My current research efforts focus on the following four areas:
Human Microbiome and Community Ecology;
Control Principles of Complex Systems;
Complex Networks: Structure and Dynamics;
Biomedical Informatics and Machine Learning.
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:
Bashan A, Gibson TE, Friedman J, Carey VJ, Weiss ST, Hohmann EL, Liu YY. Universality of Human Microbial Dynamics. Nature 2016;534:259-262.
Sun Z, Huang S, Zhang M, Zhu Q, Haiminen N, Carrieri AP, Vazquez-Baeza Y, Parida L, Kim HC, Knight R, Liu YY. Challenges in Benchmarking Metagenomic Profilers. Nature Methods 2021;18:618-626.
Liu YY. Controlling the human microbiome. Cell Systems 2023;14(2):135-159.
Wang XW, Sun Z, Jia H, Michel-Mata S, Angulo MT, Dai L, He X, Weiss ST, Liu YY. Identifying keystone species in microbial communities using deep learning. Nature Ecology & Evolution 2023 (published: 16 November 2023).
Ke S, Wang XW, Ratanatharathorn AD, Huang T, Roberts A， Grodstein F, Kubzansky LD, Koenen KC, Liu YY. Association of Post-Traumatic Stress Disorder with Dietary Pattern and Gut Microbiome in a Cohort of Women. Nature Mental Health 2023 (published: 19 October 2023).
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:
Liu YY, Slotine JJ, Barabási AL. Controllability of complex networks. Nature (featured as a cover story) 2011;473:167–173.
Liu YY, Slotine JJ, Barabási AL. Observability of complex systems. PNAS (featured as a cover story) 2013;110:2460–2465.
Liu YY, Barabási AL. Control Principles of Complex Systems. Reviews of Modern Physics 2016;88(3):053006.
D’Souza RM, Bernardo M, Liu YY, Controlling complex networks with complex nodes. Nature Review Physics 2023;5:250.
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:
Liu YY, Csóka E, Zhou H, Pósfai M. Core percolation on complex networks. Physical Review Letters. 2012;109:205703.
Zhao JH, Zhou HJ, Liu YY. Inducing effect on the percolation transition in complex networks. Nature Communications. 2013;4:2412.
Tian L, Bashan A, Shi DN, Liu YY. Articulation Points in Complex Networks. Nature Communications 2017;8:14223.
Coutinho BC, Wu AK, Zhou HJ, Liu YY. Covering problems and core percolations on hypergraphs. Physical Review Letters 2020;124(24):248301.
Biomedical Informatics 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:
Fan C, Zeng L, Sun Y, Liu YY. Finding key players in complex networks through deep reinforcement learning. Nature Machine Intelligence 2020;2:317-324.
Wang T, Wang XW, Lee-Sarwar K, Litonjua AA, Weiss ST, Sun Y, Maslov S, Liu YY. Predicting metabolomic profiles from microbial composition through neural ordinary differential equations. Nature Machine Intelligence 2023;5:284.
Fan C, Shen M, Nussinov Z, Liu Z, Sun Y, Liu YY. Searching for spin glass ground states through deep reinforcement learning. Nature Communications 2023;14:725.
Chen C, Liao C, Liu YY. Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning. Nature Communications 2023;14:2375.