Hyun-Seob Song

Contact Information:

East Campus (Lincoln)
212 L.W. Chase Hall
Twitter icon    LinkedIn icon

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


  • Ph.D.


  • 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 (Spring 2020, 2022, 2024)
  • BSEN 892 Special Topics: Microbial Community Modeling (Spring 2021, 2023)
  • BSEN 445/845 Bioprocess Engineering (Fall 2023)

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)


  • Graham EB, Song HS, Grieger S, Garayburu-Caruso VA, Stegen JC, Bladon KD, and Myers-Pigg AN (2023). Potential bioavailability of representative pyrogenic organic matter compounds in comparison to natural dissolved organic matter pools, Biogeosciences, 20, 3449–3457.  https://doi.org/10.5194/bg-20-3449-2023
  • Ahamed F, You Y, Burgin A, Stegen JC, Scheibe TD, and Song HS (2023). Exploring the determinants of organic matter bioavailability through substrate-explicit thermodynamic modeling, Frontiers in Water, 5, 1169701. https://doi.org/10.3389/frwa.2023.1169701 

  • Jung H, Song HS, and Meile C (2023). CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics, EGUsphere, 16(6), 1683–1696. https://doi.org/10.5194/gmd-16-1683-2023


  • Zhang S, Ahamed F, and Song HS (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, 996399https://doi.org/10.3389/frfst.2022.996399.
  • McClure R, Farris Y, Danczak R,  Nelson W,  Song HS, Kessell A, Lee JY, Couvillion S, Henry C, Jansson JK, and Hofmockel KS (2022). Interaction Networks Are Driven by Community-Responsive Phenotypes in a Chitin-Degrading Consortium of Soil Microbes, mSystems, e00372-22. https://doi.org/10.1128/msystems.00372-22.
  • Phalak P, Bernstein HC, Lindemann SR, Renslow RS, Thomas DG, Henson MA, and Song HS (2022). Spatiotemporal Metabolic Network Models Reveal Complex Autotroph-Heterotroph Biofilm Interactions Governed by Photon Incidences, IFAC-PapersOnLine, 55(7), 112-118. https://doi.org/10.1016/j.ifacol.2022.07.430.
  • Ro SH, Bae J, Jang Y, Myers JF, Chung S, Yu J, Natarajan SK, Franco R, and Song HS (2022). Arsenic Toxicity on Metabolism and Autophagy in Adipose and Muscle Tissues, Antioxidants, 11(4), 689. https://doi.org/10.3390/antiox11040689

  • Dwivedi D, Santos ALD, Barnard MA, Crimmins TM, Malhotra A, Rod KA, Aho KS, Bell SM, Bomfim B, Brearley FQ, Cadillo-Quiroz H, Chen J, Gough CM, Graham EB, Hakkenberg CR, Haygood L, Koren G, Lilleskov EA, Meredith LK, Naeher S, Nickerson ZL, Pourret O, Song HS, Stahl M, Taş N, Vargas R, and Weintraub-Leff S (2022). Biogeosciences Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science, Earth and Space Science, 9(3), e2021EA002119. https://doi.org/10.1029/2021EA002119


  • 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 Microbiology12, 3731. https://doi.org/10.3389/fmicb.2021.782871

  • Sengupta, A., Fansler, S. J., Chu, R. K., Danczak, R. E., Garayburu-Caruso, V. A., Renteria, L., Song, H.-S., Toyoda, J., Wells, J., & Stegen, J. C. (2021). Disturbance triggers non-linear microbe–environment feedbacks, Biogeosciences, 18(16), 4773–4789. https://doi.org/10.5194/bg-18-4773-2021

  • Ahamed, F., Song, H.-S., and Ho Y.K. (2021). Modeling Coordinated Enzymatic Control of Saccharification and Fermentation by Clostridium thermocellum During Consolidated Bioprocessing of Cellulose, Biotechnology and Bioengineering, 118, 1898-1912. https://doi.org/10.1002/bit.27705   
  • Song, H.-S., Stegen, J. C., Graham, E. B., and Scheibe, T. (2021). Historical Contingency in Microbial Resilience to Hydrologic Perturbations. Frontiers in Water, 3, 590378. https://doi.org/10.3389/frwa.2021.590378


  • 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


    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.