Hyun-Seob Song

Contact Information:

East Campus (Lincoln)

212 L.W. Chase Hall

402-472-1036

hsong5@unl.edu

Personal Links:

The Song Lab

Professional Memberships:

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

Certifications

• Ph.D.

Appointment

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:

Inventions/Patents

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)

2022

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(4), 689. https://doi.org/10.3390/antiox11040689
Dwivedi, D., A. L. D. Santos, M. A. Barnard, T. M. Crimmins, A. Malhotra, K. A. Rod, K. S. Aho, S. M. Bell, B. Bomfim, F. Q. Brearley, H. Cadillo-Quiroz, J. Chen, C. M. Gough, E. B. Graham, C. R. Hakkenberg, L. Haygood, G. Koren, E. A. Lilleskov, L. K. Meredith, S. Naeher, Z. L. Nickerson, O. Pourret, H. S. Song, M. Stahl, N. Taş, R. Vargas, and S. Weintraub-Leff. (2022). Biogeosciences Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science, Earth and Space Science, 9(3), e2021EA002119. https://doi.org/10.1029/2021EA002119

2021

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. 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

2020

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 Letters. https://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 Technology. https://doi.org/10.1016/j.apt.2020.04.032.

2019

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, 10. https://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.

2018

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, 8. https://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.

Books

Ramkrishna, D., & Song, H.-S. (2018). Cybernetic Modeling for Bioreaction Engineering. Cambridge University Press. https://doi.org/10.1017/9780511731969.
Song H.-S (ed.). (2018). Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics. MDPI. https://doi.org/10.3390/books978-3-03842-976-0.

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.