Hypothesis-driven automated strategy for drug target identification in Parkinson’s Disease

Project ID: 2531ad1491

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UCL Lead department: Biochemical Engineering

Project Summary:

The correct identification of drug targets lies at the heart of drug discovery. Despite extensive efforts poured into the research of diseases and disorders, the search for drug targets often focuses on one specific mechanism or at times a single protein. An engineering approach can address these challenges and speed up the processes that enable the identification of previously untapped drug targets. This project aims to implement a systems-based approach to model Parkinson’s disease and propose a systematic strategy to speed up drug target identification. A comprehensive formal disease model will be built, which will be used to generate hypotheses purposefully at scale, which will then be tested experimentally. To allow high-throughput large screening and data-driven hypothesis generation, an experimental setup will be designed to utilise multiplexed microscale bioreactors to grow adherent neuronal cells in automated liquid handling platforms. This will minimise the physical footprint of the experiments and reduce the labour-intensive maintenance of cell cultures over extended periods of time. The results of the experiments will further be used to streamline the test cases to identify and test novel unexplored drug target candidates.

You will be working with experts in modelling, experiment automation, microfluidics and cell culturing. In addition to professional skill and research skills development, you will be trained on formal modelling by Petri Nets, on using automated liquid handling platforms to streamline experimental design and execution, cell culturing, monitoring and analytics, the use of microfluidic platforms, particularly of microbioreactors for adherent cell cultures. You will also receive training on responsible research and innovation and will have the opportunity to develop your RRI agenda along with your project.

For this project, we are looking for enthusiastic individuals with background training or degree in biochemical engineering, chemical engineering, biochemistry, biotechnology, molecular biology, systems biology, or related fields.