Song Research Group - Journal Articles

2024

• Song HS, Lee NR, Kessell AK, McCullough HC, Park SY, Zhou K, & Lee DY (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
• Zheng J, Scheibe TD, Boye K, Song HS (2024) Thermodynamic control on the decomposition of organic matter across different electron acceptors, Soil Biology and Biochemistry, 193, 109364. https://doi.org/10.1016/j.soilbio.2024.109364

2023

• 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

2022

• 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, 996399. https://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

2021

• Song HS, Lindemann SR, and Lee DY (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 SJ, Chu RK, Danczak RE, Garayburu-Caruso VA, Renteria L, Song HS, Toyoda J, Wells J, and Stegen JC (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 HS, and Ho YK (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 HS, Stegen JC, Graham EB, 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 SH, Fay J, Cyuzuzo CI, Jang Y, Lee N, Song HS, and Harris EN (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 HS, Stegen JC, Graham EB, Lee JY, Garayburu-Caruso V, Nelson WC, Chen X, Moulton JD, and Scheibe TD (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 AK, McCullough HC, Auchtung JM, Bernstein HC, and Song HS (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 YM, Lee YQ, Song HS, and Lee DY (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 RS, Lee JY, Chowdhury TR, Bottos EM, White RA, Kim YM, Nicora CD, Metz TO, Hofmockel KS, Jansson JK, and Song HS (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 JY, Sadler NC, Egbert RG, Anderton CR, Hofmockel KS, Jansson JK, and Song HS (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 JY, Haruta S, Kato S, Bernstein HC, Lindemann SR, Lee DY, Fredrickson JK, and Song HS (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 VA, Stegen JC, Song HS, Renteria L, Wells J, Garcia W, Resch CT, Goldman AE, Chu RK, and Toyoda J (2020). Carbon limitation leads to thermodynamic regulation of aerobic metabolism. Environmental Science & Technology Letters, 7(7): 517-524. https://doi.org/10.1021/acs.estlett.0c00258.
• Ahamed F, Singh M, Song HS, Doshi P, Ooi CW, and Ho YK (2020). On the use of sectional techniques for the solution of depolymerization population balances: Results on a discrete-continuous mesh. Advanced Powder Technology, 31(7): 2669-2679. https://doi.org/10.1016/j.apt.2020.04.032. 

2019

• Ahamed F, Song HS, Ooi CW, and Ho YK (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 HS, Lee JY, Haruta S, Nelson WC, Lee DY, Lindemann SR, Fredrickson JK, and Bernstein HC (2019). Minimal Interspecies Interaction Adjustment (MIIA): Inference of Neighbor-Dependent Interactions in Microbial Communities. Frontiers in Microbiology, 10,  1264. https://doi.org/10.3389/fmicb.2019.01264.
• Chowdhury TR, Lee JY, Bottos EM, Brislawn CJ, White RA, Bramer LM, Brown J, Zucker JD, Kim YM, Jumpponen A, Rice CW, Fansler SJ, Metz TO, McCue LA, Callister SJ, Song HS, and Jansson JK (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 XH, Chen XY, Stegen J, Hammond G, Song HS, Dai H, Graham E, and Zachara JM (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 RS, Brislawn CJ, Mobberley JM, Isern N, Chrisler WB, Markillie LM, Barney BM, Song HS, Nelson WC, and Bernstein HC (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 RS, Overall CC, Hill EA, Song HS, Charania M, Bernstein HC, McDermott JE, and Beliaev AS (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 HS (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 KR, Brislawn CJ, Hutchison J, Weitz KK, Heyman HM, Song HS, Ilhan ZE, and Hill EA (2018). Lactobacillus acidophilus disrupts collaborative multispecies bile acid metabolism. bioRxiv, 296020. https://doi.org/10.1101/296020. 

