TIAN, Liang, 田亮博士
B.Sc., Ph.D., Nanjing University of Aeronautics and Astronautics, Nanjing, China
Postdoctoral, Harvard Medical School, Boston, USA
Postdoctoral, Brigham and Women’s Hospital, Boston, USA
Current Research Interests
Dr. Tian performs theoretical and empirical research in the fields of Complex Systems, Statistical Physics, and System Biology by using various analytical, numerical, simulation, and data mining and machine learning techniques. The main topics include phase transition and critical phenomena, complex network, human microbiome and community ecology, and so on.
Current projects include:
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 under each strategy, we still lack sufficient 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 quantitative study of effects of external interventions 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 to better capture the community ecology and dynamics to inform microbiome-based therapies.
Biological Big-Data 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 are generally interested in multi-dimensional biological data analysis using various machine learning techniques, such as hidden Markov modeling, network-based clustering, Bayesian network, consensus clustering, echo state networks, and so on. Recently, we focus on exploring the impact of the structure of artificial neural networks on their performance.
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 are also interested in application and development of modern statistical physics techniques and methodologies for data processing and network reconstruction, such as spectral methods in time-series data analysis and dimension reduction. Progress in this direction will improve statistical physics models across different time scales, extract universal properties, and explore new ideas towards relationships between network structure and dynamics, providing the theoretical underpinning for the other projects.
Five selected publications in recent five years:
Liang Tian#; Xuefei Li#; Fei Qi; Qian-Yuan Tang; Viola Tang; Jiang Liu; Zhiyuan Li; Xingye Cheng; Xuanxuan Li; Yingchen Shi; Haiguang Liu; Lei-Han Tang; Harnessing peak transmission around symptom onset for non-pharmaceutical intervention and containment of the COVID-19 pandemic, Nature Communications, 10.1038/s41467-021-21385-z (2021).
Liang Tian; Xu-Wen Wang; Ang-Kun Wu; Yuhang Fan; Jonathan Friedman; Amber Dahlin; Matthew K. Waldor; George M. Weinstock; Scott T. Weiss; Yang-Yu Liu; Deciphering functional redundancy in the human microbiome, Nature Communications, 11, 6217 (2020).
陈沫先; 韦中; 田亮; 谭扬; 黄建东; 戴磊; 合成微生物群落的构建与应用, 科学通报, 66, 273-283 (2020).
Abhijeet R. Sonawane#; Liang Tian#; Chin-Yi Chu; Xing Qiu; Lu Wang; Jeanne Holden-Wiltse; Alex Grier; Steven R. Gill; Mary T. Caserta; Ann R. Falsey; David J. Topham; Edward E. Walsh; Thomas J. Mariani; Scott T. Weiss; Edwin K. Silverman; Kimberly Glass; Yang-Yu Liu; Microbiome-Transcriptome Interactions Related to Severity of Respiratory Syncytial Virus Infection, Scientific Reports, 9, 13824 (2019).
Liang Tian; Amir Bashan; Da-Ning Shi; Yang-Yu Liu*; Articulation points in complex network, Nature Communications, 8, 14223 (2017).