Kevin Wood

Statistical mechanics offers an elegant description of physical systems whose macroscopic properties arise, at least in part, from mathematical laws governing large ensembles. The picture that emerges is profound: the collective behaviors of many complex systems do not depend on microscopic intricacies but instead on statistical properties shared by a large number of systems.  Research in the Wood group focuses on the development and application of similar approaches for the study of living systems, where biologically-relevant dynamics—for example, the evolution of drug-resistance in a population of cancer cells—emerge from interactions and competition between a large number of individual components. Using both theoretical and experimental tools, we study a wide range of biological systems, with particular emphasis on systems dominated by heterogeneities, non-equilibrium dynamics, strong interactions with the environment, or rare events. Our goal is to develop a quantitative physical understanding that can be used to predict, and in some cases, control these biological systems. We are also interested in exploring when, and why, statistical or dynamical properties lead to universal behaviors common to entire classes of living systems. Our research is highly multi-disciplinary and combines theoretical tools from physics, engineering, applied math, and computer science with experimental approaches from molecular biology, genetics, microbiology, and cancer biology.

Topics of current interest include:

• The role of environmental dynamics in shaping and potentially controlling the distribution of phenotypes in bacterial populations
• The interplay between multi-drug interactions and ecological dynamics in bacteria (with S.aureus and E.faecalis as model systems) and human cancer cells (with melanoma as a model system)
• The role of network structure and critical dynamics in determining the response of large statistical mechanical systems, including those close to and far from equilibrium, to single and combined perturbations.
• The effect of network topology on the response of genome-scale metabolic networks to combined changes in environmental conditions (such as nutrient availability) and intracellular network dynamics (such as flux through a particular pathway).
• Synchronization phenomena in self-replicating populations of coupled oscillators

Research Areas(s)

• Complex Biosystems
• Computational Biophysics
• Spectroscopy and Microscopy

Field(s) of Study

• Theoretical and Computational Biophysics
  
• Statistical Physics and Complex Systems
  
• Experimental Microbial Ecology
  
• Cancer Biology and Multi-Drug Resistance