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Towards personalisation of cancer treatment-predictive models of DNA damage induced pathways to cancer


   School of Science, Engineering and Environment

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  Prof M Krstic-Demonacos  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The advent of big data drives the need for biomedical resources that facilitate datamining, medical applications and increase the efficiency and predictive power of dynamic models. We have previously generated Boolean models of the DNA damage induced pathways to cancer with p53 tumor suppressor at the centre of the interactome (1-3). These models have been validated in vitro and preliminarily tested as clinical tools using patient data. This project aims to develop the model further by increasing its predictive power across all cancer types. This will be achieved by first using bioinformatic approaches and machine learning techniques to train the model for specific cancers and stratified patients groups. In addition, the creation of a database for the p53 model will allow for further dissemination of results and allow community-driven inputs. This will therefore significantly increase the benefit of these predictive models and allow other researchers to build and contribute to the resource. This resource has a potential of translating accumulated omics type of data into personalised profiles and therapy schemes, potentially leading to clinical impact.

Applicants need to have background in bioinformatics or computer science and need to provide funding for their study. Experience in machine learning would be desirable.

How to apply:

Submit a formal application at this link: http://webapps.ascentone.com/Login.aspx?key=5d4b012a-bb6c-495b-b2e4-b5a56b3ccf00 

 You will need to have the following documents ready to upload to the application site:


References

1. Tian K, Bakker E, Hussain M, Guazzelli A, Alhebshi H, Meysami P, Demonacos C, Schwartz J-M, Mutti L, Krstic-Demonacos M. p53 modeling as a route to mesothelioma patients stratification and novel therapeutic identification. J Transl Med 16 (2018) 282
2. Bakker E, Tian K, Mutti L, Demonacos C, Schwartz JM, Krstic-Demonacos M, Insight into glucocorticoid receptor signalling through interactome model analysis., PLoS Comput Biol. 2017 Nov 6;13(11):e1005825.
3. Tian K, Rajendran R, Doddananjaiah M, Krstic-Demonacos M, Schwartz JM, Dynamics of DNA damage induced pathways to cancer. PLoS One. 2013 Sep 4;8(9):e72303.
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