University of Dundee

Computational Biology

Short Code: 

“Adventures in Bioinformatics: from minimal genomes to virus pathogenicity and myosin evolution”

My research group focusses on the development of bioinformatics methods and their application to open questions in biology. These falls into two main areas i) the functional annotation of proteins and ii) investigating how genetic variation alters these functions. In this talk I will initially discuss our recent work on functional annotation of the minimal bacterial genome.

Commendations for SLS at Stephen Fry Public Engagement Awards

As part of the University of Dundee’s Celebrating our Public Engagement event, the University crowned the latest winners of the Stephen Fry Awards for Excellence in Public Engagement.

The awards celebrate the people and projects that engage with wider audiences, and the benefits they bring to society. The winners across the three categories were announced by former Rector Stephen Fry:

“Tissue time machine” target for Dundee researcher with Wellcome Leap

A University of Dundee scientist has accepted a role to lead a $55 million initiative aiming to create a “tissue time machine” to fight disease.

Jason Swedlow, Professor of Quantitative Cell Biology at Dundee’s School of Life Sciences, has been seconded to the position of Programme Director of Delta Tissue, a global effort to predict the future state of human tissue and the efficacy of available treatments for individuals diagnosed with some of the world’s deadliest diseases.

EASTBIO: *Closing date 10.04.21*: Machine Learning with Molecular Dynamics to uncover allosteric mechanisms of capping enzymes

Molecular dynamics (MD) has matured into a powerful tool for studying dynamics and function of proteins with atomistic and femtosecond resolutions. Typical MD simulations (~microsecond timescale) produce large trajectories of high-dimensional data. A great challenge is to extract useful information about important protein properties from such large (terra- to petabytes) datasets.