Journal article
Personalized Whole-Cell Kinetic Models of Metabolism for Discovery in Genomics and Pharmacodynamics
University of California at San Diego1
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark2
iLoop, Translational Management, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark3
Big Data 2 Knowledge, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark4
Network Reconstruction in Silico Biology, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark5
Understanding individual variation is fundamental to personalized medicine. Yet interpreting complex phenotype data, such as multi-compartment metabolomic profiles, in the context of genotype data for an individual is complicated by interactions within and between cells and remains an unresolved challenge.
Here, we constructed multi-omic, data-driven, personalized whole-cell kinetic models of erythrocyte metabolism for 24 healthy individuals based on fasting-state plasma and erythrocyte metabolomics and whole-genome genotyping. We show that personalized kinetic rate constants, rather than metabolite levels, better represent the genotype.
Additionally, changes in erythrocyte dynamics between individuals occur on timescales of circulation, suggesting detected differences play a role in physiology. Finally, we use the models to identify individuals at risk for a drug side effect (ribavirin-induced anemia) and how genetic variation (inosine triphosphatase deficiency) may protect against this side effect.
This study demonstrates the feasibility of personalized kinetic models, and we anticipate their use will accelerate discoveries in characterizing individual metabolic variation.
Language: | English |
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Year: | 2015 |
Pages: | 283-292 |
ISSN: | 24054720 and 24054712 |
Types: | Journal article |
DOI: | 10.1016/j.cels.2015.10.003 |
ORCIDs: | Sonnenschein, Nikolaus and Palsson, Bernhard |