Hyun-Seob Song, Ph.D.

Hyun-Seob Song

Contact Information:

209 L.W. Chase Hall
East Campus (Lincoln)
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Associate Professor and Computational Biologist

Academic Degrees

  • Ph.D., Chemical Engineering, Korea University
  • M.S., Chemical Engineering, Korea University
  • B.S., Chemical Engineering, Korea University


  • 80% Research
  • 20% Teaching

Areas of Research and Professional Interest

  • Microbiome modeling and engineering (soil microbiomes, human microbiota, and synthetic consortia)
  • Metabolic network modeling
  • Biological network inference
  • Agent-based modeling
  • AI-based modeling: cybernetic approach and machine learning

Research Profiles:


  • Kim IS, Song HS, Han SP, Lee JH, Cha CH (2007) Apparatus for Mixing Viscous Material. Pub. No.: WO/2007/081141, International Application No.: PCT/KR2007/000151

  • Hyun JC, Jung HW, Song HS, Kim H, Lee JS, Shin DM (2005) A Method for Solving Transient Solution and Dynamics in Film Blowing Process. International Application No.: PCT/KR2005/000431

Courses Taught

  • BSEN 951 Advanced Mathematical Modeling in Biological Engineering: Data-Driven Modeling
  • BSEN 892 Special Topics: Microbial Community Modeling

About Hyun-Seob Song

Dr. Song has a joint appointment with Biological Systems Engineering (BSE) and Food Science and Technology (FDST) at the University of Nebraska-Lincoln (UNL). He is also a member of Nebraska Food for Health Center (NFHC). After finishing his PhD under the guidance of Dr. Jae Chun Hyun in Chemical Engineering at Korea University (Seoul, Korea), he joined the Ramkrishna group in Chemical Engineering at Purdue University (West Lafayette, IN) to work as a postdoctoral researcher (and later research scientist) in the area of computational systems biology and metabolic modeling. Before joining UNL, he worked as Senior Scientist in Computational Biology and Bioinformatics group at Pacific Northwest National Laboratory (PNNL) (Richland, WA). Besides biological modeling, he also holds expertise on computational fluid dynamics and scale-up and mixing through industrial experience at LG Chem / Research Park (Daejeon, Korea). He edited a special issue book on microbial community modeling with Processes; co-authored a book with Prof. Ramkrishna at Purdue University, entitled “Cybernetic Modeling for Bioreaction Engineering” (2018, Cambridge University Press). Under contract with CRC Press, he is currently preparing a new book on modeling context-dependent microbial interactions.

Selected Publications

Recent Journal Publications

(Click here for the full list of publications)


  • Ro, S.-H., Fay, J., Cyuzuzo, C. I., Jang, Y., Lee, N., Song, H.-S., and Harris, E. N. (2020). SESTRINs: Emerging Dynamic Stress-Sensors in Metabolic and Environmental Health. Frontiers in Cell and Developmental Biology, 8, 603421. https://doi.org/10.3389/fcell.2020.603421.
  • Song, H.-S., Stegen, J. C., Graham, E. B., Lee, J.-Y., Garayburu-Caruso, V., Nelson, W. C., Chen, X., Moulton, J. D., & 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.

  • Kessell, A. K., McCullough, H. C., Auchtung, J. M., Bernstein, H. C., & 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.
  • Choi, Y.-M., Lee, Y. Q., Song, H.-S., & Lee, D.-Y. (2020). Genome scale metabolic models and analysis for evaluating probiotic potentials. Biochemical Society Transactions, 48(4), 1309-1321. https://doi.org/10.1042/bst20190668.
  • McClure, R. S., Lee, J.-Y., Chowdhury, T. R., Bottos, E. M., White, R. A., Kim, Y.-M., Nicora, C. D., Metz, T. O., Hofmockel, K. S., Jansson, J. K., & Song, H.-S. (2020). Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations. Scientific Reports, 10(1), 1-9. https://doi.org/10.1038/s41598-020-67878-7.
  • Lee, J.-Y., Sadler, N. C., Egbert, R. G., Anderton, C. R., Hofmockel, K. S., Jansson, J. K., & Song, H.-S. (2020). Deep Learning Predicts Microbial Interactions from Self-organized Spatiotemporal Patterns. Computational and Structural Biotechnology Journal, 18, 1259-1269.  https://doi.org/10.1016/j.csbj.2020.05.023.
  • Lee, J.-Y., Haruta, S., Kato, S., Bernstein, H. C., Lindemann, S. R., Lee, D.-Y., Fredrickson, J. K., & Song, H.-S. (2020). Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data. Frontiers in Microbiology, 10, 3049. https://doi.org/10.3389/fmicb.2019.03049.
  • Garayburu-Caruso, V. A., Stegen, J. C., Song, H.-S., Renteria, L., Wells, J., Garcia, W., Resch, C. T., Goldman, A. E., Chu, R. K., & Toyoda, J. (2020). Carbon limitation leads to thermodynamic regulation of aerobic metabolism. Environmental Science & Technology Lettershttps://doi.org/10.1021/acs.estlett.0c00258.
  • Ahamed, F., Singh, M., Song, H.-S., Doshi, P., Ooi, C. W., & Ho, Y. K. (2020). On the use of sectional techniques for the solution of depolymerization population balances: Results on a discrete-continuous mesh. Advanced Powder Technologyhttps://doi.org/10.1016/j.apt.2020.04.032


