Personalized Whole-Cell Kinetic Models of Metabolism for Discovery in Genomics and Pharmacodynamics

Aarash Bordbar; Douglas McCloskey; Daniel C Zielinski; Nikolaus Sonnenschein; Neema Jamshidi; Bernhard O Palsson

doi:10.1016/j.cels.2015.10.003

Personalized Whole-Cell Kinetic Models of Metabolism for Discovery in Genomics and Pharmacodynamics

Cell Syst. 2015 Oct 28;1(4):283-92. doi: 10.1016/j.cels.2015.10.003. Epub 2015 Oct 28.

Authors

Aarash Bordbar¹, Douglas McCloskey¹, Daniel C Zielinski¹, Nikolaus Sonnenschein², Neema Jamshidi³, Bernhard O Palsson⁴

Affiliations

¹ Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA.
² The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2970 Hørsholm, Denmark.
³ Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA; Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA 92093-0412, USA; Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA 90095-1721, USA. Electronic address: neema@ucsd.edu.
⁴ Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA; The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2970 Hørsholm, Denmark; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92123-5109, USA.

PMID: 27136057
DOI: 10.1016/j.cels.2015.10.003

Abstract

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.

Grants and funding

R01 GM068837/GM/NIGMS NIH HHS/United States