Wood Lab
Chemistry Building (3rd Floor)
930 N. University Ave
Ann Arbor, MI 48109-1055

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We're located in the Chemistry Building (3rd Floor), just across N. University Avenue from the Bell Tower (above) on the main campus at the University of Michigan, Ann Arbor.





Maltas, J., and Wood, K.  Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance, bioRxiv preprint (submitted, 2018).

De Jong, M. G., and Wood, K.  Tuning Spatial Profiles of Selection Pressure to Modulate the Evolution of Drug Resistance, Physical Review Letters, 120 (23), 2018.  PDF

Yu, W., Hallinen, K., and Wood, K., Interplay between antibiotic efficacy and drug-induced lysis underlies enhanced biofilm formation at subinhibitory drug concentrations, Antimicrobial Agents and Chemotherapy, 62 (1), 2018. 
Karslake, J., Maltas, J., Brumm, P., and Wood, K., Population density modulates drug inhibition and gives rise to potential bistability of treatment outcomes for bacterial infections, PLoS Computational Biology 12 (10) (2016).
Wood, K., Wood, K.C., Nishida, S., Cluzel, P. Uncovering scaling laws to infer multi-drug response of resistant microbes and cancer cells. Cell Reports 6, 1073 (2014).

Wood, K., Nishida, S., Sontag, E.D. & Cluzel, P. Mechanism-independent method for predicting response to multidrug combinations in bacteria. P Natl Acad Sci USA 109, 12254-12259 (2012).

Wood, K. & Cluzel, P. Trade-offs between drug toxicity and benefit in the multi-antibiotic resistance system underlie optimal growth of E. coli. BMC Syst Biol 6, 48 (2012).
Wood, K., Van den Broeck C., Kawai R., Lindenberg, K., Continuous and discontinuous phase transitions and partial synchronization in stochastic three-state oscillators, Phys. Rev. E, 76, 041132, 2007.
Cao, F., Wood, K., Lindenberg, K. Noise-induced phase transitions in field-dependent relaxational dynamics: The Gaussian ansatz, Phys. Rev. E, 76, 051111, 2007.
Wood, K., Van den Broeck C., Kawai R., Lindenberg, K., Fluctuation Theorem for the Eff usion of an Ideal Gas with Momentum Transfer, Phys. Rev. E, 75, 061116, 2007.

Wood, K., Van den Broeck C., Kawai R., Lindenberg, K., Eff ects of Disorder on Synchronization of Discrete Phase-Coupled Oscillators, Phys. Rev. E, 75, 041107, 2007.

Wood, K., Van den Broeck C., Kawai R., and Lindenberg, K., Synchrony and critical behavior: Equilibrium universality in nonequilibrium stochastic oscillators, AIP Proc. 913, 49, 2007.

Wood, K., Van den Broeck C., Kawai R., Lindenberg, K., Critical Behavior and Synchronization of Discrete Stochastic Phase Coupled Oscillators, Phys. Rev. E, 74, 031113, 2006.

Wood, K., Van den Broeck C., Kawai R., Lindenberg, K., The Universality of Synchrony: Critical Behavior in a Discrete Model of Stochastic Phase Coupled Oscillators, Phys. Rev.Lett., 96, 145701, 2006.

Buceta, J., Wood, K., Lindenberg, K. , Noise-induced oscillatory behavior in field-dependent relaxational dynamics, Phys. Rev. E, 73, 042101, 2006.
Wood, K., Buceta, J., Lindenberg, K., Comprehensive Study of Pattern Formation in Relaxational Systems, Phys. Rev. E, 73, 022101, 2006.



Biological systems are often celebrated for their tremendous complexity.  For example, the dynamics of metabolic networks in bacteria, the interplay between predators and prey in a large ecosystems, and the spread of epidemics in human populations all depend on interactions between the thousands, millions, or even billions of individual “parts” that make up the system. Despite this complexity, however, the behavior of these systems is often the result of relatively simple emergent properties that are shared by a large number of statistically similar, but microscopically distinct, systems.  Motivated by emergent phenomena across physical and biological disciplines, our group applies systems level approaches to 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.  While we study a wide range of biological systems, we are especially interested in multi-drug resistance in bacteria and cancer cells.  Our goal is the development of quantitative tools to measure and predict how interactions between cells and interactions between drugs contribute to large scale behavior of the population as a whole.  Our research is highly multidisciplinary 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:

  • Evolution of microbial drug resistance in spatially heterogeneous environments;
  • Single-cell spatial architecture in cooperative biofilms;
  • Effects of coupling between population density and fitness in microbial populations;
  • The interplay between multi-drug interactions, drug cycling, and ecological dynamics in bacteria and cancer;
  • The role of network structure and critical dynamics in determining the responses of large statistical mechanical systems, including those close to and far from equilibrium, to single and combined perturbations.
  • Synchronization phenomena in self-replicating populations of coupled oscillators

Welcome to the Wood Lab at the University of Michigan!

Department of Biophysics and Department of Physics

  • Wen Paper Math

  • Screen Shot 2017-08-16 at 3.22.12 PM
  • Biofilm- Time
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  • Using theoretical tools from statistical physics, applied math, and engineering, we study biological systems ranging from intracellular metabolic networks in bacteria to populations of cancer cells.
  • We use a combination of theory, computation, and experiment in hopes of achieving a systems-level quantitative understanding of biological systems on multiple length and time scales.
  • Our lab studies population dynamics and multi-drug resistance using different experimental model systems, including bacteria such as E. coli, E. faecalis (above), and S. aureus as well as multiple types of human cancers.
  • We are interested in quantitative biology and the dynamics of complex systems, with particular emphasis on systems dominated by heterogeneity, stochasticity, strong coupling, or rare events.