Research Interests & Selected Papers

Human Microbiome and Community Ecology. 
Human-associated microbes form a very complex and dynamic ecosystem, which can be altered by drastic diet change, medical interventions, and many other factors. The alterability of our microbiome offers a promising future for a variety of microbiome-based therapies such as ingesting probiotics or prebiotics, and fecal microbiota transplantation, in treating diseases associated with disrupted microbiota. Despite successful cases for each strategy, we still lack a complete understanding of which strategy works best for a given individual, and whether there are long-term safety issues. Indeed, the complex topology and dynamics of the ecological network underlying the human gut microbiota render the quantitative study of microbiome-based therapies extremely difficult. The future of microbiome-based therapies will be bright only if we fully understand the structure and dynamics of our gut microbial ecosystems. Our long-term objective is to construct a modeling framework based on community ecology and dynamical systems to better design microbiome-based therapies.

Selected Publications/Preprints:

  1. Bashan A, Gibson TE, Friedman J, Carey VJ, Weiss ST, Hohmann EL, Liu Y-YUniversality of Human Microbial Dynamics. Nature 2016;534:259-262.
  2. Gibson TE, Bashan A, Cao H-T, Weiss ST, Liu Y-YOn the Origins and Control of Community Types in the Human Microbiome. PLOS Computational Biology 2016;12(2):e1004688.
  3. McGeachie MJ, Sordillo JE, Gibson T, Weinstock GM, Liu Y-Y, Gold DR, Weiss ST, Litonjua A. Longitudinal Prediction of the Infant Gut Microbiome with Dynamic Bayesian Networks. Scientific Reports 2016;6:20369.
  4. Cao H-T, Gibson T, Bashan A, Liu Y-YPitfalls in Inferring Human Microbial Dynamics from Temporal Metagenomics Data. BioEssays 2017;39(2):1600188.
  5. Yan G, Martinez ND, Liu Y-YStability of Degree Heterogeneous Ecological Networks. Journal of the Royal Society Interface 2017;14(131):20170189. 
  6. Xiao Y, Angulo MT, Friedman J, Waldor MK, Weiss ST, Liu Y-YMapping the ecological networks of microbial communitiesNature Communications  2017;8:2042.
  7. Fan Y, Xiao Y, Momeni B, Liu Y-Y. Horizontal Gene Transfer Can Help Maintain the Equilibrium of Microbial Systems. Journal of Theoretical Biology 2018;454:53-59. Publisher's Version
  8. Li P, Zhang T, Xiao Y, Tian L, Cui B, Ji G, Liu Y-Y, Zhang F. Timing for the second fecal microbiota transplantation to maintain the long-term benefit from the first treatment for Crohn’s disease. Applied Microbiology and Biotechnology 2018; Publisher's Version
  9. Angulo MT, Moog CH, Liu Y-YA theoretical framework for controlling complex microbial communities. Nature Communications 2019;10:1045. Publisher's Version
  10. Sonawane AR, Tian L, Chu C, Qiu X, Wang L, Holden-Wiltse J, Grier A, Gill SR, Caserta MT, Walsh EE, Mariani TJ, Weiss ST, Silverman EK, Glass K, Liu Y-YNetwork-based Integrative Analysis of Nasal Microbiome, Host CD4 T Cell Transcriptome, and Clinical Outcomes in Infants with Respiratory Syncytial Virus InfectionScientific Reports 2019;9:13824.
  11. Chen Y, Angulo MT, Liu Y-YRevealing complex ecological dynamics via symbolic regression. BioEssays  (cover story) 2019;41(12):1970121.
  12. Wang X, Liu Y-YOvercome Competitive Exclusion in Ecosystems. iScience (cover story) 2020;23(4):101009.
  13. Wang X-W, Liu Y-YComparative study of classifiers for human microbiome data. Medicine in Microecology 2020;4:100013.
  14. Vila JCC, Liu Y-Y, Sanchez A. Dissimilarity-Overlap Analysis of Replicate Enrichment CommunitiesThe ISME Journal 2020;14(2505).

