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Hyun-Seob Song

hsong5@unl.edu

402-472-1036

The Song lab's research is centered on the development of mathematical/computational models and the creation of advanced data-model integration tools to address key challenges in biological, life, and environmental sciences. Specific aims include: (1) predicting the metabolic behaviors of microbial and human cells and (2) unraveling interspecies interactions and microbial community dynamics within environmental and human microbiomes. Major research areas the group focuses on include soil microbiome modeling, biogeochemical modeling, human microbiome modeling, human cell modeling, and computational drug discovery. In particular, a primary goal in human microbiome research is to construct omics-integrated microbial community models that enable a molecular-level understanding of the relationships among perturbations, microbial community composition, functional activities within microbial communities—especially within the gut microbiota—and their implications for human health and disease. To this end, the group employs a comprehensive suite of complementary in silico tools for synergistic integration, including data-driven computational analysis techniques such as network inference and machine learning, as well as physics-based microbial modeling approaches like metabolic network reconstruction, agent-based modeling, and cybernetic modeling.

Featured publications

  • Graham, E.B., Song, H.-S., Grieger, S., Garayburu-Caruso, V.A., Stegen, J.C., Bladon, K.D., Myers-Pigg, A.N. 2023. Potential bioavailability of representative pyrogenic organic matter compounds in comparison to natural dissolved organic matter pools, Biogeosciences 20:3449–3457.
  • Ahamed, F., You, Y., Burgin, A., Stegen, J.C., Scheibe, T.D., Song, H.-S. 2023. Exploring the determinants of organic matter bioavailability through substrate-explicit thermodynamic modeling, Frontiers in Water 5: 1169701.
  • Zhang, S., Ahamed, F., Song, H.-S. 2022. Knowledge-informed data-driven modeling for sparse identification of governing equations for microbial inactivation processes in food, Frontiers in Food Science and Technology 2:996399.
  • Ro, S.H., Bae, J., Jang, Y., Myers, J.F., Chung, S., Yu, J., Natarajan, S.K., Franco, R., Song, H.-S. 2022. Arsenic Toxicity on Metabolism and Autophagy in Adipose and Muscle Tissues, Antioxidants 11:689.
  • Song H.-S., Lindemann, S.R., Lee, D.Y. 2021. Editorial: Predictive Modeling of Human Microbiota and Their Role in Health and Disease, Frontiers in Microbiology 12:3731.