王冠宇

校长学者

副教授

教育背景

医学博士（德国科隆大学）

机械电子控制工程博士（浙江大学）

机械工程学士（吉林大学）

研究领域
生物医学工程；物理、定量及合成生物；生物信息及计算生物； 生物复杂性及理论生物
电子邮件
wangguanyu@cuhk.edu.cn
个人简介

王冠宇教授现任香港中文大学（深圳）医学院副教授、中国运筹学会|计算系统生物分会常务理事、中国生化及分子生物学会|分子系统生物专业委员会委员。曾担任美国德克萨斯大学休斯顿健康科学中心博士后及研究助理教授、美国莱斯大学生物工程系研究员、美国乔治华盛顿大学物理系研究助理教授、南方科技大学副教授。王教授以第一或通讯作者在SCI期刊Proc Nat Acad Sci USA, Phys Rev Letts, J Biol Chem, J Roy Soc Interface (2), iScience (3), IEEE Transactions (2), Trends Immunol, Sci Bull, Theranostics等上发表论文六十多篇；并著有《复杂疾病分析之数学视野》这一学术专著。

王教授主要致力于生物医学重大问题的研究，建立以生物实验为主要手段，以科学计算、数学分析、物理思维推理为辅的学术体系。他创造了“异病同究”的研究框架及分析方法，加深了人们对疾病本质及衰老过程的理解，为疾病间的关系和疾病的预防治疗提供了新的研究思路。最新研究范例包括对癌症、糖尿病、肥胖症等复杂疾病的整体分析及高精度定量实验；对大规模生物网络和细胞信号通路数理逻辑的解析；对生物大分子的全原子动力学模拟。

学术著作

(#共同第一作者； *通讯作者)