  15. Xiao Y, Angulo MT, Lao S, Weiss ST, Liu Y-YAn Ecological Framework to Understand the Efficacy of Fecal Microbiota Transplantation. Nature Communications 2020;11:3329. Publisher's Version

  16. Tian L, Wang X-W, Wu A-K, Fan Y, Friedman J, Dahlin A, Waldor MK, Weinstock GM, Weiss ST, Liu Y-YDeciphering Functional Redundancy in the Human MicrobiomeNature Communications 2020;11:6217. Publisher's Version
  17. Dedrick S, Akbari MJ, Dyckman S, Zhao N, Liu Y-Y, Momeni B. Impact of temporal pH fluctuations on the coexistence of nasal bacteria. Frontiers in Microbiology 2021;12:144. Publisher's Version

  18. Cao Y, Wang L, Ke S, Gálvez JAV, Pollock NR, Barret C, Sprague R, Daugherty K, Xu H, Lin Q, Yao J, Chen Y, Kelly CP, Liu Y-Y, Chen X. Fecal Mycobiota Combined with Host Immune Factors Distinguish Clostridioides difficile Infection from Asymptomatic Carriage. Gastroenterology 2021;160(7):2328-2339. Publisher's Version
  19. Deng Y, Huang Y, Che Y, Yang Y, Yin X, Yan A, Dai L, Liu Y-Y, Polz M, Zhang T. Microbiome assembly for sulfonamide subsistence and the transfer of genetic determinants. The ISME journal 2021;15:2817. Publisher's Version
  20. Sun Z, Huang S, Zhang M, Zhu Q, Haiminen N, Carrieri A-P, Vazquez-Baeza Y, Parida L, Kim H-C, Knight R, Liu Y-YChallenges in Benchmarking Metagenomic Profilers. Nature Methods 2021;18:618-626. Publisher's Version
  21. Zhao N, Saavedra S, Liu Y-YThe impact of colonization history on the composition of ecological systems. Physical Review E 2021;103:052403. Publisher's Version

  22. Ke S, Pollock NP, Wang X-W, Chen X, Daugherty K, Lin Q, Xu H, Garey KW, Gonzales-Luna AJ, Kelly CP, Liu Y-YIntegrating gut microbiome and host immune markers to understand the pathogenesis of Clostridioides difficile infectionGut Microbes 2021;13(1):18. Publisher's Version

  23. Huang S, Jiang S, Huo D, Allaband C, Estaki M, Cantu V, Ferre P, Vázquez-Baeza Y, Zhu Q, Ma C, Li W, Zarrinpar A, Li C, Liu Y-Y, Knight R, Zhang J. Candidate probiotic Lactiplantibacillus plantarum HNU082 rapidly and convergently evolves within human, mice, and zebrafish gut, but differentially influence the resident microbiome. Microbiome 2021;9:151. Publisher's Version

  24. Ke S, Mitchell SJ, MacArthur MR, Kane AE, Sinclair DA, Venable EM, Chadaideh KS, Carmody RN, Grodstein F, Mitchell JR, Liu Y-YGut Microbiota predicts Healthy Late-life Aging in Male Mice. Nutrients 2021;13(9):3290. Publisher's Version

  25. Morrow JD, Castaldi P, Chase R, Yun J, Lee S, Liu Y-Y, Hersh C. Peripheral blood microbial signatures in current and former smokers. Scientific Reports 2021;11:19875. Publisher's Version

  26. Aparicio A, Velasco JX, Moog CH, Liu Y-Y, Angulo MT. Identifying sensor species to predict critical transitions in complex ecosystems. PNAS 2021;118(51):e2104732118. Publisher's Version

  27. Wang Y, Tang J, Lv Q, Tan Y, Dong X, Liu H, Zhao N, He Z, Kou Y, Tan Y, Liu X-an, Wang L, Liu Y-Y, Dai L. Establishment and Resilience of Transplanted Gut Microbiota in Aged Mice . iScience 2022;25(1):103654. publisher's Version

  28. Michel-Mata S, Wang X-W, Liu Y-Y, Angulo MT. Predicting microbiome compositions through deep learning. iMeta 2022;e3 Publisher's Version
  29. Lee-Sarwar K, Dedrick S, Kelly RS, Zeiger RS, O’Connor GT, Sandel MT, Bacharier LB, Beigelman A, Laranjo N, Gold DR, Lasky-Su J, Litonjua AA, Liu Y-Y, Weiss ST. Association of the Gut Microbiome and Metabolome with Wheeze Frequency in Childhood Asthma. The Journal of Allergy and Clinical Immunology 2022 Publisher's Version

  30. Liu H, Liao C, Tang J, Chen J, Lei C, Zheng L, Wu L, Zhang C, Liu Y-Y, Xavier J, Dai L. Ecological dynamics of the gut microbiome in response to dietary fiber. The ISME Journal 2022;16:2040. Publisher's Version
  31. Ke S, Weiss ST, Liu Y-Y. Rejuvenating the human gut microbiome. Trends in Molecular Medicine 2022 Publisher's Version
  32. Hu H, Tan Y, Li C, Chen J, Kou Y, Xu Z, Liu Y-Y, Tan Y, Dai L. StrainPanDA: linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data. iMeta In Press;

