[ 3D surface image ]

We use mathematical models to understand biological and social processes. We draw upon tools from a number of disciplines, including game theory, network theory, information theory, stochastic processes, and dynamical systems. Everything we do is collaborative; working with other groups provides the perspective, excitement, and motivation to do our best work. While the array of projects in the lab is ever evolving, a few representative examples are provided below.

  1. Dynamics of emerging infectious diseases.

    Emerging infectious diseases such as SARS and, potentially, avian influenza represent a major public health threat. If we can understand the ecological and evolutionary processes by which these processes emerge from animal reserviors into human populations, we may be able to intervene to halt the spread of an emerging disease in its earliest phases, either (a) before the onset of significant human-to-human transmission or (b) early in the spread of the disease. In collaboration with the Lipsitch Lab at the Harvard School of Public Health, we are developing mathematical models to help guide prevention and response policies, and statistical methods that will allow us to respond in real time during initial onset of an newly emerging disease.

    See: Antia et al. (2003), Mills et al. (2006). Also: Pitzer et al. (2007), Bergstrom et al. (2004), Lipsitch and Bergstrom (2004).

  2. Risk, information, and evolution.

    Organisms have to deal with variable environments; to do so, they acquire information when possible and hedge their bets against uncertainty when necessary. Optimal strategies for acquiring information (or hedging bets in the absence of information) are shaped both by the scale of the uncertainty and by the presence or absence of correlations in the conditions that individuals experience or cues that they observe. Our theoretical work on the subject, conducted with Michael Lachmann's group at the Max Planck in Leipzig, Germany, is revealing intriguing connections between information theory and evolutionary biology. Closer to home, we are working with Ben Kerr's group at the UW to test these ideas directly by means of experimental evolution studies in a microfluidic system.

    See: Donaldson-Matasci et al (2008), Bergstrom and Rosvall (2008), Bergstrom and Lachmann (2005).

  3. The structure and economics of scientific publishing.

    A healthy academic community requires a vibrant system of scholarly communication - but the growing influence of large commercial publishers has made it increasingly difficult for universities to maintain their journal collections. The academic community as a whole suffers, and the authors, reviewers, and editorial board members who give their labor freely to the commercial publishers become unwitting accessories. Working with UC Santa Barbara economist Ted Bergstrom (who also happens to be my father), we have for several years studied the economics of scholarly publication. More recently, we have also started to apply network theory to problems in bibliometrics, with a particular focus on ranking journals and on mapping the structure of science.

    See: Eigenfactor.org, Rosvall and Bergstrom (2008), Bergstrom (2007), Bergstrom and Bergstrom (2006). Also: Bergstrom and Bergstrom (2001), (2004a), (2004b), Althouse et al. (2008)

  4. The evolution of immune systems.

    We study the role of information in the coevolutionary struggle between hosts who evolve immune systems and pathogens who evolve to avoid these systems. Hosts deploy sophisticated immune mechanisms to detect and respond to pathogen challenge. Pathogens - which can evolve many thousands of times faster than their hosts - devise strategems by which to deceive their hosts, in order to avoid detection and to exploit the host's metabolic processes. In collaboration with the Antia lab at Emory, we are developing models that help us understand how the information war between host and pathogen plays out.

    See: Bergstrom and Antia (2006). Also: Antia et al. (2003), Begrstrom and Antia (2005), Bergstrom (2008).

  5. Cellular population epigenetics.

    Epigenetic markings such as cytosine methylation play an important role in regulating gene expression. My colleague Charles Laird and his group have developed a set of molecular genetic techniques for assaying these epigenetic markers. In collaboration with the Laird lab here at the UW, we are adapting mathematical approaches that population geneticists use to understand patterns of individual genetic variation within and between populations, to help us understand patterns of cellular epigenetic variation within and between individuals.

    See: Genereux et al. (2005). Also: Ackermann et al. (2007).