  1. J. Akhtar, M. Imran, and G. Wang* (2023). CRISPR/Cas9-mediated CtBP1 gene editing enhances chemosensitivity and inhibits metastatic potential in esophageal squamous cell carcinoma cells. Int J Mol Sci 24: 14030.
  2. Y. Han,  J. Akhtar, G. Liu, C. Li, and G. Wang* (2023). Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning. Comput Struct Biotechnol J 21:3478—3489.
  3. Y. Wu, S. Guan, G. Wang* (2023). Drug Effect Deep Learner Based on Graphical Convolutional Network. In: Hong, H. (eds) Machine Learning and Deep Learning in Computational Toxicology. Computational Methods in Engineering & the Sciences. Springer, Cham.
  4. J. Akhtar, Y. Han, S. Han, W. Lin, C. Cao, R. Ge, I. A. Babarinde, Q. Jia, Y. Yuan, G. Chen, Y. Zhao, R. Ye*, G. Liu*, L. Chen*, and G. Wang* (2022). Bistable insulin response: the win-win solution for glycemic control. iScience 25:105561.
  5. Z. Xiao#, H. Xu#, Z. Qu#, X. Ma, B. Huang, M. Sun, B. Wang, and G. Wang* (2022). Active ingredients of Reduning injections maintain high potency against SARS-CoV-2 variants. Chin J Integr Med 28:1-8.
  6. K. Lian, H. Feng, S. Liu, K. Wang, Q. Liu, L. Deng, G. Wang, Y. Chen, G. Liu* (2022). Insulin quantification towards early diagnosis of prediabetes/diabetes Biosensors and Bioelectronics 203:114029.
  7. F. Xing, N. Ai, S. Huang, C. Jiang, M. J. Mughal, G. Wang*, C. Deng* (2022). An In Vivo Fluorescence Resonance Energy Transfer-Based Imaging Platform for Targeted Drug Discovery and Cancer Therapy. Front Bioeng-Biotech 10: 839078.
  8. J. Mao#, J. Akhtar#, X. Zhang#, L. Sun, S. Guan, X. Li, G. Chen, J. Liu, H-N. Jeon, M-S. Kim, K-T. NO*, G. Wang* (2021). Comprehensive strategies of machine learning based quantitative structure-activity relationship models. iScience 24(9): 103052.
  9. F. Xing, Y. Liu, X. Lyu, S. Su, U. Chan, P. Wu, Y. Yan, N. Ai, J. Li, M. Zhao, B. Rajendran, J. Liu, F. Shao, H. Sun, G. Luo, W. Zhu, K. Miao, K. Luo, W. Ge, X. Xu, G. Wang*, T. Liu*, C. Deng* (2021). Three Dimensional tumor slice culture as a model to evaluate efficacy of anticancer drugs and immune checkpoint blockade for precision oncology. Theranostics 11(19):9415-9430.
  10. H. Xu#, J. Li#, S. Song#, Z. Xiao, X. Chen, B. Huang, M. Sun, G. Su, D. Zhou, G. Wang*, R. Hao*, N. Wang*  (2021). Effective inhibition of coronavirus replication by polygonum cuspidatum. Front Biosci Landmark 26(10): 789-798.
  11. G. Wang (2021). Testing the leanocentric locking-point theory by in silico partial lipectomy. Quant Biol 9(1): 73-83.
  12. H. Xu#, B. Liu#, Z. Xiao#, M. Zhou, L. Ge, F. Jia, Y. Liu, H. Jin, X. Zhu, J. Gao, J. Akhtar, B. Xiang*, K. Tan*, G. Wang* (2021). Thymoquinone blocks the entry of coronaviruses into in vitro cells. Infect Dis Ther 10(1): 483-494.
  13. G. Wang (2020). Body mass dynamics is determined by the metabolic Ohm’s law and adipocyte-autonomous fat mass homeostasis. iScience 23(6): 101176. 
  14. F. Shao, X. Lyu, K. Miao, L. Xie, H. Wang, H. Xiao, J. Li, Q. Chen, R. Ding, P. Chen, F. Xing, X. Zhang, G. Luo, W. Zhu, G. Cheng, N. Lon, S. Martin, G. Wang, G. Chen, Y. Dai, C. Deng* (2020). Enhanced protein damage clearance induces broad drug resistance in multi-type of cancers revealed by an evolution drug resistant model and genome-wide siRNA screening. Adv Sci 7: 2001914.
  15. Z. Jiang#, L. Lu#, Y. Liu, S. Zhang, S. Li, G. Wang*, P. Wang*, and L. Chen* (2020). SMAD7 and SERPINE1 as novel dynamic network biomarkers detect and regulate the tipping point of TGF-beta induced EMT. Sci Bull 65(10): 842.
  16. T. Wei#, H. Wang#, X. Wu, Y. Lu, S. Guan, F. Dong, C. Dong, G. Zhu, Y. Bao, J. Zhang*, G. Wang*, and H. Li* (2020). In silico screening of potential spike glycoprotein inhibitors of SARS-CoV-2 with drug repurposing strategy. Chin J Integr Med 26(9): 663. 
  17. P. Zheng*, J. Kros, and G. Wang* (2019). Elusive neurotoxicity in T cell-boosting anticancer therapies. Trends Immunol 40(4): 274. 
  18. L. Sun#, X. Li#, J. Pan, J. Mao, Y. Yuan, D. Wang, W. Sun*, G. Krueger*, and G. Wang* (2019). Seeking mTORC1 inhibitors through molecular dynamics simulation of arginine analogs inhibiting CASTOR1. Cancer Genomics Proteomics 16(6): 465.
  19. Y. Wu and G. Wang* (2018). Machine learning based toxicity prediction: from chemical structural description to transcriptome analysis. Int J Mol Sci 19(8): 2358.  
