Network motifs in the transcriptional regulation network of Escherichia coli

By S. Shen-Orr, Ron Milo, S. Mangan, Uri Alon. Nature Genetics, 2002

3,178 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/382691140046e089646b2bcaea1cecec1cf89c8a

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Citations in the corpus

Discriminative topological features reveal biological network mechanisms (2004, neutral): “coli genetic network [10] (directed), the S.”
Structure and evolution of transcriptional regulatory networks. (2004, positive): “Global regulators can be defined based on the number of genes they regulate [1 ,16 ].”
Application of formal methods to biological regulatory networks: extending Thomas' asynchronous logical approach with temporal logic. (2004, background): “* Preservation of properties when a regulatory network is embedded into another one, including the systematic treatment of knock-out mutants, identification of functional patterns (Shen-Orr et al., 2002) as well as the structuration of huge regulatory networks.”
RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions (2005, background): “Some examples among a large set of cases include the notion of topological network motifs (13); novel or expanded computational methods of promoters; operons (16–18); microarray analyses (19,20); and chIP–chip experiments (21); formal models of transcriptional processes (22); metabolic and regulator”
The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks. (2003, methodology): “coli and yeast is the feedforward loop (FFL).(2) The FFL appears in hundreds of diverse, non-homologous gene systems.”
Revealing strengths and weaknesses of methods for gene network inference (2010, methodology): “To this end, we have analyzed the inference methods performance on the basic building blocks of networks, the network motifs (20, 21).”
The “New” Science of Networks (2004, background): “The topic of network motifs, which is gaining attention in the biological sciences (Milo et al. 2002, Shen-Orr et al. 2002), is, in principle, identical to the triad census approach of Holland & Leinhardt (1981). into a long line of work in organizational sociology starting with Burns & Stalker’s…”
Principles of assembly reveal a periodic table of protein complexes (2015, methodology): “The periodic table illustrates both the variety of observed protein complexes and the space of possible topologies through exhaustive enumeration, analogous to previous strategies of investigating network topologies (21, 22).”
The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo (2006, background): “This assumption breaks down when activators are self-activating and correctly included in biclusters that they regulate [40].”
Negative autoregulation speeds the response times of transcription networks. (2002, background): “This occurs, for example, in over 40% of known Escherichia coli transcription factors (Table 1).(2,3) Examples of transcription factors that negatively regulate their own transcription include Crp, a global transcription factor that mediates the catabolite response of several hundred genes, LexA, th”
Networks beyond pairwise interactions: structure and dynamics (2020, background): “[57] Shai S Shen-Orr, Ron Milo, Shmoolik Mangan, and Uri Alon.”
What can Statistics Contribute to a Causal Understanding? (2007, background): “However, even at a rather fundamental level, e.g. in transcriptional regulation networks the distinction between direct and indirect effects is important (see e.g. Shen-Orr et al., 2002; Wagner, 2002).”
On the uniform generation of random graphs with prescribed degree sequences (2003, methodology): “Some results for the case of the “feed-forward loop” motif [18, 19] are given in Table I.”