AI-enhanced biological network modeller software: A new horizon in bio-modelling

We are offering a self-funded PhD position in computer science with the Computational Biology Group. The project focuses on utilising AI to improve biological modelling.

Applications are accepted on a rolling basis.

If you have any questions or would like to arrange an informal discussion, please reach out to Dr Alina Miron at alina.miron@brunel.ac.uk.

The project

Our research focuses on developing an AI-enhanced biological network modeller software.

Biological networks are complex, but understanding them better can open new doors to drug discovery and new treatment methods.

Biological modelling is an area of systems biology and computer science where the modeller tries to build a model of the systems in order to understand them better and predict their behaviour.

While there are different tools for modelling biological systems, there has not been much focus on using machine learning and AI to predict the behaviour of the organism based on experimental data.

This project addresses critical challenges in systems biology and computer science, aiming to create software that uses machine learning to predict the behaviours of organisms and AI to predict changes under different circumstances.

A successful software can be introduced to biologists for modelling biological networks to save time and resources on experiments that involve living organisms.

As a postgraduate researcher, you will:

• Engage in developing software that collects data from open-source repositories and implements it for modelling purposes.
• Develop skills in machine learning, AI, and biological network modelling.
• Collaborate with leading experts in the field.
• Publish your findings in top-tier journals.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

1. Contact the supervisor by email or phone to discuss your interest and find out if you would be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Alina Miron - Alina is a lecturer in the Computer Science department and a member of the Intelligent Data Analysis (IDA). Alina has a PhD in machine learning in the field of autonomous vehicles and is an artificial intelligence researcher, developer and educator.  She has excellent understanding of data, especially real-time data and a strong background in computer vision, natural language processing and data science.

Leila Ghanbar - I am a Computational Biologist with a background in Microbiology and Biomedical Engineering. I am interested in modelling biological behaviours in silicon in order to understand and predict them.  I attained my PhD from Brunel University London under the supervision of Professor David Gilbert and Alessandro Pandini. During my PhD, I created a library of models with different levels of complexity in order to portray different biological behaviours such as movement (chemotaxis), communication, response, reproduction and death. These models can be used individually to for simpler behaviours and could be combined for more complex ones. I have also created a detailed model of biofilm formation, a biological response activity to population density, and combined it with a quorum sensing model. All these models were created in Petri nets, simulated in Spike and analysed using R.  Currently, I am interested in developing new modelling approaches for biological systems. I am looking at novel methods that are simple and yet powerful enough to be efficient and useful in creating models that help with understanding and predicting biological systems. 

Related Research Group(s)

Computational Biology - Developing and applying novel methodologies for computational modelling, simulation and analysis of biological systems