The publication detail shows the title, authors (with indicators showing other profiled authors), information on the publishing organization, abstract and a link to the article in PubMed. This abstract is what is used to create the fingerprint of the publication. If any grants are referenced by the publication, they will be listed here as well.
Conditioning-based modeling of contextual genomic regulation.
Edward R Dougherty; Marcel Brun; Jeffrey M Trent; Michael L Bittner (Profiled Author: Jeffrey Trent)
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA email@example.com
IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 2009;6(2):310-20.
A more complete understanding of the alterations in cellular regulatory and control mechanisms that occur in the various forms of cancer has been one of the central targets of the genomic and proteomic methods that allow surveys of the abundance and/or state of cellular macromolecules. This preference is driven both by the intractability of cancer to generic therapies, assumed to be due to the highly varied molecular etiologies observed in cancer, and by the opportunity to discern and dissect the regulatory and control interactions presented by the highly diverse assortment of perturbations of regulation and control that arise in cancer. Exploiting the opportunities for inference on the regulatory and control connections offered by these revealing system perturbations is fraught with the practical problems that arise from the way biological systems operate. Two classes of regulatory action in biological systems are particularly inimical to inference, convergent regulation, where a variety of regulatory actions result in a common set of control responses (crosstalk), and divergent regulation, where a single regulatory action produces entirely different sets of control responses, depending on cellular context (conditioning). We have constructed a coarse mathematical model of the propagation of regulatory influence in such distributed, context-sensitive regulatory networks that allows a quantitative estimation of the amount of crosstalk and conditioning associated with a candidate regulatory gene taken from a set of genes that have been profiled over a series of samples where the candidate's activity varies.
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