Predicting microbiome-drug interactions using metabolic modelling and machine learning

The Medical Research Council Toxicology Unit is an internationally renowned institution focused on the delivery of field-changing mechanistic insights into toxicology and disease. This studentship will join the Unit’s new Computational Systems Biology group, led by Dr Kiran Patil.

The Unit is part of the University of Cambridge; it is currently located in Leicester and will be relocating to the refurbished Gleeson Building, University of Cambridge, in 2020. The successful candidate for this studentship will undertake the first part of their PhD within the Department of Genetics in Cambridge. The Unit is equipped with state-of-the-art facilities and provides a supportive learning environment designed to meet the scientific and transferable skills required for an internationally competitive career.

Students will be registered with the University of Cambridge for the duration of the studentship. Successful applicants will enjoy membership of a University of Cambridge College from commencement of their PhD in October 2019.

Project Outline

Drugs and other xenobiotics often pass through the digestive tract. Yet, how these compounds influence the gut microbiome and in turn get modified into potentially toxic products is known only in a few cases. Recent findings in this area have clearly brought forward the fundamental role of the microbiome in determining drug efficacy, mode of action and side effects. The goal of this PhD project will be to use machine-learning and first principle modelling (e.g. metabolic flux analysis) to build novel tools for predicting microbiome-drug interactions. Towards this, large-scale datasets of bacteria-drug interactions generated in our and collaborator laboratories will provide an excellent starting point. The project will be carried out in close collaboration with experimental researchers in the group and thus additional data will be generated to feed in to multiple rounds of model improvement. We are looking for candidates with in-depth knowledge and hands-on experience in at least one of the following areas: machine learning, metabolic modelling, metabolomics data analysis. Willingness to work in an interdisciplinary team environment is a must.

There are a variety of training modules and courses which students are encouraged to attend. In addition, students follow the Toxicology Unit’s weekly external and internal seminar programs and are included in the postdoc/student forums which take place each month and offer excellent opportunities for collaboration and career development.

This advert will remain open until 28th June 2019 or until a suitable candidate is found.

It is recommended that you contact the supervisor prior to making your formal application: [Email Address Removed]