Associate Professor
B.Sc. Honours Kinesiology, Laurentian University (1996-2000)
M.Sc. Kinesiology, University of Waterloo (2000-2002)
Ph.D. Chemical and Biological Engineering,
            University of Colorado at Boulder (2003-2008)
Postdoc, Dept of Biological Engineering, 
            Massachusetts Institute of Technology (2009-2013)

Phone: (778) 782-9777
Fax: (778) 782-3040
Office: K9632

Research interests: quantitative exercise biology

The mission of my research program is to discover ways to optimize exercise training programs for health, fitness, rehabilitation, or performance goals. A central theme of my research is applying data-driven models to different data sources, including published literature, biological experiments, and wearable sensors or tracking devices.

Accordingly, the lab's research projects fall within these three themes:

  1. Evidence-based exercise prescription
    We conduct systematic reviews and meta-analyses to quantify the effects of various training strategies on goal outcomes, with the aim of facilitating the use of scientific evidence by exercise professionals in designing training programs.

  2. Cellular adaptations to exercise-related stressors
    We investigate the biochemical signaling network underlying skeletal muscle cell adaptations to exercise by systematically exposing cultured muscle cells to exercise-related stressors, followed by proteomic measurements and computational modeling of the data.

  3. Analytics of time-series data from wearable sensors and tracking systems
    We devise improved metrics and algorithms for analyzing data from exercise- and physical-activity-monitoring devices in order to determine ways to individually optimize training programs.

Our research is interdisciplinary and is informed by the concepts and tools of exercise physiology, systems biology, and biomedical engineering. Trainees can therefore expect a unique and rigorous training environment that features a mix of experimental and computational work, which will prepare them for future scientific or technical careers in academia, industry or government.

Keywords: exercise physiology, systems biology, proteomics, cell signal transduction, individualized evidence-based exercise prescription, design of experiments, statistical models, network models, systematic review and meta-analysis


A complete up-to-date list of publications is available on PubMed.

  • Landry, B. D., Clarke, D. C., Lee, M. J. Studying Cellular Signal Transduction with OMIC Technologies. In press, Journal of Molecular Biology.
  • Skiba, P. F., Fulford, J. Clarke, D. C., Vanhatalo, A., Jones, A. M. (2015) Intramuscular determinants of the ability to recover work capacity above critical power. European Journal of Applied Physiology. 115(4): 703-13.
  • Skiba, P. F., Clarke, D., Vanhatalo, A., Jones, A. M. (2014) Validation of a novel intermittent W′ model for cycling using field data. International Journal of Sports Physiology and Performance. 9(6): 900-4.
  • Skiba, P. F., Jackman, S., Clarke, D., Vanhatalo, A., Jones, A. M. (2014) Effect of work and recovery durations on W' reconstitution during intermittent exercise. Medicine and Science in Sports and Exercise. 46(7):1433-40.
  • Leon L. R., Dineen, S., Blaha, M.D., Rodriguez-Fernandez, M., Clarke, D. C. (2013) Attenuated thermoregulatory, metabolic and liver acute phase protein response to heat stroke in TNF-receptor knockout mice. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 305(12): R1421-32.
  • Clarke, D. C., Skiba, P. F. (2013) Rationale and resources for teaching mathematical modeling of athletic training and performance. Advances in Physiology Education. 37(2): 134-152.
  • Huang, S. C., Clarke, D. C., Labadorf, A., Chouinard, C. R., Gordon, W., Lauffenburger, D. A., Fraenkel, E. (2013) Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogenically induced signaling. PLoS Computational Biology. Feb 9(2): e1002887. doi:10.1371/journal.pcbi.1002887.
  • Clarke, D. C., Morris, M. K., Lauffenburger, D. A. (2013) Normalization and statistical analysis of multiplexed bead-based immunoassay data using mixed-effects models. Molecular and Cellular Proteomics. 12(1): 245-62.
  • Clarke, D. C., Lauffenburger, D. A. (2012) Multi-pathway network analysis of human epithelial cell responses in inflammatory environments. Biochemical Society Transactions. 40(1):133-8.
  • Morris, M. K., Saez-Rodriguez, J., Clarke, D. C., Sorger, P., Lauffenburger, D. A. (2011) Training signaling pathway maps to biochemical data with constrained fuzzy logic: Quantitative analysis of liver cell responses to inflammatory stimuli. PLoS Computational Biology. 7: e1001099. doi:10.1371/journal.pcbi.1001099.
  • Clarke, D. C. and Liu, X. (2010) Measuring the absolute abundance of the Smad transcription factors using quantitative immunoblotting. Methods in Molecular Biology. 647: 357-376. DOI: 10.1007/978-1-60761-738-9_22.
  • Clarke, D. C., Brown, M. L., Erickson, R. A., Shi, Y., Liu, X. (2009) Transforming Growth Factor-β depletion is the primary determinant of Smad signaling kinetics. Molecular and Cellular Biology. 29(9): 2443-2455.
  • Clarke, D. C. and Liu, X. (2008) Decoding the quantitative nature of Transforming Growth Factor-β signaling. Trends in Cell Biology. 18(9): 430-42.
  • Zhu, S., Wang, W., Clarke, D. C., Liu, X. (2007) Activation of Mps1 promotes Transforming Growth Factor-β-independent Smad signaling. J. Biol. Chem. 282(25): 18327-18338.
  • Clarke, D. C., Betterton, M. D., Liu, X. (2006) Systems theory of Smad signalling. Systems Biology (Stevenage). 153(6): 412-424.
  • Clarke, D. C., Miskovic, D., Han, X.-X., Calles-Escandon, J., Glatz, J. F. C., Luiken, J. J. F. P., Heikkila, J. J., Bonen, A. (2004) Overexpression of plasma-membrane-associated fatty acid binding protein (FABPpm) in vivo increases fatty acid sarcolemmal transport and metabolism. Physiol. Genomics. 17: 31-37.


BPK 443 – Advanced Exercise Prescription

BPK 423 – Dynamics of Cell Homeostasis