Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Partho Sen*, Matej Orešič

*Korresponderande författare för detta arbete

Forskningsoutput: TidskriftsbidragÖversiktsartikelPeer review

1 Citeringar (Scopus)
50 Nedladdningar (Pure)

Sammanfattning

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
OriginalspråkEngelska
Artikelnummer855
Antal sidor19
TidskriftMetabolites
Volym13
Nummer7
DOI
StatusPublicerad - juli 2023
MoE-publikationstypA2 Granska artikel i en vetenskaplig tidskrift

Nyckelord

  • constraint-based modeling
  • multi-omics
  • metabolic modeling
  • metabolic reconstructions
  • human metabolic networks
  • human metabolism
  • host microbiome

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