A Computational Approach to Combatting Diseases
By Dr Raymond H. Y. Louie, IAS Junior Fellow and Research Assistant Professor of Electronic and Computer Engineering
Despite the significant advances in medical research over the past decade, infectious diseases continue to have a devastating impact on humankind, resulting in high mortality rates and financial burden. For example, despite the fact that HIV was discovered over 30 years ago, there is still no viable vaccine, resulting in over 35 million people currently living with HIV. Influenza was discovered nearly a century ago, and continues to cause high annual mortality rates. Clearly, new approaches are required to combat these deadly diseases.
The past decade has also revealed an explosion of DNA and RNA sequence data for many of the diseases which plague mankind. This revolution is due to Next Generation Sequencing technology, which has allowed rapid and cost-effective sequencing. This has created many exciting research opportunities, as this data can be analyzed to reveal potentially important properties of these diseases, including their vulnerabilities.
IAS Visiting Professor Arup Chakraborty from Massachusetts Institute of Technology has recently pioneered an approach which performs statistical analysis of HIV sequence data in order to find key vulnerabilities in the virus. This involves applying sophisticated statistical methods to construct a so-called “fitness landscape”. This landscape maps each RNA sequence to a fitness value, which represents how deadly the particular HIV virus sequence variant is. This fitness landscape can then be used for the rational design of a vaccine. The key idea is to design a vaccine which will induce an immune response to force the virus to mutate into areas of low fitness.
The IAS Focused Program on Computational and Experimental Immunology held in 2014 was the first IAS program focused on computational immunology. Prof Chakraborty was one of the speakers and delivered a lecture on "How to Hit HIV Where It Hurts".
Construction of the viral fitness landscape
Growth of influenza sequences in GenBank
There are many exciting opportunities which can build upon Prof Chakraborty’s pioneering approach to construct more accurate fitness landscapes, or to obtain fitness landscapes for different viruses which may require a totally different statistical approach than for HIV. For example, as there is a strong antibody response for influenza, its evolution is more significantly different from the evolution of HIV, and thus new statistical approaches will have to be developed for influenza.
To tackle these important challenges, Prof Matthew McKay and Prof I-Ming Hsing in the Division of Biomedical Engineering have established a Computational Immunology group at HKUST. Together with Prof Chakraborty, the group aims to utilize the multi-disciplinary skills of its group members in statistical signal processing and immunology to analyze DNA and RNA sequences for different viruses such as influenza, hepatitis C and HIV. The goal is to design effective drug and vaccines using the fitness landscape.