  20. G. Wang (2017). Global quantitative biology can illuminate ontological connections between diseases. Quant Biol 5(2): 191.   
  21. Y. Wang, W. Cai, L. Chen*, G. Wang* (2017). Molecular dynamics simulation reveals how phosphorylation of tyrosine 26 of phosphoglycerate mutase 1 upregulates glycolysis and promotes tumor growth. Oncotarget 8: 12093.
  22. F. Xing, Q. Zhan, Y. He, J. Cui, S. He*, G. Wang* (2016). 1800MHz microwave induces p53 and p53-mediated caspase-3 activation leading to cell apoptosis in vitro. PLoS ONE 11(9): e0163935.
  23. G. Wang (2016). Chemoinformatics in the new era: from molecular dynamics to systems dynamics. Molecules 21(3): 71.   
  24. G. Wang* and M. Zhang (2016). Tunable ultrasensitivity: functional decoupling and biological insights. Sci Rep 6：srep20345.   
  25. Y. Wu, G. Krueger, and G. Wang* (2016). Altered micro-RNA degradation promotes tumor heterogeneity: a result from Boolean network modeling. Anticancer Res 36(2): 575.  
  26. H. Chen, G. Wang, R. Simha, C. Zeng* (2016). Boolean models of biological processes explain cascade-like behavior. Sci Rep 7：srep20067.  
  27. X. Shen, T. Huang, G. Wang*, and G. Li* (2015). How the sequence of a gene specifies structural symmetry in proteins. PLoS ONE 10(12): e0144473.  
  28. M. Zhao, Y. Cai, J. He, G. Wang, D. Wei, Y. Wei, C. Zhang, F. Zhou* (2015).  Personalized clinical data screening: special issue on health informatics. Computers Biol Med 61:161.  
  29. T. Li and G. Wang* (2014). Computer aided targeting of the PI3K/Akt/mTOR pathway: toxicity reduction and therapeutic opportunities. Int J Mol Sci 15:18856.  
  30. G. Wang (2014).  Analysis of Complex Diseases: A Mathematical Perspective. CRC press. 
  31. G. Wang (2014). Raison d’être of insulin resistance: the adjustable threshold hypothesis. J R Soc Interface 11(101): 20140892.  
  32. G. Wang (2012). Optimal homeostasis necessitates bistable control. J R Soc Interface 9: 2723.   
  33. G. Wang, Y. Rong, C. Hao, C. Pearson, C. Du, R. Simha, C. Zeng* (2012). Process-driven inference of biological network structure: feasibility, minimality, and multiplicity, PLoS ONE 7(7): e40330.  
  34. Y. Rong, C. Zeng, C. Evans, H. Chen, G. Wang (2011). Topology and dynamics of Boolean networks with strong inhibition, Discrete and Continuous Dynamical Systems S 4: 1565.  
  35. G. Wang (2010). Singularity analysis of the AKT signaling pathway reveals connections between cancer and metabolic diseases. Phys Biol 7: 046015.  
  36. G. Wang,  C. Du, H. Chen, R. Simha, Y. Rong, Y. Xiao, and C. Zeng* (2010). Process-based network decomposition reveals backbone motif structure. Proc Natl Acad Sci USA 107(23): 10478.  
  37. G. Wang* and G. Krueger (2010). Computational Analysis of mTOR Signaling Pathway: Bifurcation, Carcinogenesis, and Drug Discovery. Anticancer Res 30(7): 2683.  
  38. M. Brandt, G. Krueger, and G. Wang (2008). A biodynamical model of human T-cell development and pathology: design, testing and validation. In H. E. Kaiser and A. Nasir, Selected Aspects of Cancer Progression: Metastasis, Apoptosis and Immune Response, pp. 223-246, Springer.   
  39. Y. Yu, G. Wang, R. Simha, W. Peng, F. Turano, and C. Zeng* (2007). Pathway switching explains the sharp response characteristic of hypoxia response network. PLoS Comput Biol 3(8):1657.  
  40. G. Wang (2007). Estimation of the proliferation and maturation functions in a physiologically structured model of thymocyte development. J Math Biol 54(6):761.  
  41. G. Wang* and M. W. Deem* (2006). Physical theory of the competition that allows HIV to escape from the immune system. Phys Rev Lett 97: 188106.
  42. G. Wang* and G. Krueger (2006). Computational simulation of HHV6 infection. In G. Krueger and D. V. Ablashi (eds), 2nd eds, Human Herpesvirus-6: General Virology, Epidemiology and Clinical Pathology, vol 12, pp. 323-335, Elsevier.  
  43. G. Wang (2004). Parameter optimization in large-scale dynamical systems: a method of contractive mapping. Math Comput Simul 66(6): 565.  
  44. G. Wang (2004). The effects of affinity mediated clonal expansion of premigrant thymocytes on the periphery T cell repertoire. Math Biosci Eng 2(1): 153.  
