Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics

By M. Ronen, Revital Rosenberg, Boris I. Shraiman, Uri Alon. Proceedings of the National Academy of Sciences of the United States of America, 2002

562 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/485c0fa49b2456c42494b53abd931234a52c9a72

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Citations in the corpus

Inference of complex biological networks: distinguishability issues and optimization-based solutions (2011, background): “gene expression [5-13], signal transduction [14-17] and metabolic networks [18-25].”
The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks. (2003, methodology): “Promoter activity was measured by means of an automated multiwell fluorimeter from cultures of strains bearing the reporter plasmids.(18,19) Both green fluorescent protein (GFP) fluorescence and optical absorbance (A600 nm) were measured at a very high temporal resolution (about once per minute).”
Measuring the activity of BioBrick promoters using an in vivo reference standard (2009, methodology): “Since we are calculating a ratio of the GFP synthesis rates we do not need to determine each rate in absolute units of GFP per second per cell, rather we can use the background-subtracted fluorescence (F) that is proportional to the number of GFP molecules and the background-subtracted absorbance (A”
Fluorescence and bioluminescence reporter functions in genetically modified bacterial sensor strains (2003, methodology): “The plasmid containing the recA0<EGFP fusion (u66recA) was constructed by Alon and co-workers [22], and was used to transform our working E.”
Gene networks inference using dynamic Bayesian networks (2003, background): “The vertical scale has no absolute meaning, since we took the (dGFP/dt)/OD values of Ronen et al. (2002), because they are proportional to the expression levels of the genes as explained previously.”
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles (2007, background): “Pioneering efforts to identify regulatory interactions on a genome scale have used machine-learning algorithms to identify cis-regulatory motifs or transcription factor target genes using a large set of expression arrays [2–18], genomewide location analysis chromatin immunoprecipitation (ChIP-Chip) ”
Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs (2004, background): “, 2002) and experimentally (Kalir et al., 2001; Ronen et al., 2002; Zaslaver et al., 2004) to generate temporal programs of expression.”
Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking (2007, background): “The importance of network motifs as information-processing modules has been modeled theoretically [12,21] and verified experimentally [8,13,20,25].”
Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks (2005, unknown): “3-aminotriazole Histidine biosynthesis48 His3 Organic acid metabolism (1, 10–7) FRM2 (8), BIO5 (9), YAT2 (10), ARO10 (18), ARO9 (20), CHA1 (21), BIO3 (31), ARG1 (33), ARG4 (37), HIS5 b (42), LYS1 (47), SAM2 (50)”
Structure and evolution of transcriptional regulatory networks. (2004, unknown): “In this direction, computational and experimental approaches that model regulation of individual genes at high resolution (Ronen et al. 2002; Zaslaver et al. 2006) or the changes in the structure of entire regulatory network of an organism (Luscombe et al.”