  33. Cao Y, Wang L, Ke S, Kelly CP, NR P, Pollock NR, Gálvez JAV, Daugherty K, Xu H, Yao J, Chen Y, Liu Y-Y, Chen X. Analysis of Intestinal Mycobiota of Patients with Clostridioides difficile Infection among a Prospective Inpatient Cohort. Microbiology Spectrum In Press;

  34. Gibson TE, Carey V, Bashan A, Hohmann EL, Weiss ST, Liu Y-YOn the Stability Landscape of the Human Gut Microbiome: Implications for Microbiome-based TherapiesbioRxiv's Version
  35. Wang X-W, Hu Y, Grodstein F, Bhupathiraju SN, Sun Q, Zhang X, Hu F, Weiss ST, Liu Y-YNutritional Redundancy in Human Diet. Submitted; bioRxiv's Version
  36. Wang X-W, Liu Y-YCharacterizing scaling laws in gut microbial dynamics from time series data: caution is warranted. Submitted; bioRxiv's Version
  37. Wang X-W, Liu Y-YOrigins of Scaling Laws in Microbial Dynamics. Submitted; BioRxiv's Version
  38. Li L, Ning Z, Zhang X, Butcher J, Simopoulos C, Mayne J, Stintzi A, Mack DR, Liu Y-Y, Figeys D. Revealing Protein-Level Functional Redundancy in the Human Gut Microbiome using Ultra-deep Metaproteomics. Submitted; BioRxiv's Version
  39. Ke S, Xiao Y, Weiss ST, Chen X, Kelly CP, Liu Y-YA Computational Method to Dissect Colonization Resistance of the Gut Microbiota against Pathogens.  Submittted; BioRxiv's Version
  40. Ke S, Weiss ST, Liu Y-Y. Dissecting the Role of the Human Microbiome in COVID-19 via Metagenome-assembled Genomes. Submitted; BioRxiv's Version
  41. Wang X-W, Wu L, Dai L, Yin X, Zhang T, Weiss ST, Liu Y-YEcological Dynamics Imposes Fundamental Challenges in Microbial Source Tracking. Submitted; BioRxiv's Version
  42. Ke S, Guimond A-J, Tworoger SS, Huang T, Chan AT, Liu Y-Y, Kubzansky LD. Gut feelings: Associations of emotions and emotion regulation with the gut microbiome in women. Submitted; BioRxiv's Version
  43. Wang T, Wang X-W, Lee-Sarwar K, Litonjua AA, Weiss ST, Sun Y, Maslov S, Liu Y-YPredicting metabolomic profiles from microbial composition through neural ordinary differential equations. Submitted; BioRxiv's Version


Control Principles of Complex Systems. 
A reflection of our ultimate understanding of a complex networked system is our ability to control its behavior. Typically, control has multiple prerequisites: it requires an accurate map of the network that governs the interactions between the system’s components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in dynamical systems and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? Uncovering the control principles of complex networked systems can help us explore and ultimately understand the fundamental laws that govern their behavior.

Selected Publications/Preprints:

