Effects of oxidative damage on the mitochondrial membrane structure through molecular dynamics simulations
Supervisors: Iain Johnston and Markus Miettinen
Mitochondria power almost all complex life through respiration. Their membranes are essential for this process. These membranes contain a high proportion of cardiolipin. This is a molecule with extremely interesting biophysical and biochemical properties that are still not fully understood. The MSc project will use a unique combination of computing (molecular dynamics simulations) and experiment (solid-state NMR) to link the computer and the lab, and to understand the behaviour of this essential biomolecule.
Visualization of protein mutations for patient and clinician education using molecular dynamics simulations and 3D printing
Supervisors: Marc Vaudel and Markus Miettinen
This MSc project aims to help patients and clinicians to get a concrete view of what particular disease-causing mutations mean to protein structure—and thus improve their understanding of what genetic mutations are, and what they imply for treatment. To reach this goal, the project will create dynamic 3D visualizations (using molecular dynamics simulations) and tactile models (using 3D printing) of the unmutated and mutated proteins.
Towards precision medicine for cancer patient stratification
Supervisor: Anagha Joshi
On average, a drug or a treatment is effective in only about half of patients who take it. This means patients need to try several until they find one that is effective at the cost of side effects associated with every treatment. The ultimate goal of precision medicine is to provide a treatment best suited for every individual. Sequencing technologies have now made genomics data available in abundance to be used towards this goal.
In this project we will specifically focus on cancer. Most cancer patients get a particular treatment based on the cancer type and the stage, though different individuals will react differently to a treatment. It is now well established that genetic mutations cause cancer growth and spreading and importantly, these mutations are different in individual patients. The aim of this project is use genomic data allow to better stratification of cancer patients, to predict the treatment most likely to work. Specifically, the project will use machine learning approach to integrate genomic data and build a classifier for stratification of cancer patients.
Unraveling gene regulation from single cell data
Supervisor: Anagha Joshi
Multi-cellularity is achieved by precise control of gene expression during development and differentiation and aberrations of this process leads to disease. A key regulatory process in gene regulation is at the transcriptional level where epigenetic and transcriptional regulators control the spatial and temporal expression of the target genes in response to environmental, developmental, and physiological cues obtained from a signalling cascade. The rapid advances in sequencing technology has now made it feasible to study this process by understanding the genomewide patterns of diverse epigenetic and transcription factors as well as at a single cell level.
Single cell RNA sequencing is highly important, particularly in cancer as it allows exploration of heterogenous tumor sample, obstructing therapeutic targeting which leads to poor survival. Despite huge clinical relevance and potential, analysis of single cell RNA-seq data is challenging. In this project, we will develop strategies to infer gene regulatory networks using network inference approaches (both supervised and un-supervised). It will be primarily tested on the single cell datasets in the context of cancer.