2017

• Song HS, Thomas DG, Stegen JC, Li MJ, Liu CX, Song XH, Chen XY, Fredrickson JK, Zachara JM, and Scheibe TD (2017). Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process. Frontiers in Microbiology, 8,  1866. https://doi.org/10.3389/fmicb.2017.01866.
• Bernstein HC, Brislawn C, Renslow RS, Dana K, Morton B, Lindemann SR, Song HS, Atci E, Beyenal H, Fredrickson JK, Jansson JK, and Moran JJ (2017). Trade-offs between microbiome diversity and productivity in a stratified microbial mat. ISME Journal, 11(2), 405-414. https://dx.doi.org/10.1038/ismej.2016.133.
• Song HS, Goldberg N, Mahajan A, and Ramkrishna D (2017). Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming. Bioinformatics, 33(15), 2345-2353. https://dx.doi.org/10.1093/bioinformatics/btx171. 

2016

• Lindemann SR, Bernstein HC, Song HS, Fredrickson JK, Fields MW, Shou WY, Johnson DR, and Beliaev AS (2016). Engineering microbial consortia for controllable outputs. ISME Journal, 10(9), 2077-2084. https://dx.doi.org/10.1038/ismej.2016.26.
• Henry CS, Bernstein HC, Weisenhorn P, Taylor RC, Lee JY, Zucker J, and Song HS (2016). Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction. Journal of Cellular Physiology, 231(11), 2339-2345. https://dx.doi.org/10.1002/jcp.25428.
• Renslow RS, Lindemann SR, and Song HS (2016). A Generalized Spatial Measure for Resilience of Microbial Systems. Frontiers in Microbiology, 7,  443. https://doi.org/10.3389/fmicb.2016.00443.
• Ramkrishna D, and Song HS (2016). Analysis of Bioprocesses. Dynamic Modeling is a Must. MaterialsToday:Proceedings, 3(10), 3587-3599. https://doi.org/10.1016/j.matpr.2016.10.040. 

2015

• Song HS, and Liu CX (2015). Dynamic Metabolic Modeling of Denitrifying Bacterial Growth: The Cybernetic Approach. Industrial & Engineering Chemistry Research, 54(42), 10221-10227. https://dx.doi.org/10.1021/acs.iecr.5b01615.
• Song HS, Renslow RS, Fredrickson JK, and Lindemann SR (2015). Integrating Ecological and Engineering Concepts of Resilience in Microbial Communities. Frontiers in Microbiology, 6,  1298. https://doi.org/10.3389/fmicb.2015.01298.
• Song HS, McClure RS, Bernstein HC, Overall CC, Hill EA, and Beliaev AS (2015). Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality. Life, 5(2), 1127-1140. https://doi.org/10.3390/life5021127.

2014

• Song HS, Cannon WR, Beliaev AS, and Konopka A (2014). Mathematical Modeling of Microbial Community Dynamics: A Methodological Review. Processes, 2(4), 711-752. https://doi.org/10.3390/pr2040711; [Correction: Processes, 2015, 3(3), 699-700: https://doi.org/10.3390/pr3030699].
• Song HS, Reifman J, and Wallqvist A (2014). Prediction of Metabolic Flux Distribution from Gene Expression Data Based on the Flux Minimization Principle. PLOS ONE, 9(11), e112524. https://doi.org/10.1371/journal.pone.0112524.

