Determining noisy attractors of delayed stochastic gene regulatory networks from multiple data sources
Determining noisy attractors of delayed stochastic gene regulatory networks from multiple data sources
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DOI：
10.1093/bioinformatics/btp411
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发表时间：
2009-09-15
期刊：
影响因子：
5.8
通讯作者：
Ribeiro, Andre S.
中科院分区：
文献类型：
--
作者：
Dai, Xiaofeng;Yli-Harja, Olli;Ribeiro, Andre S.
动机：基因调控网络(GRN)是随机的，因此，没有吸引子，但可以停留在状态空间的有限区域，即噪声吸引子。结果：我们提出了一种Gamma-Bernoulli混合模型聚类算法(Gamma BMM)，用于从Gamma和Bernoulli分布数据中量化状态，以确定随机GRN的噪声吸引子。Gamma BMM使用多个数据源，自然地选择状态的数量，并可以根据可用的数据源的数量和类型扩展到其他参数分布。我们将其应用于蛋白质和RNA水平以及触发器开关的启动子占有态，并表明它可以是双稳、三稳或单稳的，取决于其内部噪声水平。我们表明，这些结果与模型细胞的分化模式是一致的，其路径选择是由开关驱动的。进一步将Gamma BMM应用于枯草芽孢杆菌MEKS模块的建模，结果与实验数据吻合，验证了Gamma BMM的可用性。
Motivation: Gene regulatory networks (GRNs) are stochastic, thus, do not have attractors, but can remain in confined regions of the state space, i.e. the 'noisy attractors', which de. ne the cell type and phenotype.Results: We propose a gamma-Bernoulli mixture model clustering algorithm (Gamma BMM), tailored for quantizing states from gamma and Bernoulli distributed data, to determine the noisy attractors of stochastic GRN. Gamma BMM uses multiple data sources, naturally selects the number of states and can be extended to other parametric distributions according to the number and type of data sources available. We apply it to protein and RNA levels, and promoter occupancy state of a toggle switch and show that it can be bistable, tristable or monostable depending on its internal noise level. We show that these results are in agreement with the patterns of differentiation of model cells whose pathway choice is driven by the switch. We further apply Gamma BMM to a model of the MeKS module of Bacillus subtilis, and the results match experimental data, demonstrating the usability of Gamma BMM.