Dr Ekene Anakor

Supervisor Details

Contact Details

Dr Ekene Anakor

Supervisor Department, Supervisor University

Scientific Background

Research Interests

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Scientific Inspiration

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Research Groups

List Research Groups

Supervision Style

In three words or phrases: Collaborative, technique-driven and outcome-oriented, supportive.

Provision of Training

As a supervisor, I prioritize clarity in any task/project, encourage open questions, and foster an environment where students lead their research while knowing I’m available for support or a chat. This approach ensures structured training and gradual independence.

Progression Monitoring and Management

Together, we will set monthly expectations for your progression meetings. I appreciate regular updates and as you progress and take full ownership, I will still be available to help with troubleshooting when needed.

Communication

I am happy to discuss any issues that are impacting your ability to fulfil your potential or my/our expectations to the best of my ability. My main tools for communication will be email and Teams. I'm open to exploring other methods (Whatsapp or Slack) if they better support your needs.

Meetings

PhD Students can expect scheduled meetings with me at least once per week. These meetings will be face-to-face. I am usually contactable for an instant response on every working day.

Work Patterns

Certain tasks in my lab need to occur at set times, and students need to be able to commit to a rota/timetable shared with other members of the team.

Notice Period for Feedback

I need at least 2 weeks' notice to provide feedback on written work of up to 5,000 words.

MIBTP Project Details

Primary supervisor for:

Modelling lysosomal dysfunction in neurodegeneration: An integrated experimental and computational approach.

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

Modelling lysosomal dysfunction in neurodegeneration: An integrated experimental and computational approach.

Secondary Supervisor(s): Prof Fabian Spill

University of Registration: University of Birmingham

BBSRC Research Themes:

Understanding the Rules of Life (Neuroscience and Behaviour, Systems Biology)

Project Outline

Background:

Lysosomal dysfunction is a shared, early hallmark of many neurodegenerative diseases. This project integrates experimental and computational approaches to model lysosomal spatial dynamics in Amyotrophic Lateral Sclerosis (ALS) as a model for neurodegeneration. It aims to uncover important lysosomal morphological fingerprints & predict impairments driving disease pathology.

Aims & Objectives:

A. Experimental characterisation of lysosomal dynamics in neurons.

-Establish a quantitative baseline for lysosomal dynamics in healthy cells.

-Observe & quantify changes in lysosomal dynamics due to ALS-gene mutations.

B. Develop a predictive mathematical model of lysosomal functioning.

-Build a computational model of lysosomal dynamics using quantitative experimental data.

-Use the model to predict contributions to lysosomal dysfunction.

-Generate testable hypotheses to guide future experiments.

C. Model and validate lysosomal-relevant interventions for neurodegeneration.

-Use the model to predict the effects of perturbations or genetic mutations on lysosomal function.

-Experimentally validate important model predictions through targeted in vitro assays.

Key Terms: Neurodegeneration; ageing; lysosomal dynamics; cell & molecular biology; CRISPR; microscopy; bioimage analysis; mathematics; in silico simulation; computational modelling; supervised machine learning.

Methodology: The project combines experimental biology with computational modelling. Using a suite of techniques, important aspects of lysosomal function will be assessed & quantified in neuronal-like cells with key ALS-gene mutations and patient iPSC lines. Quantitative data from these experiments will be used to build a mathematical model of lysosomal function to help predict contribution to pathology and support in silico simulation of lysosomal-relevant interactions & interventions. The most significant model predictions will be experimentally validated.