Metabolite profiling, as demonstrated by the host laboratory, has the power to discriminate single gene alterations and to identify metabolite biomarkers that potentially can function as surrogates for the genetic event. In addition they have shown that pattern recognition within NMR-derived metabolome profiles from gene deletion mutants can be used as a functional genomics tool to confirm the identity of modules or co-sets predicted by genome-scale metabolic models, demonstrating how metabolomic data can be used to understand the structure of metabolic networks, relating the metabolome to the genome. The aim of this project is to define the metabolic profiles associated with the expression of specific genes in tumour cells. This will allow us to define the metabolic network structure in tumour cells (and how it is related to expression of different oncogenes and tumour suppressor genes), and to identify metabolites that are correlated with common genetic changes in cancer cells. This should allow us to design new molecular imaging methods for detecting tumours and their response to treatment. Moreover, the proposed work will also allow us to understand the effects of specific drugs, by allowing us to monitor the modulation of the activity of specific drug targets and also by identifying off-target drug effects.
Field of science
- /natural sciences/computer and information sciences/artificial intelligence/pattern recognition
Call for proposal
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