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Dr Alexis Barr

Senior Researcher

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Dr Alexis Barr is a postdoctoral researcher who specialises in the mechanisms used by cells as they grow and divide. Team: Dynamical Cell Systems

T 020 7153 5170

Biography and research overview

Dr Alexis Barr is working with Dr Chris Bakal’s team at the ICR and Professor Bela Novak’s team at the University of Oxford to understand how cancer cells lose control of their cell division cycle. In October 2013, she received a Pathway to Independence award from the Academic Dean.

The cell cycle is a series of consecutive stages that a cell must irreversibly pass through in order to divide. It consists of five stages: G0, G1, S (DNA synthesis), G2 and M (Mitosis). Dr Barr is particularly interested in the control of the transition from G1 into S-phase, since many cancer cells lose some of the controls over this transition, allowing them to undergo uncontrolled rounds of cell division.

To understand the signalling networks that regulate the G1 to S transition in both tumour and non-tumour cells, Dr Barr and her collaborators are aiming to build a computational model of the transition to determine: a) the systems that operate to control the transition, b) how the transition is made irreversible, c) how cancer cells can circumvent this control, and d) what strategies could be employed to reinstate control in tumour cells to prevent them from duplicating.

To build a computational model, Dr Barr uses video microscopy to track the behaviour of cells over time. Specifically, she has developed a series of fluorescent sensors that can monitor the activation of different signalling pathways involved in the G1 to S transition, so that she can determine when, where and for how long a particular component is active for. By combining imaging with automated and quantitative image analysis, Dr Barr can extract parameters that are then used to build and constrain a mathematical model.

Once an accurate and predictive mathematical model of the transition is completed, potential treatment strategies could be tested in silico to guide more rational drug combination strategies in the future.

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