Dr J-M Schwartz, Dr G Nenadic, Prof David Robertson
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
Most biological functions arise from complex sets of physical interactions between proteins and various regulatory pathways, which are extremely difficult to reconstruct, represent and analyse manually. In this project, we will focus on a systems approach to model cellular functions, and use these models to unravel the response of human cells to viral infection and drug treatments. We will develop and exploit a bioinformatics infrastructure to logically model biological function using systematic literature mining and biological data integration. Logical modelling will include construction, exploration and simulation of multilayer networks that include multiple types of entities and relations, including biomolecular, genetic and pathways interactions. Although techniques to reconstruct and analyse networks of homogeneous components (such as protein-protein interactions) are well established, it is more challenging to identify and build coherent representations of heterogeneous systems, e.g. involving both genes and pathways. There is a vast amount of data available in the literature and public databases, but this data suffers from numerous inconsistencies and redundancies. Therefore this project will develop a computational framework to integrate heterogeneous data from both text mining and structured databases in order to build structured multilayered network models. Interactions will be modelled by elementary logical functions; unlike classical network representations, these models are not static but are able to predict the dynamic properties of the system. We will apply these model to (1) understand how viral pathogens infect and control human cells, and (2) identify new combinations of drugs and intervention points that would mitigate viral control. The infrastructure and techniques developed are expected to be of importance to improve the drug development and testing process for the pharmaceutical industry.
http://www.bioinf.manchester.ac.uk/schwartz/
http://gnteam.cs.manchester.ac.uk/
http://www.bioinf.manchester.ac.uk/robertson/
Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.
Funding Notes
This project is to be funded under the BBSRC Doctoral Training Programme. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the BBSRC DTP website www.manchester.ac.uk/bbsrcdtpstudentships
As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
References
•Stoney R, Robertson DL, Nenadic G, Schwartz JM (2018). Mapping biological process relationships and disease perturbations within a pathway network. npj Systems Biology and Applications 4: 22.
•Soul J, Dunn SL, Hardingham TE, Boot-Handford RP, Schwartz JM (2016). PhenomeScape: a Cytoscape app to identify differentially regulated sub-networks using known disease associations. Bioinformatics 32: 3847-9.
•Stoney R, Ames R, Nenadic G, Robertson DL, Schwartz JM (2015). Disentangling the multigenic and pleiotropic nature of molecular function. BMC Systems Biology 9(Suppl 6): S3.
•Wu C, Schwartz JM, Brabant G, Peng SL, Nenadic G (2015). Constructing a molecular interaction network for thyroid cancer via large-scale text mining of gene and pathway events. BMC Systems Biology 9(Suppl 6): S5.
• Wu C, Schwartz JM, Brabant G, Nenadic G (2014). Molecular profiling of thyroid cancer subtypes using large-scale text-mining. BMC Medical Genomics 7(Suppl 3): S3.
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