Forward Individualized Medicine from Personal Genomes to Interactomes

Xiang Zhang*, Jan A. Kuivenhoven, Albert K. Groen

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

11 Citations (Scopus)
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When considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease treatment and thereby improve health benefits for every patient. The success of individualized medicine relies on a precise understanding of the genotype-phenotype relationship. Although omics technologies advance rapidly, there are several challenges that need to be overcome: Next generation sequencing can efficiently decipher genomic sequences, epigenetic changes, and transcriptomic variation in patients, but it does not automatically indicate how or whether the identified variation will cause pathological changes. This is likely due to the inability to account for (1) the consequences of gene-gene and gene-environment interactions, and (2) (post)transcriptional as well as (post)translational processes that eventually determine the concentration of key metabolites. The technologies to accurately measure changes in these latter layers are still under development, and such measurements in humans are also mainly restricted to blood and circulating cells. Despite these challenges, it is already possible to track dynamic changes in the human interactome in healthy and diseased states by using the integration of multi-omics data. In this review, we evaluate the potential value of current major bioinformatics and systems biology-based approaches, including genome wide association studies, epigenetics, gene regulatory and protein-protein interaction networks, and genome-scale metabolic modeling. Moreover, we address the question whether integrative analysis of personal multi-omics data will help understanding of personal genotype-phenotype relationships.

Original languageEnglish
Article number364
Number of pages11
JournalFrontiers in Physiology
Publication statusPublished - 9-Dec-2015


  • personalized medicine
  • interactome
  • gene regulatory networks (GRN)
  • protein-protein interaction (PPI)
  • genome-scale metabolic models
  • integrative genomics
  • network medicine

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