Faculty Members

TANG, Lei Han - Professor


B.Sc., Univ. Sci. & Tech. China;
M.S., Ph.D., Carnegie Mellon University

Council Chair, The Physical Society of Hong Kong
Council Member, Association of Asia Pacific Physical Societies (AAPPS)
Member of the IUPAP C3 Commission on Statistical Physics
Member of Steering Committee, Biophysical Society of Hong Kong and the Asian Biophysics Association
Co-Editor, EPL (IOP Science)
Fellow, American Physical Society, 2010
Editorial Board, Journal of Statistical Physics: Theory and Experiment (IOP and SISSA)
Editorial Board, Progress of Theoretical and Experimental Physics (JPS and Oxford Press)
Editorial Board, Frontiers of Physics in China
Editorial Board, Wu Li 物理 (The Chinese Physical Society Monthly Journal)


Contact: Rm. T903
Tel: (852) 3411-7031
Email: lhtang@hkbu.edu.hk
Website: Condensed Matter Theory and Biophysics

    Dr. Tang’s research combines analytical and computational approaches to explore the effect of equilibrium and nonequilibrium fluctuations on the stability of ordered structures in various physical and biophysical contexts, in particular the energetics and dynamics of defects that disrupt ordering. In recent years, he has collaborated with experimentalists on the development of quantitative tools to analyze and integrate biological data and information.

  1. Superconducting Networks, Quantum Phase Transitions, and Orbital States
    The I-V characteristics of 2D superconducting films and Josephson-junction arrays are extremely rich and susceptible to various finite-size and disorder effects. Although the basic mechanism for current dissipation in the classical regime has long been understood, various quantitative details relevant to a correct interpretation of simulation and experimental results are often missing in the literature. One of our recent work in this direction addresses a peculiar finite-size effect for an array under periodic boundary conditions that had previously led to an incorrect interpretation of simulation data. To construct the equilibrium phase diagram with random frustration, we have extended the multi-canonical Monte Carlo scheme developed by Berg to systems with continuous degrees of freedom, and have also improved the iterative determination of the sampling function. These improvements allow us to perform efficient sampling down to very low temperatures. Combined with analysis of the classical ground state, we have been able to provide a microscopic picture of the zero-temperature criticality in the 2D gauge-glass model.

    More recently, we have devoted our attention to theoretical and simulation studies of a JJ array in the quantum regime. By introducing a suitable form of random frustration, such a system may afford a “metallic phase” at zero temperature as characterized by short-range spatial order and gapless quasi-particle excitations. As such, it makes a good candidate for the observed low-temperature metallic behavior of thin superconducting films.

    Another direction we are following is the properties of quantum spin models with a large degeneracy associated with one-dimensional nematic order. Such models have been proposed to describe orbital ordering in various transition metal oxides, and more recently polar molecules in an optical lattice. The unusual degeneracy alters the excitation spectrum of the system and may allow for nonconventional topological phase transitions. We are using both analytical and numerical techniques to calculate the phase diagram of such systems.

  2. Folding and Conformational Transformation of Biopolymers
    The folding/denaturation transformation of DNA, RNA and protein molecules is a subject of lasting interest in structural biology. The physical interactions are relatively well-characterized, and hence such systems are more amenable to traditional methods of statistical mechanics. For a random DNA sequence, we have been able to provide a renormalization group description of the melting transition. In collaboration with Terry Hwa at UCSD and others, we have also investigated the formation of bubbles in the DNA double helix due to either under-twisting or heating. The problem is of interest in the quantitative modelling of gene transcription. On the RNA secondary structures, we developed Monte Carlo methods to search minimum energy base pairings where pseudoknot formation is allowed. RNA’s without pseudoknots have been shown to undergo a transition from specific to non-specific pairing, but the precise nature of the low temperature glass state and the mechanism of the transition have not been completely characterized. Our recent work on this problem indicates a novel log2L energy for “droplet” excitations and possibly a phase transition of infinite order.

    The bigger issue, however, is how to uncover the mystery hidden in protein sequences that allow them to fold into a specific shape and in a cooperative manner. Proteins, and to a lesser extent, functional RNA’s, exploit the physical and chemical properties of constituent units in unique ways to accomplish their designated biological function. In addition to studying simple models (such as the HP model on a lattice) for extracting generic behavior, we are also examining interactions (such as secondary structure propensities) in stabilizing real proteins.

