Song Feng, Ph.D.

Educational background

• Postdoc, Center for Nonlinear Studies, Los Alamos National Laboratory
• PhD, Evolutionary Systems Biology, University of Warwick
• MSc, Bioscience, King Abdullah University of Science and Technology
• BSc, Biotechnology, Zhejiang University

Professional biography

• Song Feng was born and raised in Northern China. After receiving a BSc in Biotechnology from Zhejiang University, he studied at King Abdullah University of Science and Technology in Saudi Arabia, where he earned an MSc in Bioscience. He then moved to the United Kingdom to pursue a PhD in Evolutionary Systems Biology at the University of Warwick, where he explored the design principles of biochemical reaction networks through in silico evolution and theoretical analysis. He subsequently came to the United States, first as a postdoctoral researcher at the Center for Nonlinear Studies at Los Alamos National Laboratory, and later as a scientist at Pacific Northwest National Laboratory. Throughout his career, he has studied diverse complex biological systems using complementary quantitative approaches, including computational modeling, multi-omics analysis, and machine learning.

Research interests

• Biological systems are complex in the sense that their dynamics and functions emerge from interactions among many different molecules and cells across multiple scales. As an example, microbial physiology is determined by networks of biomolecules whose functions are characterized by their interactions with, and modifications by, other molecules; meanwhile, microbial physiology in turn drives the assembly, dynamics, and functions of microbial communities. Dr. Feng's research program explores the design principles underlying microbial physiology and microbial communities in the contexts of evolution and ecology. The lab currently combines an integrative multi-omics approach (e.g., proteomics, metabolomics) with computational modeling to characterize and understand microbial physiology and microbial communities. One overarching goal is to build mechanistic yet data-constrained virtual cell models that enable the rational design of microbial communities. Other projects leverage AI and automation to build models, tools, and software that accelerate our research.

Selected publications

• Rozum JC, Sims AC, Li X, Snigdha S, Zhang T, Melchior JT, Ciesielski D, Pollock DD, Wiley HS, Qian WJ, Feng S‡. ProteoMeter: a pipeline for integrating multi-PTM and limited proteolysis data to reveal modification-structure coupling at the residue level. NAR Genom. Bioinform.. 2026 Sep;8(3):lqag073. doi: 10.1093/nargab/lqag073
• VonKaenel ED‡, Rozum JC, Zhang T, Stratton K, Bramer L, Wiley HS, Qian WJ, Sims AS, Melchior JT, Feng S‡. Aggregation methods for quantifying PTM and structural changes in bottom-up proteomics. J. Proteome Res.. 2026, 25, 6, 3159–3167. doi: 10.1021/acs.jproteome.5c00782.
• Kim H∗‡, Feng S∗, Bohutskyi P, Li X, Mejia-Rodriguez D, Zhang T, Qian WJ, Cheung MS‡. Thiol post-translational modifications modulate allosteric regulation of the OpcA-G6PDH complex through conformational gate control. Protein Sci. 2026 May;35(5):e70561. doi: 10.1002/pro.70561.
• Rozum JC∗, Sineath W∗, Bohutskyi P, Quenneville J, Kim DN, Johnson C, Mehta AP, Evans J, Pollock D, Qian WJ, Cheung MS, Wu R‡, Feng S‡. Synergy and antagonism in a genome-scale model of metabolic hijacking by bacteriophage. Sci Adv. 2026 Apr 3;12(14):eaeb7646. doi: 10.1126/sciadv.aeb7646.
• Rozum JC, Ufford H, Im AK, Zhang T, Pollock DD, Kim DN‡, Feng S‡. ProCaliper: functional and structural analysis, visualization, and annotation of proteins. Bioinform Adv. 2025 Nov 9;5(1):vbaf275. doi: 10.1093/bioadv/vbaf275
• Johnson CGM∗, Johnson Z∗, Mackey LS∗, Li X, Sadler NC, Zhang T, Qian W, Bohutskyi P, Feng S‡, Cheung MS‡. Multi-omics reveals temporal scales of carbon metabolism in Synechococcus elongatus PCC 7942 under light disturbance. PRX Life. 2025, 3(3):033017. doi: 10.1103/l2dp-kw2t
• Kim DN‡, Yin T, Zhang T, Im AK, Cort JR, Rozum JC, Pollock D, Qian W-J, Feng S‡. Artificial intelligence transforming post-translational modification research. Bioengineering. 2025; 12(1):26. doi: 10.3390/bioengineering12010026
• Ushakumary MG∗, Feng S∗, Bandyopadhyay G, Olson H, Weitz KK, Huyck HL, Poole C, Purkerson JM, Bhattacharya S, Ljungberg MC, Mariani TJ, Deutsch GH, Misra RS, Carson JP, Adkins JN, Pryhuber GS, Clair G‡. Cell population-resolved multi-omics atlas of the developing lung. Am J Respir Cell Mol Biol. 2025 May;72(5):484-495. doi: 10.1165/rcmb.2024-0105OC.
• Feng S, Calinawan A, Pugliese P, Wang P, Ceccarelli M, Petralia F, Gosline SJC‡. Decomprolute is a benchmarking platform designed for proteomic based tumor deconvolution. Cell Reports Methods, 2024, 4(2):100708. doi: 10.1016/j.crmeth.2024.100708.
• Feng S, Heath E, Jefferson B, Joslyn C, Kvinge H, Mitchell HD, Praggastis B, Eisfeld AJ, Sims AC, Thackray LB, Fan S, Walters KB, Halfmann PJ, Westhoff-Smith D, Tan Q, Menachery VD, Sheahan TP, Cockrell AS, Kocher JF, Stratton KG, Heller NC, Bramer LM, Diamond MS, Baric RS, Waters KM, Kawaoka Y, McDermott JE, Purvine E‡. Hypergraph models of biological networks to identify genes critical to pathogenic viral response. BMC Bioinformatics. 2021 May 29;22(1):287. doi: 10.1186/s12859-021-04197-2.
• Lin YT∗‡, Feng S∗‡, Hlavacek WS‡. Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks. J Chem Phys. 2019 Jun 28;150(24):244101. doi: 10.1063/1.5096774.
• Shirin A∗, Klickstein IS∗, Feng S∗, Lin YT∗, Hlavacek WS‡, Sorrentino F‡. Prediction of optimal drug schedules for controlling autophagy. Sci Rep. 2019 Feb 5;9(1):1428. doi: 10.1038/s41598-019-38763-9.
• Feng S, Soyer OS‡. In silico evolution of signaling networks using rule-based models: bistable response dynamics. Methods Mol Biol. 2019;1945:315-339. doi: 10.1007/978-1-4939-9102-0 15.
• Feng S∗, S´aez M∗, Wiuf C, Feliu E‡, Soyer OS‡. Core signalling motif displaying multi-stability through multi-state enzymes. J R Soc Interface. 2016 Oct;13(123):20160524. doi: 10.1098/rsif.2016.0524.
• Feng S, Ollivier JF, Soyer OS‡. Enzyme sequestration as a tuning point in controlling response dynamics of signalling networks. PLoS Comput Biol. 2016 May 10;12(5):e1004918. doi: 10.1371/journal.pcbi.1004918.
• Feng S∗, Ollivier JF∗, Swain PS‡, Soyer OS‡. BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling. Nucleic Acids Res. 2015 Oct 30;43(19):e123. doi: 10.1093/nar/gkv595.