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Controlling cancer: a new perspective to uncover tumor growth patterns

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  • Full or part time
    Dr E Navarro-Lopez
  • Application Deadline
    Applications accepted all year round

Project Description

Although the combination of control theory, dynamical systems analysis and systems biology has the potential to reveal important patterns in cancer evolution, it is not widely used. One of the challenges is to find a common language within the parameters of these disciplines. There has been some success in identifying common features in different types of cancer at various stages of the disease. Most of these results are related to intracellular properties and genetic mutations that lead to the onset of replicating malignant cells.

This project will follow the control engineering intuition that designs with modularity have higher adaptability and better resist the changes in their components or environment. We believe that systems thinking can be successfully applied to enhance a better understanding of self-organising and adaptive biological ’systems’ like tumors. The aim of this project is to study the extracellular collective relationships that lead to the formation of tumors when patients have developed cancer. One of our hypotheses is that by uncovering these relationships, we might be able to propose more efficient treatments and detect a cancer profile earlier. This research pushes the boundaries of control engineering, dynamical systems, network science and systems biology.

Funding Notes

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. The funding is available to citizens of a number of European countries (including the UK). In most cases this will include all EU nationals. However full funding may not be available to all applicants and you should read the full department and project details for further information.

References

http://staff.cs.manchester.ac.uk/~navarroe/

How good is research at University of Manchester in Computer Science and Informatics?

FTE Category A staff submitted: 44.86

Research output data provided by the Research Excellence Framework (REF)

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