  • Ahamed, F., Song, H.-S., Ooi, C. W., & Ho, Y. K. (2019). Modelling heterogeneity in cellulose properties predicts the slowdown phenomenon during enzymatic hydrolysis. Chemical Engineering Science, 206, 118-133. https://dx.doi.org/10.1016/j.ces.2019.05.028.
  • Song, H.-S., Lee, J. Y., Haruta, S., Nelson, W. C., Lee, D. Y., Lindemann, S. R., Fredrickson, J. K., & Bernstein, H. C. (2019). Minimal Interspecies Interaction Adjustment (MIIA): Inference of Neighbor-Dependent Interactions in Microbial Communities. Frontiers in Microbiology, 10https://doi.org/10.3389/fmicb.2019.01264.
  • Chowdhury, T. R., Lee, J. Y., Bottos, E. M., Brislawn, C. J., White, R. A., Bramer, L. M., Brown, J., Zucker, J. D., Kim, Y. M., Jumpponen, A., Rice, C. W., Fansler, S. J., Metz, T. O., McCue, L. A., Callister, S. J., Song, H.-S., & Jansson, J. K. (2019). Metaphenomic Responses of a Native Prairie Soil Microbiome to Moisture Perturbations. mSystems, 4(4). https://doi.org/10.1128/mSystems.00061-19


  • Song, X. H., Chen, X. Y., Stegen, J., Hammond, G., Song, H.-S., Dai, H., Graham, E., & Zachara, J. M. (2018). Drought Conditions Maximize the Impact of High-Frequency Flow Variations on Thermal Regimes and Biogeochemical Function in the Hyporheic Zone. Water Resources Research, 54(10), 7361-7382. https://dx.doi.org/10.1029/2018wr022586.
  • Khan, N., Maezato, Y., McClure, R. S., Brislawn, C. J., Mobberley, J. M., Isern, N., Chrisler, W. B., Markillie, L. M., Barney, B. M., Song, H.-S., Nelson, W. C., & Bernstein, H. C. (2018). Phenotypic responses to interspecies competition and commensalism in a naturally-derived microbial co-culture. Scientific Reports, 8https://doi.org/10.1038/s41598-017-18630-1.
  • McClure, R. S., Overall, C. C., Hill, E. A., Song, H.-S., Charania, M., Bernstein, H. C., McDermott, J. E., & Beliaev, A. S. (2018). Species-specific transcriptomic network inference of interspecies interactions. Isme Journal, 12(8), 2011-2023. https://dx.doi.org/10.1038/s41396-018-0145-6.
  • Song, H.-S. (2018). Design Principles of Microbial Communities: From Understanding to Engineering. Current Genomics, 19(8), 699-700. https://dx.doi.org/10.2174/138920291908181005100741.
  • Dautel, S., Khan, N., Brandvold, K. R., Brislawn, C. J., Hutchison, J., Weitz, K. K., Heyman, H. M., Song, H.-S., Ilhan, Z. E., & Hill, E. A. (2018). Lactobacillus acidophilus disrupts collaborative multispecies bile acid metabolism. bioRxiv, 296020. https://doi.org/10.1101/296020


    Book Chapters

      • Song, H.-S., Nelson, W. C., Lee, J.-Y., Taylor, R. C., Henry, C. S., Beliaev, A. S., Ramkrishna, D., & Bernstein, H. C. (2018). Metabolic network modeling for computer-aided design of microbial interactions. Emerg. Areas Bioeng, 2, 793-801. https://doi.org/10.1002/9783527803293.ch45

      • Song, H. S., Morgan, J. A., & Ramkrishna, D. (2012). Towards Increasing the Productivity of Lignocellulosic Bioethanol: Rational Strategies Fueled by Modeling. Bioethanol, 173-190. DOI: 10.5772/24278.