Summary
The increasing availability of whole-genome sequences will ultimately transform medicine into personalised medicine. To realise this potential, we need to understand which of the millions of genetic variants in a person’s genome can alter a phenotype. Genome-wide association studies (GWAS) have associated many genetic variants with thousands of phenotypic traits. Metabolomics has further informed GWAS. However, these methods do generally not consider the biochemical network connecting genetic variants with the metabolic phenotype. Additionally, extrinsic factors, such as diet and the microbiome, also modulate the metabolic phenotype. A computational systems approach is required to untangle this complex interplay. In AVATAR, I shall develop and apply a novel mechanistic computational modelling framework that will significantly expand cutting-edge computational models of whole-body metabolism. The novel in silico models will mechanistically describe the network of genetic variants, genes, proteins, and biochemical reactions, as well as underlying physiological processes that are influenced by microbial and nutrient metabolism. I shall devise a novel algorithm to predict phenotypically relevant genetic variants based on a person’s genome and metabolome. The validated algorithm and the modelling framework shall then be used for two distinct biomedical proof-of-concept studies: the diagnosis and diet-based treatment of inherited metabolic diseases and the metabolic pathway-based stratification of individuals with cognitive impairment. AVATAR will enable novel insights into the genotype-phenotype-environment relationship by enabling systematic mechanism-based analyses of genetic variants, diet, and the microbiome. This ground-breaking, innovative, multidisciplinary project will influence precision medicine by providing a personalisable modelling analysis framework that may ultimately provide a foundation for computer-guided diagnosis and treatment strategies.
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| Web resources: | https://cordis.europa.eu/project/id/101125633 |
| Start date: | 01-04-2024 |
| End date: | 31-03-2029 |
| Total budget - Public funding: | 1 999 991,00 Euro - 1 999 991,00 Euro |
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Original description
The increasing availability of whole-genome sequences will ultimately transform medicine into personalised medicine. To realise this potential, we need to understand which of the millions of genetic variants in a person’s genome can alter a phenotype. Genome-wide association studies (GWAS) have associated many genetic variants with thousands of phenotypic traits. Metabolomics has further informed GWAS. However, these methods do generally not consider the biochemical network connecting genetic variants with the metabolic phenotype. Additionally, extrinsic factors, such as diet and the microbiome, also modulate the metabolic phenotype. A computational systems approach is required to untangle this complex interplay. In AVATAR, I shall develop and apply a novel mechanistic computational modelling framework that will significantly expand cutting-edge computational models of whole-body metabolism. The novel in silico models will mechanistically describe the network of genetic variants, genes, proteins, and biochemical reactions, as well as underlying physiological processes that are influenced by microbial and nutrient metabolism. I shall devise a novel algorithm to predict phenotypically relevant genetic variants based on a person’s genome and metabolome. The validated algorithm and the modelling framework shall then be used for two distinct biomedical proof-of-concept studies: the diagnosis and diet-based treatment of inherited metabolic diseases and the metabolic pathway-based stratification of individuals with cognitive impairment. AVATAR will enable novel insights into the genotype-phenotype-environment relationship by enabling systematic mechanism-based analyses of genetic variants, diet, and the microbiome. This ground-breaking, innovative, multidisciplinary project will influence precision medicine by providing a personalisable modelling analysis framework that may ultimately provide a foundation for computer-guided diagnosis and treatment strategies.Status
SIGNEDCall topic
ERC-2023-COGUpdate Date
01-11-2024
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