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Although several studies have provided important insights into the general principles of biological networks, the link between network organization and the genome-scale dynamics of the underlying entities (genes, mRNAs, and proteins) and its role in systems behavior remain unclear. Here we show that transcription factor (TF) dynamics and regulatory network organization are tightly linked. By classifying TFs in the yeast regulatory network into three hierarchical layers (top, core, and bottom) and integrating diverse genome-scale datasets, we find that the TFs have static and dynamic properties that are similar within a layer and different across layers. At the protein level, the top-layer TFs are relatively abundant, long-lived, and noisy compared with the core- and bottom-layer TFs. Although variability in expression of top-layer TFs might confer a selective advantage, as this permits at least some members in a clonal cell population to initiate a response to changing conditions, tight regulation of the core- and bottom-layer TFs may minimize noise propagation and ensure fidelity in regulation. We propose that the interplay between network organization and TF dynamics could permit differential utilization of the same underlying network by distinct members of a clonal cell population.
noise, Medicine (General), Chromatin Immunoprecipitation, Genome, Models, Statistical, QH301-705.5, Systems Biology, Amino Acid Motifs, systems biology, dynamics, hierarchy, Saccharomyces cerevisiae, Models, Biological, Article, R5-920, Gene Expression Regulation, Fungal, transcription network, Gene Regulatory Networks, Biology (General), Genome, Fungal, Algorithms, Transcription Factors
noise, Medicine (General), Chromatin Immunoprecipitation, Genome, Models, Statistical, QH301-705.5, Systems Biology, Amino Acid Motifs, systems biology, dynamics, hierarchy, Saccharomyces cerevisiae, Models, Biological, Article, R5-920, Gene Expression Regulation, Fungal, transcription network, Gene Regulatory Networks, Biology (General), Genome, Fungal, Algorithms, Transcription Factors
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 147 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |