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Supervisor: Dr TIAN, Liang  (Tel: (852) 3411-7030; email: )

Postgraduate Fellowship in the Statistical Physics of Complex Systems

Theoretical Biology Research Laboratory
Department of Physics, Hong Kong Baptist University

The Theoretical Biology Research Laboratory (TBRL) at the Department of Physics, Hong Kong Baptist University is looking for Ph.D. candidates in the area of systems biology and biophysics, machine-learning and bioinformatics, non-equilibrium statistical physics, and network science.

The research will be done under the mentorship of Dr. Liang Tian and Prof. Leihan Tang, with opportunities for collaboration with experimental and computational research groups in Hong Kong and mainland China such as Beijing Computational Science Research Center, Institute of Synthetic Biology/SIAT, Songshan Lake Materials Laboratory, and so on. 

Current projects in the lab include:
Human Microbiome and Community Ecology
Human-associated microbes form a very complex and dynamic ecosystem, which can be altered by drastic diet change, medical interventions, and many other factors. The alterability of our microbiome offers a promising future for a variety of microbiome-based therapies such as ingesting probiotics or prebiotics, and fecal microbiota transplantation, in treating diseases associated with disrupted microbiota. Despite successful cases under each strategy, we still lack sufficient understanding of which strategy works best for a given individual, and whether there are long-term safety issues. Indeed, the complex topology and dynamics of the ecological network underlying the human gut microbiota render quantitative study of effects of external interventions extremely difficult. The future of microbiome-based therapies will be bright only if we fully understand the structure and dynamics of our gut microbial ecosystems. Our long-term objective is to construct a modeling framework to better capture the community ecology and dynamics to inform microbiome-based therapies. 

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 many add-on features have been incorporated to enhance the fitness of an organism in its natural habitat. Furthermore, dynamic reshaping of the proteome is required for efficient channeling of resources and protecting the cell against environmental stress. 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). There are also ongoing collaborative projects on metabolic analysis related to symbiosis and disease. The study of various design issues under relevant physical and biochemical constraints will deepen our understanding of biological organization. 

Bioinformatics and Machine Learning
The exponential growth of the amount of biological data available today prompts us to adopt and develop machine techniques to transform all these heterogeneous data into biological knowledge and testable models. We are generally interested in multi-dimensional biological data analysis using various machine learning techniques, such as hidden Markov modeling, network-based clustering, Bayesian network, consensus clustering, echo state networks, and so on. Recently, we focus on exploring the impact of the structure of artificial neural networks on their performance.

Self-organization in Active Systems 
Self-organization is wide spread in biology but its underlying principles have not been well understood. In our recent study of collective oscillations in cell populations, adaptive response in cell-to-cell communication emerged as the root cause of collective behavior. Active processes inside individual cells enable a form of energy outflow and collective motion ruled out by the fluctuation-dissipation theorem of equilibrium statistical mechanics. Experiments on bacterial suspensions have revealed interesting rheological properties of so called “active gels” that include spontaneous flow and other dynamical patterns. The proposed study will take advantage of the extensive literature on active fluids at the continuum level and supplement it with behavioral analysis of individual cells and the associated energetics, both from models and from experimental data. The aim is to advance our current understanding of self-organization in biological systems, particularly from the point of view of adaptation.

Complex Networks: Structure and Dynamics
We are interested in the intricate interplay between the structure and dynamics of complex networks. In particular, using tools from statistical physics and graph theory, we studied various percolation transitions on complex networks, revealing their implications in dynamical processes on networked systems. We are also interested in application and development of modern statistical physics techniques and methodologies for data processing and network reconstruction, such as spectral methods in time-series data analysis and dimension reduction. Progress in this direction will improve statistical physics models across different time scales, extract universal properties, and explore new ideas towards relationships between network structure and dynamics, providing the theoretical underpinning for the other projects. 

Eligibility: The ideal candidate is expected to have a background in one or more of the following fields: physics, biology, computer science, or mathematics. Excellent organizational and interpersonal skills, along with a stated interest in scientific research, are essential. Computer programming and analytic experience with large datasets is a plus.

Application: All interested candidates should submit an application consisting of (i) current Curriculum Vitae, (ii) brief statement of research experience and interests, and (iii) transcripts (as one single PDF file) to or