Allen Lin

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About Me

I am currently a bioinformatics scientist at Regeneron, focused on advancing genetic medicines. My training spans computer science, systems and synthetic biology, health economics, and technology policy. Below are some of my previous academic interests, centered around the development and provision of affordable vaccinations and therapies against viral infections.

Understanding HIV Dynamics to Create Vaccines and Therapies

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?

Epidemiological Modeling and Economic Evaluation of Vaccines and Therapies against Infectious Diseases

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].

Synthetic Biology

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.


Academic Training

My Ph.D. is in Systems, Synthetic, and Quantitative Biology at Harvard University in the laboratories of Alejandro Balazs and Arup Chakraborty, both of the Ragon Institute of MGH, MIT and Harvard, funded by a Paul and Daisy Soros Fellowship For New Americans and a National Science Foundation Graduate Research Fellowship. Prior to my Ph.D., I 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), and an M.Phil. in Technology Policy at the Judge Business School at University of Cambridge, 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 graduated 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.

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