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Statistical Properties of Randomly Constructed Boolean Networks that Resemble the Transcription Factor Network of Escherichia coli

机译:随机构造的布尔网络的统计特性,其类似于大肠杆菌的转录因子网络

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The complete genome sequence has been determined for many microbial species, including Escherichia coli, the best-studied of model organisms. This information, together with the global patterns of gene expression revealed by microarray technology, permits a "top-down" approach to understanding the integrated genetic network of an organisms. This global view is in contrast to the "bottom-up" approach of traditional molecular biology, which builds upon the detailed characterization of the individual component parts of the organisms. A third approach to understanding genetic networks, first introduced by Stuart Kauffman, is to construct networks at random and ask what behaviors are expected to arise with high probability, independent of selection.
机译:已经确定了许多微生物物种的完整基因组序列,包括大肠杆菌,最佳研究的模型生物。这些信息与微阵列技术揭示的基因表达的全局模式一起允许“自上而下”方法来理解生物体的综合遗传网络。这种全球视图与传统分子生物学的“自下而上”方法形成鲜明对比,其在有机体的各个组分部分的详细表征上建立。理解遗传网络的第三种方法是由Stuart Kauffman引入的,是在随机构建网络,并询问预期的行为有什么高概率,与选择无关。

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