Sen Partho, Orešič Matej
Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.
School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden.
Metabolites. 2023 Jul 18;13(7):855. doi: 10.3390/metabo13070855.
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.
组学技术的最新进展产生了大量生物数据。将这些数据整合到数学模型中对于充分发挥其潜力至关重要。基因组规模代谢模型（GEMs）为研究复杂生物系统提供了一个强大的框架。GEMs对我们理解人类新陈代谢做出了重大贡献，包括肠道微生物群与宿主新陈代谢之间的内在关系。在本综述中，我们强调了GEMs的贡献，并讨论了为确保其可重复性和提高其预测准确性而必须克服的关键挑战，特别是在精准医学的背景下。我们还探讨了机器学习在应对GEMs中的这些挑战方面的作用。组学数据与GEMs的整合有可能带来新的见解，并推进我们对人类健康和疾病分子机制的理解。