Before 2014

• Song HS, DeVilbiss F, and Ramkrishna D (2013). Modeling metabolic systems: the need for dynamics. Current Opinion in Chemical Engineering, 2(4), 373-382. https://dx.doi.org/10.1016/j.coche.2013.08.004.
• Song HS, Ramkrishna D, Pinchuk GE, Beliaev AS, Konopka AE, and Fredrickson JK (2013). Dynamic modeling of aerobic growth of Shewanella oneidensis. Predicting triauxic growth, flux distributions, and energy requirement for growth. Metabolic Engineering, 15, 25-33. https://dx.doi.org/10.1016/j.ymben.2012.08.004.
• Song HS, and Ramkrishna D (2013). Complex nonlinear behavior in metabolic processes: Global bifurcation analysis of Escherichia coli growth on multiple substrates. Processes, 1(3), 263-278. https://doi.org/10.3390/pr1030263.
• Kim JI, Song HS, Sunkara SR, Lali A, and Ramkrishna D (2012). Exacting predictions by cybernetic model confirmed experimentally: Steady state multiplicity in the chemostat. Biotechnology Progress, 28(5), 1160-1166. https://dx.doi.org/10.1002/btpr.1583.
• Adler P, Song HS, Kastner K, Ramkrishna D, and Kunz B (2012). Prediction of dynamic metabolic behavior of Pediococcus pentosaceus producing lactic acid from lignocellulosic sugars. Biotechnology Progress, 28(3), 623-635. https://dx.doi.org/10.1002/btpr.1521.
• Song HS, and Ramkrishna D (2012). Prediction of dynamic behavior of mutant strains from limited wild-type data. Metabolic Engineering, 14(2), 69-80. https://dx.doi.org/10.1016/j.ymben.2012.02.003.
• Song HS, Kim SJ, and Ramkrishna D (2012). Synergistic Optimal Integration of Continuous and Fed-Batch Reactors for Enhanced Productivity of Lignocellulosic Bioethanol. Industrial & Engineering Chemistry Research, 51(4), 1690-1696. https://dx.doi.org/10.1021/ie200879s.
• Ramkrishna D, and Song HS (2012). Dynamic models of metabolism: Review of the cybernetic approach. AIChE Journal, 58(4), 986-997. https://dx.doi.org/10.1002/aic.13734.
• Song HS, Morgan JA, and Ramkrishna D (2012). Towards Increasing the Productivity of Lignocellulosic Bioethanol: Rational Strategies Fueled by Modeling. Bioethanol, 173-190. DOI: 10.5772/24278.
• Geng J, Song HS, Yuan J, and Ramkrishna D (2012). On enhancing productivity of bioethanol with multiple species. Biotechnology and bioengineering, 109(6), 1508-1517. https://doi.org/10.1002/bit.24419.
• Song HS, and Ramkrishna D (2011). Cybernetic Models Based on Lumped Elementary Modes Accurately Predict Strain-Specific Metabolic Function. Biotechnology and bioengineering, 108(1), 127-140. https://dx.doi.org/10.1002/bit.22922.
• Franz A, Song HS, Ramkrishna D, and Kienle A (2011). Experimental and theoretical analysis of poly (β-hydroxybutyrate) formation and consumption in Ralstonia eutropha. Biochemical Engineering Journal, 55(1), 49-58. https://doi.org/10.1016/j.bej.2011.03.006.
• Song HS, and Ramkrishna D (2010). Issues with increasing bioethanol productivity: A model directed study. Korean Journal of Chemical Engineering, 27(2), 576-586. https://dx.doi.org/10.1007/s11814-010-0101-2.
• Song HS, and Ramkrishna D (2010). Prediction of Metabolic Function From Limited Data: Lumped Hybrid Cybernetic Modeling (L-HCM). Biotechnology and bioengineering, 106(2), 271-284. https://dx.doi.org/10.1002/bit.22692.
• Wong WC, Song HS, Lee JH, and Ramkrishna D (2010). Hybrid cybernetic model-based simulation of continuous production of lignocellulosic ethanol: Rejecting abruptly changing feed conditions. Control Engineering Practice, 18(2), 177-189. https://dx.doi.org/10.1016/j.conengprac.2009.09.002.
• Song HS, and Ramkrishna D (2009). Reduction of a Set of Elementary Modes Using Yield Analysis. Biotechnology and bioengineering, 102(2), 554-568. https://dx.doi.org/10.1002/bit.22062. 
• Song HS, Morgan JA, and Ramkrishna D (2009). Systematic Development of Hybrid Cybernetic Models: Application to Recombinant Yeast Co-Consuming Glucose and Xylose. Biotechnology and bioengineering, 103(5), 984-1002. https://doi.org/10.1002/bit.22332.
