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Applications of p53 interactome analysis to personalised drug discovery

Hussain, M, Stutchbury, B, Tian, K, Atalay, R, Schwartz, JM and Krstic-Demonacos, M 2014, Applications of p53 interactome analysis to personalised drug discovery , in: Conference: International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2014), 7-9 April, 2014, Universidad de Granada, Granada, Spain. (In Press)

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Abstract

The stress responsive transcription factor p53 is a powerful tumor suppressor implicated in over 50 % of all human cancers. The complexity and nonlinear dynamics of p53 network coupled with extensive literature is challenging. Sys-tems biology methodologies offer promising tools to investigate perturbed net-works, providing structured in silico representations for integrative analysis. The Boolean p53 interactome (PKT206) incorporates the diverse p53 information into a comprehensible framework, and demonstrated good predictive ratios (51 – 75%) using logical steady state analysis. Whilst extensive, Boolean models provide only a qualitative approximation of the system. A prerequisite of diseased in silico models is to accurately represent biological phenomena to characterize network perturbations for effective drug candidates. Thus a quanti-tative approach is necessitated. We have applied a novel signal transduction al-gorithm (STSFA) to PKT206 for model performance and comparison. The STSFA quantitatively analyses large scale ‘omics’ data, typically not accessible with large networks. STFSA in silico simulations of DNA damage and p53 knock out were accurately predicted compared to various gene expression pro-files (P = <1x10-16), and was consistently more accurate than LSSA, generating a significantly higher proportion of correct predictions (P = 0.003). Furthermore, genes CKS2, WWP1, EPHB4 were identified as prospective drug candidates by in silico knockout analysis. In summary, refinement of Boolean PKT206 using STFSA has provided a semi quantitative view of the p53 interactome, and along with the use of ‘omics’ data, may be of greater clinical relevance for identification of perturbed pathways and personalized therapies by superimposition of individual genomic profiles.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Environment and Life Sciences
Journal or Publication Title: Proceedings IWBBIO 2014
Refereed: Yes
Funders: University of Salford
Depositing User: M Krstic-Demonacos
Date Deposited: 16 Mar 2015 13:41
Last Modified: 20 Jul 2016 07:59
URI: http://usir.salford.ac.uk/id/eprint/33366

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