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

Sen, Partho,1983-Örebro universitet,Institutionen för medicinska vetenskaper,Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland (författare)

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

This work was supported by the Research Council of Finland (grant no. 333981 to M.O.) and by the “Inflammation in human early life: targeting impacts on life-course health” (INITIALISE) consortium funded by the Horizon Europe Program of the European Union under Grant Agreement 101094099.
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.

Oresic, Matej,1967-Örebro universitet,Institutionen för medicinska vetenskaper,Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland(Swepub:oru)moc (författare)
Örebro universitetInstitutionen för medicinska vetenskaper (creator_code:org_t)

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