Computational biology approaches for novel antifungal target discovery
Ryan Ames
EPSRC/BBSRC Innovation Fellow
Living Systems Institute
University of Exeter

Research Themes

My research focuses on using network approaches to study how phenotype arises from complex systems. Currently, I am using these approaches to study the evolution of pathogenicity in yeast in order to identify novel disease-associated pathways. These pathways will be the focus of efforts to identify new targets for antifungal treatments.

Computational Biology

My research uses computational approaches to gather, integrate and analyse biological big data, including working extensively with next-generation sequencing data. I make use of existing computational biology techniques and develop novel methods where needed.

Protecting global food supplies

Yeast cause infection in many crops including rice and wheat leading to the loss of millions of tonnes of crops each year. This causes significant financial losses but with our growing population also represents a significant threat to global food security. I am currently developing tools to identify targets for new fungicide development.

Ensuring human health

Many yeast species are important human pathogens that cause recurrent treatable infections and life-threatening illness. My research includes studying the evolution of pathogenicity and the identification of disease-causing genes that could be targeted for treatment of infections.

Project Areas

I am developing and applying computational tools to understand pathogenic yeast in order to develop new treatments.

Candida albicans

Identifying disease pathways in the pathogenic yeast Candida albicans

Magnaporthe oryzae

New approaches to identify fungicide targets in the crop pathogen Magnaporthe oryzae

Evolution of pathogenicity

Inferring the evolutionary events that led to pathogenicity in yeast


Ryan Ames

EPSRC/BBSRC Innovation Fellow

Living Systems Institute

University of Exeter

Stocker Road