• Song HS, and Ramkrishna D (2009). When is the quasi-steady-state approximation admissible in metabolic modeling? When admissible, what models are desirable? Industrial & Engineering Chemistry Research, 48(17), 7976-7985. https://doi.org/10.1021/ie900075f.
• Ramkrishna D, and Song HS (2008). A Rationale for Monod's Biochemical Growth Kinetics. Industrial & Engineering Chemistry Research, 47(23), 9090-9098. https://dx.doi.org/10.1021/ie800905d.
• Lee JS, Shin DM, Song HS, Jung HW, and Hyun JC (2006). Existence of optimal cooling conditions in the film blowing process. Journal of Non-Newtonian Fluid Mechanics, 137(1-3), 24-30. https://dx.doi.org/10.1016/j.jnnfm.2005.12.011.
• Song HS, and Han SP (2005). A general correlation for pressure drop in a Kenics static mixer. Chemical Engineering Science, 60(21), 5696-5704. https://dx.doi.org/10.1016/j.ces.2005.04.084.
• Song HS, Ramkrishna D, Trinh S, and Wright H (2004). Operating strategies for Fischer-Tropsch reactors: A model-directed study. Korean Journal of Chemical Engineering, 21(2), 308-317. https://doi.org/10.1007/BF02705414.
• Hyun JC, Kim H, Lee JS, Song HS, and Jung HW (2004). Transient solutions of the dynamics in film blowing processes. Journal of Non-Newtonian Fluid Mechanics, 121(2-3), 157-162. https://dx.doi.org/10.1016/j.jnnfm.2004.06.004.
• Choe J, Kwon Y, Kim Y, Song HS, and Song KH (2003). Micromixer as a continuous flow reactor for the synthesis of a pharmaceutical intermediate. Korean Journal of Chemical Engineering, 20(2), 268-272. https://doi.org/10.1007/BF02697239.
• Song HS, Ramkrishna D, Trinh S, Espinoza RL, and Wright H (2003). Multiplicity and sensitivity analysis of Fischer-Tropsch bubble column slurry reactors: plug-flow gas and well-mixed slurry model. Chemical Engineering Science, 58(12), 2759-2766. https://dx.doi.org/10.1016/S0009-2509(03)00125-8.
• Song HS, Ramkrishna D, Trinh S, and Wright H (2003). Diagnostic nonlinear analysis of Fischer-Tropsch synthesis in stirred-tank slurry reactors. AIChE Journal, 49(7), 1803-1820.  https://doi.org/10.1002/aic.690490717.
• Song HS, Lee JS, and Hyun JC (2002). A kinetic model for polystyrene (PS) pyrolysis reaction. Korean Journal of Chemical Engineering, 19(6), 949-953. https://doi.org/10.1007/BF02707216.
• Lee JS, Jung HW, Song HS, Lee KY, and Hyun JC (2001). Kinematic waves and draw resonance in film casting process. Journal of Non-Newtonian Fluid Mechanics, 101(1-3), 43-54. https://doi.org/10.1016/S0377-0257(01)00155-0.
• Song HS, and Hyun JC (2001). Practical optimization methods for finding best recycling pathways of plastic materials. Clean Technology, 7(2), 99-107. https://www.cheric.org/research/tech/periodicals/view.php?seq=12826.
• Jung HW, Song HS, and Hyun JC (2000). Draw resonance and kinematic waves in viscoelastic isothermal spinning. AIChE Journal, 46(10), 2106-2111. https://grtrkr.korea.ac.kr/jchyun/papers/AIChE46-10-2106.pdf.
• Jung HW, Song HS, and Hyun JC (1999). Analysis of the stabilizing effect of spinline cooling in melt spinning. Journal of Non-Newtonian Fluid Mechanics, 87(2-3), 165-174. https://doi.org/10.1016/S0377-0257(99)00061-0.
• Song HS, and Hyun JC (1999). An optimization study on the pyrolysis of polystyrene in a batch reactor. Korean Journal of Chemical Engineering, 16(3), 316-324. https://doi.org/10.1007/BF02707119.
• Song HS, Moon KS, and Hyun JC (1999). A life-cycle assessment (LCA) study on the various recycle routes of PET bottles. Korean Journal of Chemical Engineering, 16(2), 202-207. https://doi.org/10.1007/BF02706837.
• Song HS, and Hyun JC (1999). A study on the comparison of the various waste management scenarios for PET bottles using the life-cycle assessment (LCA) methodology. Resources Conservation and Recycling, 27(3), 267-284. https://doi.org/10.1016/S0921-3449(99)00022-1.
• Song HS, Park YD, and Hyun JC (1996). Optimization for the minimum reaction time of PET esterification. Korean Journal of Chemical Engineering, 13(4), 369-378. https://doi.org/10.1007/BF02705964.