I am a Ph.D. student in Systems Biology at Harvard University. My interests center on both the development and provision of affordable vaccinations and therapies against persistent infections (such as HIV and HPV) that disproportionately affect marginalized populations. I am in the labortories of Alejandro Balazs and Arup Chakraborty, both of the Ragon Institute of MGH, MIT and Harvard, and I am on a Paul and Daisy Soros Fellowship For New Americans and a National Science Foundation Graduate Research Fellowship.
I am currently focused on the following topics: stochasticity and evolution of pathogenic viruses (particularly HIV), economic evaluation of vaccinations and therapies against such viruses, synthetic biology, and open data and data analytics for healthcare services. If you want to chat with me about any of these topics, I would be more than happy to do so. Just shoot me an email at "allenlin" appended by "g.harvard.edu".
HIV is one of the fastest evolving biological systems. HIV acquires on average one mutation every three replication cycles, and the diversity of HIV strains in an infected individual approximates the global diversity of influenza. The immune system has difficulty eliminating a constantly moving target, and a practical vaccine or cure against HIV remains elusive.
Systems biology may be able to yield insights into how to design a HIV vaccine or cure. I am particularly interested in designing combinatorial vaccines and therapies. When HIV mutates to evade the immune system, it often does so at a fitness cost, lowering its ability to replicate. Can we train the immune system to launch a multi-pronged attack, such that as HIV mutates away from one immune pressure, it becomes more susceptible to the other? That is, can we "corner" HIV and limit its evolution? How can we know if multiple vaccines or therapies will act synergistically? Current treatment for HIV involves providing at least three active compounds in concert. A vaccine or cure against HIV may also require the use of a cocktail. Can we make predictions about (1) HIV's weak spots and (2) effective cocktails by computationally studying the sequences of thousands of HIV strains?
Lastly, evolution is inherently stochastic. How might we deal with chance events that occur in the evolution of HIV or in the immune system's response? Can we use this to our advantage?
Difficult-to-reach populations often bear a disproportionate burden of infectious diseases. New vaccines and therapies against these diseases are ever more expensive. Faced with limited healthcare dollars and resources, governments and public health agencies may not know if it is cost-effective to vaccinate or treat such populations. Interventions might not be cost-effective because of the difficulty of reaching these populations and the high costs of medicines. On the other hand, these interventions may be cost-effective because of the higher prevalence of these diseases in such populations. In addition, there are positive externalities to vaccinating or treating individuals against communicable, infectious diseases - protecting one person also protects other individuals that person contacts.
However, production of such evidence can be challenging. Modeling the epidemiological effects of positive externalities can be computationally intense, and data on these marginalized populations is often scarce. How can evidence be generated in such cases, and how often can we analyze and represent the uncertainty in our analyses?
I am currently working with Public Health England, which is England's public health agency, on the cost-effectiveness of vaccinating MSM against HPV. Our work formed the evidence recently reviewed by the country's vaccination decision-making board (see statement), which received attention from the press [1, 2, 3].
We live in an exciting point in time, when we know enough about biology that we can begin to try to engineer it. The emergence of biological engineering can be compared to the growth of electrical engineering from physics and chemical engineering from chemistry. Synthetic biology, a particular field in biological engineering, uses what is already known in biology to redesign or design from scratch biological systems to carry out useful functions. These systems can be used to process information, produce chemicals, or synthesize materials. Research in synthetic biology can also provide insights into the workings of and common network motifs in biological networks.
Since synthetic biology is such a new discipline, how does one go about engineering biology? Perhaps engineering concepts in other disciplines can be transferred to work with biological systems. Electrical engineers have developed the principles necessary for counters, boolean algebra, data storage, and more. Maybe these concepts can be implemented in cells. Electrical engineers also have determined methods to analyze circuits, which exist inside cells in the form of webs of protein interactions. In addition, cell processes are fundamentally made up of chemical reactions. Chemical engineers learn how to maximize the products of reactions, a skill that is useful when engineering cellular metabolic processes to produce certain compounds.
Lastly, there are many framework issues in synthetic biology that are currently unanswered. It is important to resolve the ethics of engineering organisms before such practice becomes widespread. Since synthetic biology can potentially bring about unexpected risks, it is also essential to develop regulatory guidelines in biotechnology that will minimize the dangers of bioterrorism, while allowing the field to grow with flexibility. Moreover, a legal framework for the open sharing of building blocks used to engineer biological systems needs to be developed. With these frameworks in place, synthetic biology can increase our knowledge about biological systems and also revolutionize the biotechnology industry.
My prior research profile can be found here.
The recent increase in open data released by governments (e.g. US and UK) is an exciting step towards greater accountability, transparency, and civic engagement. How can open data can stimulate innovation and, in particular, advance healthcare services? In addition, this data is difficult for the average person to interact with directly. How can current Open Data Initiatives be further improved and inspire more developers? Will such IT advances drastically change the way governments provide services to their citizens and lead to more public collaborations?
Secondly, there has been a growing buzz about the advent of "big data." How can data analytic techniques be used to advance healthcare services? The US government has recently been promoting the use of electronic health records. Digitizing healthcare information creates many opportunities for streamlining and improving the delivery of care. Now that healthcare data is starting to be centralized, can big data algorithms and machine learning techniques be used to find new insights? Can addition information collected from patients via mobile phones also be used to keep patients healthy?
Developing countries face medical challenges that more advanced developed countries have solved. Although one solution is to simply transfer technology that exists in developed countries to poorer countries, such a solution is usually ineffective in the long run. However, modifying that same technology to fit the environment and parameters presented by developing countries can significantly increase its effectiveness. What modifications in medical technology are necessary to address global health challenges?
I previously completed an M.Sc. in Public Health (with a focus on Health Economics) from the London School of Hygiene and Tropical Medicine (part of the University of London) in 2014, and an M.Phil. in Technology Policy at the Judge Business School at University of Cambridge in 2013, both funded by a Marshall Scholarship. Prior to that, I was a research technical assistant at the Weiss Lab for Synthetic Biology in the Department of Biological Engineering at MIT. I am a Class of 2011 graduate from MIT, with an M.Eng. and B.S. in Computer Science and Electrical Engineering (Course 6-2), a B.S. in Chemical-Biological Engineering (Course 10B), and minors in Political Science (Course 17, focus on globalization) and in Biomedical Engineering (Course 20). My MIT coursework is listed here.