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Adv Appl Bioinform Chem. 2009;2:125-38. doi: 10.2147/aabc.s8211. Epub 2009 Dec 03.

Logical network of genotoxic stress-induced NF-κB signal transduction predicts putative target structures for therapeutic intervention strategies.

Advances and applications in bioinformatics and chemistry : AABC

Rainer Poltz, Raimo Franke, Katrin Schweitzer, Steffen Klamt, Ernst-Dieter Gilles, Michael Naumann

Affiliations

  1. Institute of Experimental Internal Medicine, Otto von Guericke University, Magdeburg, Germany.

PMID: 21918620 PMCID: PMC3169943 DOI: 10.2147/aabc.s8211

Abstract

Genotoxic stress is induced by a broad range of DNA-damaging agents and could lead to a variety of human diseases including cancer. DNA damage is also therapeutically induced for cancer treatment with the aim to eliminate tumor cells. However, the effectiveness of radio- and chemotherapy is strongly hampered by tumor cell resistance. A major reason for radio- and chemotherapeutic resistances is the simultaneous activation of cell survival pathways resulting in the activation of the transcription factor nuclear factor-kappa B (NF-κB). Here, we present a Boolean network model of the NF-κB signal transduction induced by genotoxic stress in epithelial cells. For the representation and analysis of the model, we used the formalism of logical interaction hypergraphs. Model reconstruction was based on a careful meta-analysis of published data. By calculating minimal intervention sets, we identified p53-induced protein with a death domain (PIDD), receptor-interacting protein 1 (RIP1), and protein inhibitor of activated STAT y (PIASy) as putative therapeutic targets to abrogate NF-κB activation resulting in apoptosis. Targeting these structures therapeutically may potentiate the effectiveness of radio-and chemotherapy. Thus, the presented model allows a better understanding of the signal transduction in tumor cells and provides candidates as new therapeutic target structures.

Keywords: Boolean network; DNA-damage response; NF-κB; apoptosis; cancer therapy

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