Supervisor: Prof. TANG, Qianyuan (Tel: (852) 3411- 2186; email: tangqy@hkbu.edu.hk)
Research Overview
Prof. Tang's research program integrates computational, statistical, and theoretical approaches to understand complex biological systems across multiple scales. By combining AI methodologies and statistical physics with biological principles, we aim to uncover fundamental laws governing biological behavior while developing quantitative tools for biomedical applications. This interdisciplinary approach enables new perspectives on longstanding questions in biology - from the origins and evolution of life to the principles of protein function - while creating practical advances in disease detection and treatment. Through the integration of physical principles with AI methodologies, we bridge the gap between basic science and translational research, contributing to both fundamental understanding of biological systems and the development of new therapeutic strategies.
Our research currently focuses on four main objectives:
- Developing high-throughput computational methods for AI-based protein dynamics prediction and analysis
- Investigating protein evolution through cross-organism comparison and proteome analysis
- Establishing novel computational frameworks for complex biological data analysis and interpretation
- Quantifying parameter sensitivities in biological networks to guide neuromodulation strategies for disease intervention
Student Opportunities
Prof. Tang is actively recruiting PhD students, research assistants, postdoctoral researchers, and visiting scholars. Candidates with strong quantitative backgrounds and interests in interdisciplinary research are particularly encouraged to apply.
This research program provides unique opportunities for students to work at the intersection of multiple scientific disciplines. Students can engage in projects that combine computational biology, AI, biophysics, and biomedical sciences, gaining expertise in modern quantitative methods while contributing to significant scientific advances. Whether your background is in physics, biology, computer science, or mathematics, the program offers opportunities to apply your skills to challenging biological problems. Students in our lab develop proficiency in cutting-edge computational techniques, learn to integrate theoretical and data-driven approaches, and work on projects with potential impact on both fundamental science and human health.
More detailed information on the core themes of the research is listed below for your reference.
Core Research Topics
1) Protein Dynamics and Evolution
Our research explores how proteins perform their functions and evolve at the molecular level. Proteins, as essential molecular machines in living systems, undergo complex shape changes to carry out their biological roles. We integrate cutting-edge AI techniques, particularly AlphaFold and protein language models, with molecular biophysics to study protein dynamics across different processes - from protein folding to binding interactions and conformational changes. By combining physical principles with AI, we develop novel methods for predicting not just static structures, but the full spectrum of protein conformations throughout their dynamics. Our research extends to analyzing patterns of protein evolution across organisms and integrating these findings with proteome-wide analyses, revealing fundamental principles about how protein structure, dynamics, and evolution are interconnected in biological systems.
2) Complex Systems Analysis
In biological systems, we investigate how multiple components interact to generate complex, emergent behaviors. Our research focuses on quantifying parameter sensitivities - understanding how changes in key system variables influence overall dynamics. We develop methods to find simpler, low-dimensional representations of these complex systems, making them more predictable and understandable. A particular focus is studying 'critical behaviors,' where systems achieve a balance between stability and flexibility, often manifesting as power laws and long-range correlations in their dynamics. Intriguingly, these principles appear not only in biological systems but also in deep neural networks, suggesting fundamental features common to complex systems across different domains.
3) AI and Deep Learning Applications
Our AI research focuses on developing and applying AI-based methods for biological data analysis. The exponential growth of AI-generated biological data (particularly from AlphaFold and protein language models) provides unprecedented coverage of the protein universe and metagenomic landscapes. These computational approaches have revolutionized our capability to explore the vast space of protein structures and sequences, offering near-comprehensive coverage of both known and predicted proteins across diverse organisms. This big data enables systematic studies at multiple biological scales while raising fundamental questions about the statistical properties and biases of AI-generated data compared to experimental observations. By integrating AI-based approaches with physical principles, we will develop theoretical frameworks and computational tools that bridge data-driven discovery with mechanistic understanding of biological systems.
4) Biomedical Applications
With our theoretical and computational approaches, we develop integrated toolsets to address crucial health challenges. We apply protein dynamics insights to enhance drug discovery, investigating therapeutic molecule-protein interactions. Our complex systems analysis reveals brain network function in health and disease, while AI methods enable large-scale analysis of biological data to identify disease signatures. This integration advances multiple areas: drug design and screening, therapeutic protein engineering, and neurological disorder treatment. Our parameter sensitivity studies inform the development of targeted neuromodulation therapies, while AI-driven approaches help identify early indicators of neurological conditions such as autism and Alzheimer's disease.
More information available at: https://sites.google.com/view/tangqy/research