Portrait of Dr Mark Wass

Dr Mark Wass

Reader in Computational Biology

About

Mark obtained a BA in Natural Sciences from Cambridge University in 2000 followed by an MSc in Computing at Imperial College London (2001). After a few years working in Industry as an IT consultant Mark studied for a PhD with Prof Mike Sternberg at Imperial (2004-2008) and continued onto a post-doctoral position in the group until 2011. In 2011 Mark was awarded a FEBS Long Term Fellowship to work in the group of Alfonso Valencia at the CNIO (Spanish National Cancer Research Centre, Madrid, Spain). Mark joined the School of Biosciences at Kent in October 2012 as a lecturer in Computational Biology and now runs a joint wet/ dry laboratory research group together with Martin Michaelis

orcid.org/0000-0001-5428-6479

Google Scholar: http://tinyurl.com/lsesv4h

Research interests

Mark’s research focusses in two main areas. The first is the development of novel computational methods for the analysis of large scale biological data, particularly methods for the prediction of protein structure, function and interactions. The second area is the application of such methods to address important biological problems. These cover the association of genetic variation with human disease, investigating mechanisms and biomarkers of acquired resistance to anti-cancer drugs and also identifying determinants of pathogenicity in viruses.
In the area of acquired resistance in cancer, Mark’s research focusses on using the Resistant Cancer Cell Line Collection (RCCL), a unique collection of >1,300 cancer cell lines with acquired resistance to anti-cancer drugs, which provides a model to study how tumours become resistant to anti-cancer drugs during treatment. In the area of computational virology Mark’s research initially focussed on investigating determinants of Ebola virus pathogenicity, in 2016, Mark won the International Society of Computational Biology ‘Fight against Ebola award’. Mark’s continues research on Ebola virus and has expanded this area of research to other other viruses including Marburg virus and Zika virus.

Teaching

Undergraduate:

  • BI638 – Bioinformatics and Genomics
  • BI639 – Frontiers in Oncology 
  • BI620 – Frontiers in Virology 
  • BI629 – Proteins

Supervision

MSc-R projects available for 2020/21

Investigation of drug-adapted cancer cell lines

Jointly supervised with Martin Michaelis

We host the Resistant Cancer Cell Line (RCCL) collection, the worldwide largest collection of drug-adapted cancer cell lines and models of acquired drug resistance in cancer at Kent. In this project, drug-adapted cancer cell lines will be characterised and investigated to gain novel insights into the processes underlying resistance formation and to identify novel therapy candidates (including biomarkers)

Using cancer genomics to identify biomarkers of cancer resistance

Jointly supervised with Martin Michaelis

At Kent we host the Resistant Cancer Cell Line (RCCL) collection, the largest collection of cancer cell lines worldwide that have been adapted to anti-cancer drugs. These cells represent a model of drug resistance in tumours. This project will analyse exome sequencing data of a set of cell lines to identify mechanisms of resistance and biomarkers.

Investigating the determinants of SARS Coronavirus-2 pathogenicity

Jointly supervised with Martin Michaelis

Severe Acute Respiratory Coronavirus-2 (SARS-CoV-2) is currently causing a pandemic with much of the world in a lockdown state to limit the spread of the virus and number of cases and deaths that it causes. Due to the latest genome sequencing technologies there are now many thousands of SARS-CoV-2 genome sequences obtained from those infected. These can be analysed to advance our understanding of the genetic and molecular features that determine the properties of the virus. This project will focus on using computational approaches to compare the thousands of SARS-CoV-2 genome sequences with those of SARS-CoV, the related virus that caused the 2002-2003 SARS Coronavirus outbreak. While these two viruses are closely related there are important differences in the disease that they cause. For example SARS-CoV-2 has a much lower death rate and appears to be more easily transmitted. We have already begun research in this area (see our preprint here: https://www.biorxiv.org/content/10.1101/2020.04.03.024257v1) and this project will expand on this work.

Investigating determinants of virus pathogenicity

Jointly supervised with Martin Michaelis

Our research has recently compared different species of Ebolaviruses to identify parts of their proteins that determine if they are pathogenic. This project will apply these computational approaches to different types of viruses (e.g. Zika virus, west Nile, human papillomavirus) to identify determinants of virus pathogenicity and gain insight into what make some viruses highly virulent while others are harmless.

Predicting protein function

Advances in sequencing technologies have identified millions of protein sequences but the function of many of these proteins remains unknown. This project will focus on developing a computational method to predict protein function.

Evolution of the muscle sarcomere. A bioinformatics approach to the interaction between myosin and myosin binding protein-C

Joint supervision with Prof M Geeves

Following on from a study of how muscle-type myosins have adapted, over evolutionary timescales, for different types of muscle contraction, we will explore the co-evolution of myosin and the myosin binding proteins C. MyBP-C is well known to carry mutations linked to inherited heart disease.

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