  45. G. Wang*, G. Krueger, and L. Buja (2004). Continuous model studying T cell differentiation and lymphomagenesis and its distinction with discrete models. Anticancer Res 24(3): 1813.  
  46. G. Wang* and G. Krueger (2004). A general mathematical method for investigating the thymic microenvironment, thymocyte development, and immunopathogenesis. Math Biosci Eng 1(2): 289.   
  47. G. Wang*, G. Krueger, and L. Buja (2004). A simple model to simulate cellular changes in the T cell system following HIV-1 infection. Anticancer Res 24(3): 1689.  
  48. G. Krueger*, M. Brandt, G. Wang, and L. Buja (2004). Computational simulation of chronic persistent virus infection: factors determining differences in clinical outcome of HHV-6, HIV-1 and HTLV-1 infections including aplastic, hyperplastic and neoplastic response. Anticancer Res 24:187.  
  49. M. Brandt*, G. Krueger, G. Wang, L. Buja (2004). Feed-forward and feedback mechanisms in a dynamical model of human T cell development and regulation. In Vivo 18: 465.  
  50. M. Brandt*, G. Wang, H. T. Shih (2004). Feedback Control of a Nonlinear Dual Oscillator Heartbeat Model. In: G. Chen, D. J. Hill, X. Yu (eds) Bifurcation Control. Lecture Notes in Control and Information Science, vol 293, Springer, Berlin, Heidelberg.
  51. G. Wang* and S. He (2003). A Quantitative Study on Detection and Estimation of Weak Signals by Using Chaotic Duffing Oscillators. IEEE Trans Circuits Syst I, Fundam Theory 50(7): 945.  
  52. G. Wang*, G. Krueger, and L. Buja (2003). A simplified and comprehensive computational model to study the behavior of T cell populations in the thymus during normal maturation and in infection with mouse Moloney leukemiavirus. In Vivo 17(3): 225.  
  53. G. Krueger*, M. Brandt, G. Wang, and L. Buja (2003). TCM-1: a nonlinear dynamical computational model to simulate cellular changes in the T cell system: conceptional design and validation. Anticancer Res 23(2): 123.  
  54. G. Krueger*, G. Marshall, L. Buja, H. Schroeder, M. Brandt, G. Wang and U. Junker (2002). Growth factors, cytokines, chemokines and neuropeptides in the modeling of T-cells. In Vivo 16: 365.  
  55. G. Krueger*, M. Brandt, G. Wang, F. Berthold, and L. Buja (2002). A computational analysis of Canale-Smith syndrome: chronic lymphadenopathy simulating malignant lymphoma. Anticancer Res 22: 2365.  
  56. G. Wang*, G. Krueger and L. Buja (2003). A mathematical model to simulate the cellular dynamics of infection with Human Herpesvirus-6 in EBV negative infectious mononucleosis.  J Med Virol 71(4): 569.  
  57. G. Wang*, W. Zheng, and S. He (2002). Estimation of amplitude and phase of a weak signal by using the property of sensitive dependence on initial conditions of a nonlinear oscillator. Signal Processing 82: 103.  
  58. G. Krueger*, M. Brandt, G. Wang and L. Buja (2002). Dynamics of HTLV-1 leukemogenesis: data acquisition for computer modeling. In Vivo 16: 87.  
  59. G. Krueger*, B. Koch, J. Weldner, G. Tymister, A. Ramon, M. Brandt, G. Wang and L. Buja (2001). Dynamics of Active Progressive Infection with HIV1: Data Acquisition for Computer Modeling. In Vivo 15: 513.  
  60. G. Krueger*, B. Koch, A. Hoffmann, J. Rojo, M. Brandt, G. Wang and L. Buja (2001). Dynamics of chronic active herpesvirus-6 infection in patients with chronic fatigue syndrome: data acquisition for computer modeling. In Vivo 15(6): 461-466.  
  61. G. Krueger*, G. Bertram, A. Ramon, B. Koch, D. Ablashi, M. Brandt, G. Wang and L. Buja (2001). Dynamics of infection with Human Herpesvirus-6 in EBV-negative infectious mononucleosis: data acquisition for computer modeling. In Vivo 15(5): 373.  
  62. G.Wang*, D. Chen, J. Lin and X. Chen (1999). The application of chaotic oscillators to weak signal processing. IEEE T Ind Electron 46(2): 440.  
  63. G. Wang*, D. Chen, X. Chen and J. Lin (1998). The statistical characteristic of weak signal detection by Duffing oscillators. Acta Electronica Sinica 26(10): 38.   
  64. G. Wang*, G. Tao, X. Chen and J. Lin (1997). Signal detection by chaotic oscillators with intensive background noise. Chin J Sci Instru 18(2): 209.  
  65. G. Wang*, X. Chen, and J. Lin (1996). The application of ultrasonic imaging in robotics. Robot 18(2): 122.