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Probing illness with a novel multi-omic time-course statistical platform

54 months
Approved budget:
Dr Katya Ruggiero
Health issue:
Proposal type:
Emerging Researcher First Grant
Lay summary
The impact of our research to human health will primarily be to make a new computational toolset available for biomedical researchers to elucidate co-ordinately regulated changes in gene products and metabolic processes in response to correlated temporal changes across different disease statuses, thereby gaining new practical insights into how disease changes at this integrated level. The primary aim of our research is to develop and test new statistical and computational methodologies for the integrated analysis of large complex datasets simultaneously generated from the three common 'omic' platforms - transcriptomics, proteomics and metabolomics - by using time-course data derived from appropriately matched small tissue samples. A secondary benefit from our research is that during the course of developing and testing these statistical methods we need real datasets. We have chosen, as our first target of study, to investigate how the presence of underlying obesity alters the response to a severe illness over time.