Data-based model and parameter evaluation in dynamic transcriptional regulatory networks
Data-based model and parameter evaluation in dynamic transcriptional regulatory networks
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DOI：
10.1002/prot.20056
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发表时间：
2004-05-01
影响因子：
2.9
通讯作者：
Anastassiou, D
中科院分区：
文献类型：
--
作者：
Cavelier, G;Anastassiou, D
从时间序列数据中发现转录调控网络中的因果关系和连接强度将为分析细胞状态提供有力的工具。这里提出的是网络的模型结构和参数的评估工具的设计。最有效的工具是基于进化策略。我们评估日益复杂的模型，从集总，代数唯象模型希尔函数和衍生函数。这些最后的功能提供了自由能的转录因子结合到他们的运营商，以及协同能。优化结果的基础上发表的实验数据，从大肠杆菌的合成网络。我们的工具所发现的结合和协同的自由能是在相同的生理范围内的噬菌体λ系统中实验得出的。我们还使用了酵母减数分裂表达模式的高密度寡核苷酸微阵列的时间序列数据。该算法适当地找到了受调控的调控酵母基因对的参数，表明对于相关基因，总体合理的计算工作足以找到大量基因的连接性的强度和因果关系。(C)2004 Wiley-Liss，Inc.
Finding the causality and strength of connectivity in transcriptional regulatory networks from time-series data will provide a powerful tool for the analysis of cellular states. Presented here is the design of tools for the evaluation of the network's model structure and parameters. The most effective tools are found to be based on evolution strategies. We evaluate models of increasing complexity, from lumped, algebraic phenomenological models to Hill functions and thermodynamically derived functions. These last functions provide the free energies of binding of transcription factors to their operators, as well as cooperativity energies. Optimization results based on published experimental data from a synthetic network in Escherichia coli are presented. The free energies of binding and cooperativity found by our tools are in the same physiological ranges as those experimentally derived in the bacteriophage lambda system. We also use time-series data from high-density oligonucleotide microarrays of yeast meiotic expression patterns. The algorithm appropriately finds the parameters of pairs of regulated regulatory yeast genes, showing that for related genes an overall reasonable computation effort is sufficient to find the strength and causality of the connectivity of large numbers of them. (C) 2004 Wiley-Liss, Inc.