Enabling personalised diagnosis, treatment, and stratification through whole-body metabolic modelling of an individual’s genome, metabolome, and metagenome.

Introduction

The increasing availability of whole-genome sequences will ultimately transform medicine into personalized medicine. To realize 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

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.

Extrinsic Factors

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.

Project Overview

In AVATAR, I shall develop and apply a novel mechanistic computational modeling framework that will significantly expand cutting-edge computational models of whole-body metabolism.

In Silico Models

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.

Algorithm Development

I shall devise a novel algorithm to predict phenotypically relevant genetic variants based on a person's genome and metabolome.

Biomedical Applications

The validated algorithm and the modeling framework shall then be used for two distinct biomedical proof-of-concept studies:

1. The diagnosis and diet-based treatment of inherited metabolic diseases.
2. The metabolic pathway-based stratification of individuals with cognitive impairment.

Conclusion

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 personalizable modeling analysis framework that may ultimately provide a foundation for computer-guided diagnosis and treatment strategies.