  1. Liu Y-Y, Slotine J-J, Barabási A-L. Controllability of complex networks. Nature (featured as a cover story) 2011;473:167–173.
  2. Liu YY, Slotine JJ, Barabasi AL. Few inputs can reprogram biological networks (Liu et al. Reply). Nature 2011;478:E4–E5.
  3. Slotine J-J, Liu Y-YComplex networks: The missing link. Nature Physics 2012;8:512–513.
  4. Liu Y-Y, Slotine J-J, Barabási A-L. Control Centrality and Hierarchical Structure in Complex Networks. PLOS ONE 2012;7:e44459.
  5. Liu Y-Y, Slotine J-J, Barabási A-L. Observability of complex systemsPNAS (featured as a cover story) 2013;110:2460–2465.
  6. Pósfai M, Liu Y-Y, Slotine J-J, Barabási A-L. Effect of correlations on network controllability. Scientific Reports 2013;3:1067
  7. Jia T, Liu Y-Y, Csóka E, Pósfai M, Slotine J-J, Barabási A-L. Emergence of bimodality in controlling complex networks. Nature Communications 2013;4:2002.
  8. Gao J, Liu Y-Y, D’Souza R, Barabási A-L. Target Control of Complex Networks. Nature Communications 2014;5:5415.
  9. Liu Y-YTheoretical progress and practical challenges in controlling complex networks. National Science Review 2014;1(3):341-343.
  10. Yan G, Tsekenis G, Barzel B, Slotine J-J, Liu Y-Y, Barabási A-L. Spectrum of Controlling and Observing Complex Networks. Nature Physics 2015;11:779-786.
  11. Zhao C, Wang W-X, Liu Y-Y, Slotine J-J. Individual dynamics induces symmetry in network controllability. Scientific Reports 2015;5(8422):1-5. 
  12. Basler G, Nikoloski Z, Larhlimi A, Barabási A-L, Liu Y-YControl of Fluxes in Metabolic Networks. Genome Research (featured as a cover story) 2016;26:956-968.
  13. Vinayagam A, Gibson TE, Lee H-J, Yilmazel B, Roesel C, Hu Y, Kwon Y, Sharma A, Liu Y-Y, Perrimon N, Barabási A-L. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets. PNAS 2016;113(18):4976-4981.
  14. Liu Y-Y, Barabási A-L. Control Principles of Complex Systems. Reviews of Modern Physics 2016;88(3):053006.
  15. Li A, Cornelius S, Liu Y-Y, Wang L, Barabási A-L. The Fundamental Advantages of Temporal Networks. Science 2017;358(6366):1042-1046.
  16. Sharma A, Halu A, Decano JL, Menche J, Liu Y-Y, Fadista J, Santolini M, Padi M, Weiss ST, Vidal M, Silverman EK, Aikawa M, Barabási A-L, Groop L, Loscalzo J. Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes. npj Systems Biology and Applications 2018;4:25. Publisher's Version
  17. Li A, Liu Y-Y. Controlling network dynamics. Advances in Complex Systems 2019;22:1950021.

  18. Xiao Y, Song C, Tian L, Liu Y-Y. Accelerating the Emergence of Order in Swarming Systems. Advances in Complex Systems 2019;22:1950015.

  19. Ma Q, Liu Y-Y, Olshevsky A. Optimal Lockdown for Pandemic Control. Submitted; arXiv's Version

  20. Li A, Cornelius S, Liu Y-Y, Wang L, Barabási A-L. Control energy scaling in temporal networks. arXiv's Version
  21. Ma Q, Liu Y-Y, Olshevsky A. Optimal Lockdown for Pandemic ControlarXiv's Version

Complex Networks: Structure and Dynamics.
We are interested in the intricate interplay between the structure and dynamics of complex networks. In particular, using tools from statistical physics and graph theory, we studied various percolation transitions on complex networks, revealing their implications in dynamical processes on networked systems. We explored the origins of network motifs --- the overrepresented interconnection patterns observed in various real-world networks, finding that network motifs naturally emerge from interconnection patterns that favor stability. We also studied the fundamental limitations in reconstructing networks from measured temporal data of complex dynamical systems. Counterintuitively, we find that reconstructing any property of the interaction matrix is generically as difficult as reconstructing the interaction matrix itself, requiring equally informative temporal data. 

Selected Publications/Preprints:

  1. Liu Y-Y, Csóka E, Zhou H, Pósfai M. Core percolation on complex networks. Physical Review Letters. 2012;109:205703.
  2. Zhao J-H, Zhou H-J, Liu Y-YInducing effect on the percolation transition in complex networks. Nature Communications 2013;4:2412.
  3. Angulo MT, Liu Y-Y, Slotine J-J. Network motifs emerge from interconnections that favor stability. Nature Physics 2015;11:848-852.
  4. Barzel B, Liu Y-Y, Barabási A-L. Constructing minimal models for complex system dynamics. Nature Communications 2015;6:7186.
  5. Tian L, Bashan A, Shi D-N, Liu Y-YArticulation Points in Complex Networks. Nature Communications 2017;8:14223.
  6. Cao H-T, Gibson TE, Mou S, Liu Y-YImpacts of Network Topology on the Performance of a Distributed Algorithm Solving Linear Equations. Proceedings of the 55th IEEE Conference on Decision and Control (CDC), 2016; arXiv's Version
  7. Angulo MT, Moreno JA, Barabási A-L, Liu Y-YFundamental limitations of network reconstruction. Journal of the Royal Society Interface 2017;14(127):20160966.
  8. Wu A-K, Tian L, Liu Y-YBridges in Complex Networks. Physical Review E 2018;97:012307.
  9. Wu M, Zhang Y, He S, Chen J, Sun Y, Liu Y-Y, Zhang J, Poor HV. A General Framework of Studying Eigenvector Multicentrality in Multilayer Networks. PNAS 2019.
  10. Wu A-K, Tian L, Coutinho BC, Omar Y, Liu Y-YStructural vulnerability of quantum networks. Physical Review A 2020;101(5):052315.
  11. Li A, Zhou L, Su Q, Cornelius SP, Liu Y-Y, Wang L, Levin SA. Evolution of Cooperation on Temporal Networks. Nature Communications 2020;11:2259.
  12. Coutinho BC, Wu A-K, Zhou H-J, Liu Y-YCovering problems and core percolations on hypergraphs. Physical Review Letters 2020;124(24):248301. Publisher Version
  13. Angulo MT, Lippner G, Liu Y-Y, Barabási A-L. Sensitivity of Complex NetworksarXiv Version

Bioinformatics and Machine Learning.

