIPPELIUS, A
影响因子：
7.3
作者：
Choi, Minsoo;Shi, Jue;Cho, Kwang-Hyun
通讯作者：
Cho, Kwang-Hyun
影响因子：
3.4
作者：
Cote, S;Tatnall, ARL
通讯作者：
Tatnall, ARL