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-Y. Universality of Human Microbial Dynamics. Nature 2016;534:259-262.
  2. Xiao Y, Angulo MT, Friedman J, Waldor MK, Weiss ST, Liu Y-Y. Mapping the ecological networks of microbial communities. Nature Communications  2017;8:2042.
  3. Angulo MT, Moog CH, Liu Y-Y. A theoretical framework for controlling complex microbial communities. Nature Communications 2019;10:1045. Publisher's Version
  4. Vila JCC, Liu Y-Y, Sanchez A. Dissimilarity-Overlap Analysis of Replicate Enrichment Communities. The ISME Journal 2020;14(2505).

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

  6. Tian L, Wang X-W, Wu A-K, Fan Y, Friedman J, Dahlin A, Waldor MK, Weinstock GM, Weiss ST, Liu Y-Y. Deciphering Functional Redundancy in the Human Microbiome. Nature Communications 2020;11:6217. Publisher's Version

  7. 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

  8. 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
  9. 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-Y. Challenges in Benchmarking Metagenomic Profilers. Nature Methods 2021;18:618-626. Publisher's Version

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

  11. 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

  12. 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

  13. Michel-Mata S, Wang X-W, Liu Y-Y, Angulo MT. Predicting microbiome compositions through deep learning. iMeta 2022;e3 Publisher's Version

  14. Ke S, Weiss ST, Liu Y-Y. Rejuvenating the human gut microbiome. Trends in Molecular Medicine 2022;28(8):619-630. Publisher's Version

  15. Ke S, Weiss ST, Liu Y-Y. Dissecting the Role of the Human Microbiome in COVID-19 via Metagenome-assembled Genomes. Nature Communications 2022;13:5235. Publisher's Version

  16. Wang X-W, Wu L, Dai L, Yin X, Zhang T, Weiss ST, Liu Y-Y. Ecological Dynamics Imposes Fundamental Challenges in Microbial Source Tracking. iMeta 2023;:e75. Publisher's Version

  17. Wang X-W, Liu Y-Y. Origins of Scaling Laws in Microbial Dynamics. Physical Review Research 2023;5:013004. Publisher's Version

  18. Liu Y-Y. Controlling the Human Microbiome. Cell Systems (In Press)

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-Y. Complex 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 systems. PNAS (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-Y. Theoretical 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-Y. Control 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. D’Souza RM, de Bernardo M, Liu Y-Y. Complex networks with complex nodes: How can we control them?. Nature Review Physics (In Press)
     

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-Y. Inducing 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-Y. Articulation Points in Complex Networks. Nature Communications 2017;8:14223.
  6. Cao H-T, Gibson TE, Mou S, Liu Y-Y. Impacts 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-Y. Fundamental limitations of network reconstruction. Journal of the Royal Society Interface 2017;14(127):20160966.
  8. Wu A-K, Tian L, Liu Y-Y. Bridges 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-Y. Structural 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-Y. Covering problems and core percolations on hypergraphs. Physical Review Letters 2020;124(24):248301. Publisher 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.   

ECC

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-Y. Entropy-based consensus clustering for patient stratification. Bioinformatics 2017;btx167:1-8..
  6. Chen Y, Angulo MT, Liu Y-Y. Revealing 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-Y. Tailoring Artificial Neural Networks for Optimal Learning. iScience 2020;23(9):101440. 
  9. Wang X-W, Chen Y, Liu Y-Y. Link 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. Wang T, Wang X-W, Lee-Sarwar K, Litonjua AA, Weiss ST, Sun Y, Maslov S, Liu Y-Y. Predicting metabolomic profiles from microbial composition through neural ordinary differential equations. Nature Machine Intelligence (In Press); BioRxiv's Version

  12. Fan C, Shen M, Nussinov Z, Liu Z, Sun Y, Liu Y-Y. Finding spin glass ground states through deep reinforcement learning. Nature Communications (In Press); arXiv's Version