Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks

@article{Bernardo2005ChemogenomicPO,
  title={Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks},
  author={Diego di Bernardo and Michael J. Thompson and Timothy S. Gardner and Sarah E. Chobot and Erin L Eastwood and Andrew P. Wojtovich and Sean J. Elliott and Scott E. Schaus and James J. Collins},
  journal={Nature Biotechnology},
  year={2005},
  volume={23},
  pages={377-383}
}
A major challenge in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets. Here, we present an integrated computational-experimental approach for computing the likelihood that gene products and associated pathways are targets of a compound. This is achieved by filtering the mRNA expression profile of compound-exposed cells using a reverse… 
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References

SHOWING 1-10 OF 51 REFERENCES
Chemogenomic profiling: identifying the functional interactions of small molecules in yeast.
TLDR
The efficacy of a genome-wide protocol in yeast is demonstrated that allows the identification of those gene products that functionally interact with small molecules and result in the inhibition of cellular proliferation and a chemical core structure shared among three therapeutically distinct compounds that inhibit the ERG24 heterozygous deletion strain is identified.
Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways
TLDR
By filtering chemical-genetic profiles for the multidrug-resistant genes and then clustering the compound-specific profiles with a compendium of large-scale genetic interaction profiles, this method provides a powerful means for inferring mechanism of action.
Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling
TLDR
Systematic transcriptional perturbations are used to construct a first-order model of regulatory interactions in a nine-gene subnetwork of the SOS pathway in Escherichia coli that provides a framework for elucidating the functional properties of genetic networks and identifying molecular targets of pharmacological compounds.
Genomic profiling of drug sensitivities via induced haploinsufficiency
TLDR
The discovery that both drug target and hypersensitive loci exhibit drug-induced haploinsufficiency may have important consequences in pharmacogenomics and variable drug toxicity observed in human populations.
Integrating high-throughput and computational data elucidates bacterial networks
TLDR
This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks.
Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling
TLDR
This work shows how the perturbation of carefully chosen genes in a microarray experiment can be used in conjunction with a reverse engineering algorithm to reveal the architecture of an underlying gene regulatory network.
Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.
TLDR
An integrated approach to build, test, and refine a model of a cellular pathway, in which perturbations to critical pathway components are analyzed using DNA microarrays, quantitative proteomics, and databases of known physical interactions, suggests hypotheses about the regulation of galactose utilization and physical interactions between this and a variety of other metabolic pathways.
Computational discovery of gene modules and regulatory networks
TLDR
An algorithm for discovering regulatory networks of gene modules, GRAM (Genetic Regulatory Modules), that combines information from genome-wide location and expression data sets and explicitly links genes to the factors that regulate them by incorporating DNA binding data, which provide direct physical evidence of regulatory interactions.
...
1
2
3
4
5
...