Education

• B.S. Chemical Engineering, Korea University
• M.S. Process Systems Engineering, Korea University
• Ph.D. Reaction Engineering, Korea University
• Post-doc, Nonlinear Analysis / Metabolic Modeling, Purdue University

Research

Dr. Song’s research is focused on metabolic and microbiome modeling for predicting 1) interspecies interactions and complex system dynamics in environmental communities, human microbiota, and synthetic consortia, and 2) their compositional and functional shifts in response to environmental perturbations and membership changes. He employs a suite of complementary computational and modeling tools for integration, including:

• Metabolic network reconstruction, metabolic pathway analysis, flux balance analysis, and other constraint-based approaches
• Data-driven network inference: similarity-, regression-, and rule-based approaches
• Agent-based modeling for simulating spatiotemporal dynamics in microbial assemblies
• The cybernetic approach to model metabolic regulation and cell-to-cell communication

Publications

• Song HS, Lee JY, Haruta S, Nelson WC, Lee DY, Lindemann SR, Fredrickson JK, Bernstein HC (2019), Minimal Interspecies Interaction Adjustment (MIIA): inference of member-dependent interactions in microbial communities, Frontiers in Microbiology, 10: Article number 1264
• McClure RS, Overall CC, Hill EA, Song HS, Charania M, Bernstein HC, McDermott JE, Beliaev AS (2018) Species-specific transcriptomic network inference of interspecies interactions, ISME J. 12: 2011–2023
• Song HS, Goldberg N, Mahajan A, 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
• Henry CS, Bernstein HC, Weisenhorn P, Taylor RC, Lee JY, Zucker J, Song HS (2016). Microbial Community Metabolic Modeling: A Community Data‐Driven Network Reconstruction. Journal of Cellular Physiology 231: 2339–2345
• Song HS, McClure RS, Bernstein HC, Overall CC, Hill EA, 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