Hyun-Seob Song

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Hyun-Seob Song

Associate Professor Biological Systems Engineering University of Nebraska-Lincoln

Contact

Address
CHA 212
Lincoln NE 68583-0726
Phone
402-472-1413 On-campus 2-1413
Email
hsong5@unl.edu

The Song lab aims to develop advanced computational and data integration tools to predict context-dependent microbial interactions and community functions. The lab synergistically combines a suite of complementary process-based and data-driven modeling approaches, including genome-scale metabolic network modeling, thermodynamics-based population dynamics modeling, agent-based modeling, network inference, and machine learning. The lab has developed several new computational tools that enable multi-scale simulations through the integration of omics data, metabolic networks, and reactive-transport models. These new methods are applicable to diverse microbial systems, including both complex natural communities and tractable model consortia.

Education

  • Ph D, Korea Univesity, 1999
  • MS, Korea University, 1995
  • BA, Korea University, 1993

Areas of Expertise

Featured publications

Song, H.-S., Lee, N.R., Kessell, A.K., McCullough, H.C., Park, S.-Y., Zhou K., & Lee D.-Y. 2024, Kinetics-based inference of environment-dependent microbial interactions and their dynamic variation, mSystems, 9(5): e01305-23. https://doi.org/10.1128/msystems.01305-23

Song, H.-S., Stegen, J.C., Graham, E.B., Lee, J.Y., Garayburu-Caruso, V., Nelson, W.C., Chen, X., Moulton. J.D., and Scheibe, T.D. 2020. Representing Organic Matter Thermodynamics in Biogeochemical Reactions via Substrate-Explicit Modeling. Frontiers in Microbiology, 11, 531756. https://doi.org/10.3389/fmicb.2020.531756

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.

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.

Kessell, A.K., McCullough, H.C., Auchtung, J.M., Bernstein, H.C., and Song, H.S. 2020. Predictive interactome modeling for precision microbiome engineering. Current Opinion in Chemical Engineering, 30, 77-85. https://doi.org/10.1016/j.coche.2020.08.003