Complex Systems

Division research focus

The research focus of the Complex Systems Lab at CSRC is the development and application of methods of statistical physics in the treatment of complex physical and biological phenomena. We are particularly interested in the construction and analysis of simple yet quantitative models that expose the key factors responsible for the system’s functionality and behavior. Current research topics include stochastic processes arising from the cell, fluctuation phenomena, molecular dynamics simulation of enzymatic reactions, molecular motors and transcriptional machinery, multiscale modeling and algorithmic development. We seek close collaboration with domestic and overseas research groups and institutions in the area of single-molecule and single cell imaging, metabolic regulation, bioinformatics, advanced computational methods and nonequilibrium statistical physics.

Division Research Areas
  • ·  Statistical mechanics of nonequilibrium, soft-matter and biological systems

    Research has been carried out on the following topics: 1) First-passage time statistics of a Brownian particle in a quenched random environment and its application tonano-particle tracking in a living cell;2) General relations between energy dissipation, linear response, and thermal fluctuations in small systems with continuous and discrete states and their applications to molecular machines that function in an open thermal environment; 3) Dynamic renormalization group treatment of the entrainment transition in the globally coupled Kuramoto model; 4) fundamental principles underlying non-equilibrium phenomena: macroscopic representation of fluctuation theorems for stochastic systems at macroscopic level, such colloidal suspensions, active matter systems; 5) the microscopic mechanism underlying swarming behavior displayed by a special kind of non-equilibrium systems: active systems where each individual propels itself, and the influence of such swarming behavior to system properties such as viscosity.

  • ·  Modeling and large-scale computational studies of proteins and molecular

    We study physical mechanisms of bimolecular systems through quantitative modeling and large scale simulations. We try to understand how biomolecules operate in interactions with one another to achieve their physiological functions. In particular, we focus on studying protein machinery such as RNA polymerases from atomistic scale to their functional level in gene transcription. We also study the functional conformational dynamics of plexin, a signaling protein that plays important roles in neural development. By computationally investigating the mechanochemical coupling and informational control of such systems, we aim to achieved a detailed understanding of how those biomolecular machines and the circuits they belong to function with high efficiency and robustness in the cellular context. The rational can help molecular engineering and support further biomedical research. Moreover, aiming to improve the simulation efficiency in our studies we work on implementing, testing, and improving the Gentlest Ascent Dynamics (GAD) algorithm to realistic biomolecular systems in the framework of all-atomic molecular dynamics simulations.

  • ·  High-performance computing platform for structural biology studies

    Collaborating with a large international consortium, we develop software for analyzing experimental data (X-ray scattering, CryoEM, and single molecule tracking), providing interface to computational modeling and simulations to reveal richer dynamics and function information of biomolecules. Research topics include: i) XFEL single particle/molecule structure determination at high resolutions; ii) Algorithms to process noisy CryoEM image data; iii) Diffusion and activity switching of receptor proteins from single-molecule fluorescence microscopy.

  • ·  Analysis and exploration of regulatory interactions in micro-organisms

    Current research in this direction focuses on two aspects: 1) genome-scale analysis and modeling of metabolic flux and proteome data to decipher the gene and pathway regulatory programs in E. coli as the growth rate and nutrient conditions are varied; 2) spatio-temporal patterning of bacterial populations upon reprogramming of cell motility circuit.

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