The exponential growth of the amount of biological data available today prompts us to adopt and develop machine techniques to transform all these heterogeneous data into biological knowledge and testable models. We have been working on biomedical data analysis using various machine learning techniques, e.g., hidden Markov modeling, network-based clustering, Bayesian network, consensus clustering, echo state networks. We are genearally interested in integrative analysis of multi-omics data. Currently, we are interested in exploring the impact of network structure of artificial neural networks on their performance.   


Selected Publications/Preprints:

  1. Liu Y, Park J, Dahmen KA, Chemla YR, Ha T. A comparative study of multivariate and univariate hidden Markov modelings in time-binned single-molecule FRET data analysis. The Journal of Physical Chemistry B 2010;114:5386–5403.
  2. McDonald M-L, Mattheisen M, Cho M, Liu Y-Y, Harshfield B, Hersh C, Bakke P, Gulsvik A, Lange C, Beaty T, Silverman E. Beyond GWAS in COPD: Probing the Landscape between Gene-Set Associations, Genome-Wide Associations and Protein-Protein Interaction Networks. Human Heredity 2014;78(3):131-139.
  3. McGeachie MJ, Sordillo JE, Gibson T, Weinstock GM, Liu Y-Y, Gold DR, Weiss ST, Litonjua A. Longitudinal Prediction of the Infant Gut Microbiome with Dynamic Bayesian Networks. Scientific Reports 2016;6:20369.
  4. Chang Y, Glass K, Liu Y-Y, Silverman EK, Crapo J, Tal-Singer R, Bowler RP, Dy J, Cho MH, Castaldi PJ. COPD Subtypes Identified by Network-Based Clustering of Blood Gene Expression. Genomics (featured as a cover story) 2016;107(2-3):51-58.
  5. Liu H, Zhao R, Fang H, Cheng F, Fu Y, Liu Y-YEntropy-based consensus clustering for patient stratification. Bioinformatics 2017;btx167:1-8..
  6. Chen Y, Angulo MT, Liu Y-YRevealing complex ecological dynamics via symbolic regression. BioEssays (cover story) 2019;41(12):1970121
  7. Fan C, Zeng L, Sun Y, Liu Y-Y. Finding key players in complex networks through deep reinforcement learning. Nature Machine Intelligence 2020.
  8. Aceituno PV, Yan G, Liu Y-YTailoring Artificial Neural Networks for Optimal Learning. iScience 2020;23(9):101440. 
  9. Wang X-W, Chen Y, Liu Y-YLink Prediction through Deep Learning. iScience 2020;23(10):101626. 
  10. Levy O, Amit G, Vaknin D, Snir T, Efroni S, Castaldi P, Liu Y-Y, Cohen HY, Bashan A. Age-related loss of gene-to-gene transcriptional coordination among single cells. Nature Metabolism 2020;2:1305-1315. Publisher's Version
  11. Fan C, Shen M, Nussinov Z, Liu Z, Sun Y, Liu Y-YFinding spin glass ground states through deep reinforcement learning. Submitted; arXiv's Version

  12. Wang X-W, Madeddu L, Spirohn K, Martini L, Fazzone A, Becchetti L, Wytock TP, Kovács IA, Balogh OM, Benczik B, Pétervári M, Ágg B, Ferdinandy P, Vulliard L, Menche J, Colonnese S, Petti M, Scarano G, Cuomo F, Hao T, Laval F, Willems L, Twizere J-C, Calderwood MA, Petrillo E, Barabási A-L, Silverman EK, Loscalzo J, Velardi P, Liu Y-YAssessment of community efforts to advance computational prediction of protein-protein interactions. Submitted; bioRxiv's Version

  13. Chen C, Liao C, Liu Y-YTeasing out Missing Reactions in Genome-scale Metabolic Networks through Deep Learning. Submitted; BioRxiv's Version

  14. Chen C, Liu Y-YA Survey on Hyperlink Prediction. Submitted; arXiv's Version