Our Research Area
Please refer to the following summer research study topics. You may also click here check other research area.
Topic: Single cell dynamics in response to anticancer drugs: a comparison of 2D vs. 3D
Supervisor: Dr Jade Shi (email@example.com)
Increasing evidence indicates that the dynamics of key signaling pathways play a crucial role in regulating cellular responses to various environmental stimuli; however, there is still a large gap in our understanding of how complex dynamical responses are modulated and how they vary between individual cells and thus lead to alternative cell fate. For instance, cell-to-cell variation in anticancer drug responses is well recognized to be a great challenge that hinders the development of effective anticancer regimen for a broad patient population. This problem is further complicated by the fact that most of the mechanistic data on anticancer drug response were obtained using 2D cultured cancer cells, which poorly mimic the physiology and pathology of 3D solid tumors. By combining highly quantitative single-cell microscopy assays with computational analysis, this research project aims to identify points of variation in drug response dynamics between 2D and the more realistic 3D cancer models, explore quantitative mechanism by which drug response is regulated by differential pathway dynamics in 2D vs. 3D, and determine how this varies between individual cells. The project brings together highly interdisciplinary methodologies and techniques in cell biology, cancer biology, live-cell imaging and computational biology. The results shall shed important new light on mechanism underlying drug response variability and reveal critical factors that control drug response in 3D, providing novel targets to develop new drugs and drug combinations.
Topic: Predicting immunomodulating drug target by network-based analysis of drug-induced genome-wide expression dynamics
Supervisor: Dr Jade Shi (firstname.lastname@example.org)
Modern drug discovery has so far shown disappointingly low success rate, despite large investment of time and money from both the pharmaceutical industry and governments. Clearly in need are alternatives to the commonly adopted drug development pipeline, which generally starts with biochemical or cell-based screens of known drug targets, followed by preclinical animal model studies and then clinical trials. One of the possible alternatives is re-development of traditional Chinese herbal medicine that have demonstrated clinical efficacy in human, but lack preclinical evidence of their bio-activity as well as cellular and molecular data for their mechanism of action (MOA), thus hindering their application in broad patient population. Pinpointing the MOAs of herbal medicine is challenging, as it is complicated by the fact that the active chemical components in a herb or herbal combination are often unknown and a multi-component, multi-target drug mechanism may be involved, making it difficult to design suitable biochemical and cell-based assays to quantify the complex medicinal effects of herbal medicine at the system level. This research project thus aims to develop a new network-based approach to investigate and predict the immunomodulating drug targets and mechanisms of herbal medicine with unknown active components, by analyzing genome-wide expression altered by herbal medicine at the system level. Specifically, the genome-wide expression data would be first projected onto a functional network model for immune cell activation, based on which network signatures specific to distinct target perturbations of the network would be defined and then applied to predict immunomodulating drug targets of herbal medicine. Once successfully developed, this network-based approach will not only provide a novel paradigm to decipher the immunomodulating mechanisms of herbal medicine but also reveal new drugs and druggable targets to activate immune response for disease treatment, such as cancer and infection.
Topic: Simulating Biomedical Sciences with Computers
Supervisor: Dr. WONG, Kin-Yiu (email@example.com
Many drugs used in medicine are designed as inhibitors of some protein enzymes. For example, a popular anti-flu drug, Tamiflu (特敏福) is designed as an inhibitor of neuraminidase (i.e., the “N” of H1N1 and H5N1 viruses). Another example is the well-known drug Viagra® (偉哥/威而綱) is designed as an inhibitor of phosphodiesterase (PDE) for a treatment of erectile dysfunction. PDE is a protein enzyme that functions as an important terminator in signal-transduction pathways. Signal transduction is critical to the metabolism of our body. Indeed, at least five Nobel Prizes (in Physiology or Medicine) have been awarded for it.
On the other hand, applications of quantum mechanics in chemistry and in molecular biochemistry/medicine with help of computer have been recognized by the two Chemistry Nobel Prizes in 1998 and 2013, respectively.
In this project, by collaborating closely with the prestigious experimental group of Prof. Michael E. Harris in USA, we would like to compute (quantum) isotope effects on PDE and to check whether we can predict experimental values. Computing the (quantum) isotope effects could in turn have a profound impact on the rational drug design, by manipulating terminations of signal responses.
Through this summer project, students would learn the interplay between medicine and fundamental sciences (e.g., Physics, Chemistry, Biology, and Mathematics). Moreover, they would have hands-on experience in using some popular software on a supercomputing machine at HKBU. In addition, they would have a chance to study the famous legacy of Richard Feynman, i.e., Feynman’s path integral (Feynman was a 1965 Nobel Prize Winner in Physics). For instance, students would use Feynman’s path integral to compute some internuclear quantum-statistical properties for the protein-enzyme phosphodiesterase (PDE), such as quantum tunneling, zero-point motion, and isotope effects.
More details can be found at Dr. Wong’s research group website:
Topic: Dynamics and Complexity of Excitation-Inhibition balanced Neural Networks
Supervisor: Dr. Changsong Zhou (firstname.lastname@example.org)
Biological neural networks display complex dynamics patterns and high complexity as measured by multi-scale entropy (MSE). This summer study will guide student to study the emergence of complex dynamical patterns in network of coupled excitatory and inhibitory neurons and measure MSE for different dynamical regimes of sub-critical, critical and super-critical states. Through the project, the students will get familiar with basics of dynamical systems, neural modeling, computer programing and numerical simulations, which are essential skillsets for future studies in computational neuroscience.