RILD Building Level 3
University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
I am a Research Fellow in the Centre for Biomedical Modelling and Analysis (www.exeter.ac.uk/cbma/). My research uses computational approaches to gather, integrate and analyse biological big data, including working extensively with next-generation sequencing data. A major part of my work uses 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.
I also collaborate on a number of inter-disciplinary projects within the Wellcome Trust Centre for Biomedical Modelling and Analysis. The projects include collaborations with researchers from Biosciences, the Medical School and Mathematics.
Broad research specialisms:
- Next-generation sequence analysis
- Network analysis
- Systems biology
2007 – 2011 PhD Bioinformatics, University of Manchester
2006 – 2007 MSc Bioinformatics, University of Manchester
2003 – 2006 BSc Biology, University of Bristol
I am interested in understanding how phenotype arises from complex systems. I employ a variety of computational techniques and network approaches in my research. In particular I am interested in integrating genomic, transcriptomic and proteomic data to build network models that can be used to answer important biological questions. I apply these techniques to projects in my own research programme and a range of collaborative inter-disciplinary projects in the Centre for Biomedical Modelling and Analysis.
Currently, I am focused on identifying novel disease pathways in the pathogenic yeast Candida albicans. C. albicans is a human commensal and opportunistic pathogen that causes candidiasis and, in immumocomprimised patients, infection can be life threatening. My work employs network biology techniques to integrate genomic, transcriptomic and proteomic data. These data are then interrogated to identify specific pathways. Ultimately, the identification of novel disease pathways may lead to new drug targets and new treatments.
Identifying novel disease pathways in Candida albicans
Candida albicans is a human pathogen that causes a high rate of mortality in immunocomprimised patients. Its genome is currently only partially annotated with many genes uncharacterized. I aim to employ a series of network techniques to integrate genomic, transcriptomic and proteomic data to identify novel disease pathways. These pathways will be used to identify new drug targets for medical intervention. This work benefits from collaboration with the MRC Medical Mycology Centre at the University of Aberdeen.
Investigating the impact of clozapine treatment on gene expression and methylation in a neuronal cell line
As part of my work in the Centre for Biomedical Modelling and analysis I am working to identify gene expression and methylation changes associated with the drug clozapine, a common treatment for people with schizophrenia. We are measuring expression and methylation changes in response to different treatments. The changes brought about by treatment will provide a detailed understanding of the molecular actions of this important drug. This work is in collaboration with Prof Jon Mill and Dr Marc Goodfellow.
The role of mechano-transduction in the regenerative capacity of young and ageing human skeletal muscle
As people grow older skeletal muscle gradually becomes smaller and weaker. The progressive muscle deterioration results in reduced mobility and quality of life, increased incidence of frailty-related falls and metabolic disease in ageing populations. This project uses an interdisciplinary approach of computational and experimental methods to identify novel signaling pathways regulating exercise-induced muscle regeneration. This project will provide new insight into the mechanisms underpinning adaptation to exercise and its deregulation during ageing. This project is in collaboration with Dr Tim Etheridge and Prof Michael Weedon.
Using genetics to understand the impact of sleep timing and quantity on disease risk
This project is attempting to better understand the biology of sleep and its links to disease. Too much, too little or poor quality of sleep is associated with several human diseases, in particular disorders such as obesity and type 2 diabetes. Using data from the UK biobank (70 million genetic markers from 500,000 individuals) we will identify the genetic markers influencing sleep duration and timing and test these markers for causal relationships to a range of diseases. The results of this work have applications in drug development and public health policy. This project is in collaboration with Prof Michael Weedon and Dr Andrew Wood.
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Ryan_Ames Details from cache as at 2018-10-20 05:03:23