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TANG, Lei Han

TANG, Lei Han, 湯雷翰教授


Dr. Lei-Han Tang completed his PhD in statistical physics at the Carnegie Mellon University in 1987. He did postdoctoral work on nonequilibrium and disordered systems at various US and German institutions including Texas A&M University, the IFF at KFA Jülich and the Institute for Theoretical Physics at the University of Cologne. He served as Lecturer at the Imperial College London from 1996-1997 before joining the Hong Kong Baptist University as an associate professor in 1997 and then full professor in 2005. His research combines analytical and computational approaches to explore the effects of equilibrium and nonequilibrium fluctuations in various physical and biophysical contexts. In recent years, he has collaborated with experimentalists on the development of quantitative tools and models to analyze and integrate biological data and behavior at the cellular level, in particular those related to metabolism, cell motility, and development. He has also developed formalisms that integrate complex behavior of individuals with communication and feedbacks at the population level to explain collective phenomena such as oscillations and outbreaks. He has published more than 90 articles in physics and biological physics, including 20 in the Physical Review Letters. He has been an active researcher and facilitator of interdisciplinary study of living systems. He was elected Fellow of the American Physical Society in 2010 and is a current member of the IUPAP C3 Commission on Statistical Physics. He is the Director of the Institute of Computational and Theoretical Studies at HKBU.

Current Research Interests

Research highlights

Lei-Han Tang, Department of Physics, HKBU

Emergence of collective oscillations in adaptive cells

Shou-Wen Wang and Lei-Han Tang, Nature Communications10: 5613 (2019)

Emergence of collective oscillations in adaptive cells

Caption: Spontaneous oscillations in a communicating cell population. A. An example of chemical oscillations where cells sense and secrete a signaling molecule that mediates cell-to-cell communication. B. In adaptive response, intracellular activities boost transiently under a stimulus ramp but return to normal level when the signal stabilizes. Such behavior typically involves active processes that consume ATP. C. Our work establishes a rigorous mathematical relation between adaptive response and phase-leading behavior in intracellular activity at low frequencies, which underlies spontaneous collective oscillations in diverse biological contexts, including starved social amoeba (D. discoideum), yeast suspensions, and otoacoustic emissions from the human ear.


Calibrated Intervention and Containment of the COVID-19 Pandemic

L. Tian et al., COVID-19 Modelling Group, HKBU, under review at Nature Communications,

Calibrated Intervention and Containment of the COVID-19 Pandemic

Caption: Early development of COVID-19 outbreaks and containment. a. Daily confirmed cases in Hubei province since the Wuhan lockdown on 23 January, 2020, showing three phases of epidemic development. A daily growth rate of 0.3/day is observed during the initial outbreak (Phase I). b. Data for the rest of China. Early control measures significantly shortened phase II. c. d. Countries in Asia and Europe went through similar stages of pandemic development, but the duration of each phase varied greatly. Mathematical analysis of a model we developed, with parameters calibrated against clinical data, offers a quantitative explanation of the observed behaviour.

Unveil Hidden Dissipation from Trajectory Measurement

S.-W. Wang, K. Kawaguchi, S.-i. Sasa, and L.-H. Tang, Phys. Rev. Lett. 117, 070601 (2016).

Man-made and biological nano-devices perform their function by staying out of thermal equilibrium. A formalism is developed to estimate the energy loss associated with a slow variable from the frequency spectrum of its FDT violation.

Fig. 1.

Fig. 1. Violation spectrum of the fluctuation-dissipation theorem (FDT) computed from partially observed trajectory. The system (a colloidal particle) is imbedded in a fluid and is driven by a hidden active process on a time scale time scale, where symbol is time scale for viscous relaxation.

Fig. 2.

Fig. 2. (a) A general nonequilibrium Markov process with a dissipative cycle formed by fast and slow processes. (b) Effective slow dynamics. (c) Eigenvalue spectrum of the master equation leading to the relation between FDT violation and hidden entropy production.


