原核生物基因表达的动态模拟：应用于大肠杆菌的葡萄糖-乳糖二倍体
Dynamic modeling of gene expression in prokaryotes: application to glucose-lactose diauxie in Escherichia coli
摘要：
基因的共表达，或者更一般地说，表达谱的相似性，对仅基于DNA微阵列时间序列的数据推断基因调控网络（GRN）构成了不可逾越的障碍。具有相似表达谱的基因的聚类允许GRN的粗粒度视图和聚类之间的连通性的概率确定。我们提出了一个基因簇网络的时间演化模型，该模型考虑了基因产物与基因的相互作用，并通过非恒定的降解速率与其他基因产物的相互作用。通过使用多项式函数插值时间数据点，减少了模型参数的数量。以这种方式，参数估计的任务被简化为线性代数方程组，从而使计算时间缩短了几个数量级。为了消除不相关的网络，我们测试每个GRN相对于参数变化的稳定性，并对其在稳态附近的行为施加限制。我们将我们的模型和方法应用于在葡萄糖-乳糖二倍性期间收集的大肠杆菌上的DNA微阵列时间序列数据，并推断实验的不同阶段的最可能的聚类网络。
英文摘要：
Coexpression of genes or, more generally, similarity in the expression profiles poses an unsurmountable obstacle to inferring the gene regulatory network (GRN) based solely on data from DNA microarray time series. Clustering of genes with similar expression profiles allows for a course-grained view of the GRN and a probabilistic determination of the connectivity among the clusters. We present a model for the temporal evolution of a gene cluster network which takes into account interactions of gene products with genes and, through a non-constant degradation rate, with other gene products. The number of model parameters is reduced by using polynomial functions to interpolate temporal data points. In this manner, the task of parameter estimation is reduced to a system of linear algebraic equations, thus making the computation time shorter by orders of magnitude. To eliminate irrelevant networks, we test each GRN for stability with respect to parameter variations, and impose restrictions on its behavior near the steady state. We apply our model and methods to DNA microarray time series' data collected on Escherichia coli during glucose-lactose diauxie and infer the most probable cluster network for different phases of the experiment.
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