Modelling microbial evolution: predicting evolutionary escape

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Microbes, such as bacteria and viruses, are critical to both human health and disease, underpin the health of our lakes, oceans and forests, and are also foundational to agriculture and global nutrient cycling. With large population sizes and short generation times, microbes evolve very quickly; this is repeatedly observed as bacteria evolve resistance to each new antibiotic. My research group uses mathematical models to predict how microbes evolve. We verify our mathematical work by using computer simulations of evolving microbial populations. We also work closely with experimental scientists to better understand the data obtained when microbes evolve in controlled laboratory settings. Whenever microbial growth is held in check - by the human immune system, by medical intervention, or by other microbes - evolution typically circumvents the restriction, such that an adapted microbial population recovers and grows; this recovery through adaptation is called "evolutionary escape". My goal is to develop models and simulations that allow us to better predict evolutionary escape in three situations: 1. Escape from Immunity. If enough individuals in a population are immune to an infectious microbe, the infection will not spread. Unfortunately, the microbe can evolve to avoid recognition by the immune system; this is why it is difficult to design the influenza vaccine. In previous work we found that in most situations, this evolutionary escape is delayed if the population is immune to the microbe in many different ways. We have recently discovered, however, situations in which immune diversity makes escape more likely. My goal is to use mathematical models to identify these unexpected and potentially dangerous situations in which diversity promotes escape. 2. Escape from Sequence-Specific Therapy. An exciting new class of antibiotic therapies is emerging that can be programmed to target specific gene sequences. This will allow us to kill, for example, only the harmful bacteria in an infection, or only the antibiotic-resistant bacteria in the environment. Unfortunately, as with previous antibiotics, evolutionary escape is inevitable. My goal is to predict the expected rate of escape from sequence-specific agents; this work will be a critical step in the development of this new class of therapy. 3. Co-evolutionary Escape. Infectious microbes and the populations they infect can co-evolve in a series of evolutionary escapes: the "host" population escapes the microbe by developing immunity; the microbe co-evolves to escape immune pressure. This situation is made more complex by gene sequences that can move between microbes and their hosts. Using bacteria and viruses as a model system, I plan to use evolutionary models to predict the fate of gene sequences that move between microbes and their hosts as they co-evolve. Ultimately, this work will help us understand and explain the 8% of the human genome that we have adopted from the viruses that infect us.