    More recently, we have been interested in the development of multi-scale algorithms to study the working principles of molecular machines such as complex bacterial chemo-receptors and motors in rotary complexes driven by proton motive force. Our current focus include: i) simulational studies of the molecular assembly of receptor clusters and their dynamical fluctuations; ii) Detailed modelling of the switching kinetics of bacterial flagella motor as well as the energy efficiency of the individual stroke, taking into account fluctuations of the molecular components in an aqueous environment.

  3. Metabolic Network Organization and Regulation
    Enzyme-assisted metabolic flow is one of the best characterized molecular systems in cell biology. Its backbone is universal among nearly all living organisms while, through evolution, many add-on features have been developed to enhance the fitness of a given organism. A large percentage of cell’s transcriptional regulatory circuit is devoted to efficient channelling of resources in steady-state growth, meeting the demand under changing internal or external conditions, and protecting the cell against environmental stress. Analysis of the system-wide metabolic flow pattern under various growth conditions and correlate it with the known regulatory interactions offers the possibility of deciphering the genetic circuit from a functional perspective. There exists now a large body of genome-scale experimental data that can be used to help reconstruct the flux pattern under different growth conditions. We have recently implemented in silico growth models iJR904 for E. coli and iND750 for yeast (S. cerevisiae) developed by Palsson’s group at UCSD, which allow for calculation of biomass production under a given nutrient condition. Through detailed simulations, we are able to isolate factors controlling the efficiency of biomass production. We are working on a comparative study of simulated flux pattern and microarray data to identify regulators that are responsible for activation of alternative pathways (e.g., the aerobic/anaerobic switch in E. coli involving ArcA/B and Fnr regulons). The study of relevant design issues will deepen our understanding of biological organization.

  4. Noise propagation in biochemical pathways
    How can genetically identical cells exhibit a broad range of phenotypes and responses? The search for answers to this question, which appears to violate some of the long-held believes in textbook biology, prompted active experimental and theoretical investigation of noise in molecular networks in recent years. The research is aided by recent advances in GFP fluorescent microscopy that tracks the copy number of selected proteins in a single cell, and in flow cytometry that enables collection of large number of single-cell data for detailed statistical analysis. We develop effective and robust methodologies to analyze the rapidly accumulating experimental data to extract underlying molecular mechanisms of noise generation and propagation. Collaborations are underway with experimental groups to integrate molecular level knowledge and data for systems level modeling and analysis, and on testing the general framework in specific cases of interest, in particular the osmo-stress response pathway in yeast. Although our current focus is on quantitative description of basic biological processes in a network setting, the capabilities developed will enable us to tackle problems related to complex dynamics in human disease such as cancer, thus gaining a broader impact.

  5. Growth dynamics and pattern formation in cell populations
    Pattern formation is a fundamental process in embryogenesis and development. In a seminal paper half a century ago, Turing proposed a mechanism for spontaneous pattern formation in biological systems that involve the diffusion of two types of morphogens (“activator” and “inhibitor”) whose interaction stimulates their own synthesis. Starting from random initial perturbations, the Turing model typically generates patterns via the development of finite-wavelength dynamical instabilities in confined geometries. In collaboration with Prof. Terry Hwa at UCSD and Prof. Jiangdong Huang’s group at HKU, we are investigating pattern formation in a bacterial system where the motility of bacteria is controlled via a synthetic circuit. The work opens up a novel route to pattern formation in autonomous systems without invoking external guidance. It has been shown that the Fisher-Kolmogorov type equations that implement the density-dependent diffusivity correctly reproduce the stripe patterns seen in the experiments. However, a number of important theoretical issues have been left unanswered so far. Current theoretical work include: i) extend the steady-traveling wave analysis of the continuum growth equations to study the time evolution of an initial cell density profile and, in the striped phase, compute the period of the stripes by combining analytic and numerical methods; ii) develop multi-scale models that incorporate individual cell level behavior and analysis methods for a closer comparison with experiments; iii) study the effect of a residual chemotactic component in bacterial movement on the stripe formation process. Through our research we will be able to quantify the robustness of the newly-established pattern formation scheme. In parallel, we are investigating the growth of engineered anaerobic bacteria in a complex environment with the aim of developing bacteria-based cancer therapy.