摘要12-郑鸣

Abstract of Mings Annual Biophysical ProgramsM. Mocarlo Zheng (郑鸣, 12, undergraduate, School of Physics)Identifying Network Topologies That Can Generate Turing Pattern M. Mocarlo Zheng1, Bin Shao1,2, Qi Ouyang (欧阳颀)1,2,* 1 The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing 100871, China2 The Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China These authors contributed equally to this work. * E-mail: Turing pattern provides a paradigm of non-equilibrated self-organization in reaction- diffusion systems. On the basis of many mathematical studies, it has been proposed that many biological pattern formation processes use Turing instability to achieve periodic patterns. In this paper, we introduce a framework to systematic identify network topologies that are capable for Turing pattern formation. All possible 2,3-node genetic regulatory networks are enumerated and bifurcation analysis is applied to access their ability to generate Turing instability. We find that there are two mechanisms which can lead to Turing instability with different phase difference between 2 morphogens, and all 3-node networks that can achieve Turing pattern can be classified into either mechanism. We also find that the classical activator-inhibitor system is the most robust mechanism for Turing pattern generation, and appropriate involvement of a third node can further increase the performance of the circuit by introducing another core topology or enhancing existing regulations. Our results provide the design principle of robust Turing pattern circuits and can be further applied for exploring other bifurcation phenomena systematically. Biplane Spatial Covariance Reconstructive (bpSCORE) Nanoscopy Achieves Ultrafast High-Density 3D Structure Identification M. Mocarlo Zheng1,2, Benjamin P. Bratton3,4, Yujie Sun (孙育杰)1,*, Joshua W. Shaevitz4,5,* 1 Biodynamic Optical Imaging Center (BIOPIC), Peking University, Beijing 100871, China2 School of Physics, Peking University, Beijing 100871, China3 Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA 4 Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA 5 Department of Physics, Princeton University, Princeton, NJ 08544, USA * Correspondence: sun_ or shaevitzPrinceton.EDU Principle component analysis (PCA) provides a paradigm of revealing the internal structure of the data in signal processing. On the basis of independence of fluctuation, it has been proposed that spatial covariance reconstructive (SCORE) super-resolution microscopy successfully utilizes PCA for two-dimensional (2D) imaging analysis. In this paper, we demonstrate a framework of expanding SCORE to three-dimensional (3D) analysis via biplane (BP). We achieve lateral resolution of 50nm and axial resolution of 100nm within seconds. We also develop another optimization strategy for processing dataset of low signal-noise ratio (SNR) and short image stack. Our results provide the principle of 3D continuous-structure-based reconstruction and can be further applied for live-cell imaging and dynamic-process capturing. Nanometer-scale RNA polymerase II clustering in- side live-cell nucleus revealed by Bayesian nanoscopy Xuanze Chen1,2, Mian Wei1, M. Mocarlo Zheng1,3, Peng Xi2,*, Yujie Sun (孙育杰)1,* 1 State Key Laboratory of Biomembrane and Membrane Biotechnology, Biodynamic Optical Imaging Center (BIOPIC), School of Life Sciences, Peking University, Beijing 100871, China2 Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China 3 School of Physics, Peking University, Beijing 100871, China These authors contributed equally to this work. Correspondence: sun_ Spatial and temporal clustering of RNA polymerase II (Pol II) plays an important role in gene regulation. However, the assembly and disassembly of Pol II clusters in live- cell nucleus have not been observed directly yet. In this paper, we report a Bayesian nanoscocpy with 4s temporal resolution and 50 nm spatial resolution, which enables direct observation and dynamic analysis of the Pol II clustering process. In addition, DBSCAN analysis on the Bayesian super-