A Statistical Mechanical Study of Circularly Polarized Collective Motion of Micro-Swimmers in a Thin Liquid Film

In collaboration with Dr. Yilin Wu, Chinese University of Hong Kong

Flocking, swarming and other forms of collective motion in a population of individuals are common in the biological world, spanning from micro-organisms to animals. Progress on the principles of self-organization in these systems has inspired the design of artificial systems such as self-propelled colloidal particles and synthetic cells for drug delivery and other medical applications. In this project, we identified an unstable mode responsible for the emergence of collective motion of E. coli cells confined to a thin layer of liquid film, first discovered in Yilin Wu’s Lab (Nature 542, 210–214(2017)).

Polarized Collective Motion of Micro-Swimmers in a Thin Liquid Film

Selected Publications

  1. Calibrated Intervention and Containment of the COVID-19 Pandemic, 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 (2020).
  2. Pre-symptomatic Transmission in the Evolution of the COVID-19 Pandemic, 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, 2020 - (2020).
  3. L. Tian, H. Ma, W. Guo and L.-H. Tang, “Phase transitions of the q-state Potts model on multiply-laced Sierpinski gaskets”, Euro. Phys. J. B 86, 197 (2013).
  4. H. Zhi, L.-H. Tang, Y. Xia and J. Zhang, “Ssk1p-independent activation of Ssk2p plays an important role in the osmotic stress response in Saccharomyces cerevisiae: alternative activation of Ssk2p in osmotic stress”, PLoS One 8, e54867 (2013).
  5. B. Yu, M. Yang, L. Shi, Y. Yao, Q. Jiang, X. Li, L.-H. Tang, B. J. Zheng, K. Y. Yuen, D. K. Smith, E. Song, J. D. Huang, “Explicit hypoxia targeting with tumor suppression by creating an "obligate" anaerobic Salmonella Typhimurium strain”, Scientific Reports 2, 436 (2012).
  6. X. Fu, L.-H. Tang, C. Liu, J.-D. Huang, T. Hwa, and P. Lenz, “Stripe formation in bacterial systems with density-suppressed motility”, Phys. Rev. Lett. 108, 198102 (2012).
  7. C. Liu, X. Fu, L. Liu, X. Ren, C.K.L. Chau, S. Li, L. Xiang, H. Zeng, G. Chen, L.-H. Tang, P. Lenz, X. Cui, W. Huang, T. Hwa, J.-D. Huang, “Sequential establishment of stripe patterns in an expanding cell population”, Science 334, 238 (2011).
  8. S. Lin, Z. Yang, H. Liu, L.-H. Tang, Z. Cai, “Beyond glucose: metabolic shifts in responses to the effects of the oral glucose tolerance test and the high-fructose diet in rats”, Mol. Biosyst. 7, 1537 (2011).
  9. L.-H. Tang, “To synchronize or not to synchronize, that is the question: finite-size scaling and fluctuation effects in the Kuramoto model”, J. Stat. Phys.: Theory and Experiment, P01034 (2011).
  10. L.-P. Xiong, Y.Q. Ma, and L.-H. Tang, “Synergistic effect of auto-activation and small RNA regulation on gene expression”, Chin. Phys. Lett. 27, 098701 (2010).
  11. L.P. Xiong, Y.Q. Ma and L.H. Tang, “Attenuation of transcriptional bursting in mRNA transport”, Physical Biology (2010).
  12. T.K. Ng, Y. Zhou, and L.H. Tang, “Topological glass states”, Europhyslett. 86, 10003 (2009).
  13. Z. Yang and L.H. Tang, “Coordination motifs and large-scale structural organization in atomic clusters”, Phys. Rev. B 79, 045402 (2009).
  14. L.H. Tang and Q.H. Chen, “Phase glass and zero-temperature phase transition in a randomly frustrated two-dimensional quantum rotor model”, J. Stat. Phys.: Theory and Experimental P04003 (2008).