Dr. David C. Clarke

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

Lab website:

Research interests: quantitative exercise biology

The mission of my research program is to build predictive models of exercise-training adaptations to improve fundamental understanding of exercise biology and to optimize exercise training programs for health, fitness, rehabilitation or performance goals.

The lab's research is organized into the following themes:

  1. Exercise-responsive cell signaling
    We study how exercise duration and intensity are encoded as cell signaling network dynamics using computational modeling and cell-culture-based experimental techniques.
  2. Sports analytics and modeling of wearable-sensor data
    We devise improved metrics and algorithms for analyzing data from portable exercise- and physical-activity-monitoring devices in order to determine ways to individually optimize training programs.
  3. Evidence-based exercise programming
    We conduct systematic reviews and meta-analyses of published data to create tools that facilitate evidence-based exercise programming.
  4. Clinical exercise physiology
    Student interests and requests for collaboration have motivated us to pursue several clinically relevant exercise physiology projects, such as mitochondrial disease, congenital heart disease, and relative energy deficiency in sport.

Our research is interdisciplinary and is informed by the concepts and tools of exercise physiology, systems biology, and biomedical engineering. Each project typically involves the development, validation, and application of mathematical models.

Keywords: exercise physiology, training programming, cell signal transduction, mathematical modeling, statistical modeling, systems biology, proteomics, sports analytics


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

  • Coccimiglio, I. F., Clarke, D. C. (2020) ADP is the dominant controller of AMP-activated protein kinase activity dynamics in skeletal muscle during exercise. PLoS Computational Biology. 16 (7): e1008079.
  • Maganja, S. A., Clarke, D. C., Lear, S. A., Mackey, D. C. (2020) Formative Evaluation of Consumer-Grade Activity Monitors Worn by Older Adults: Test-Retest Reliability and Criterion Validity of Step Counts. JMIR Formative Research. 4(8):e16537. doi: 10.2196/16537.
  • Nadeau, E., Mezei, M. M., Cresswell, M., Zhao, S., Bosdet, T., Sin, D. D., Guenette, J. A., Dupuis, I., Allin, E., Clarke, D. C., Mattman, A. (2020) Self-initiated lifestyle interventions lead to potential insight into an effective, alternative, non-surgical therapy for mitochondrial disease associated multiple symmetric lipomatosis. Mitochondrion. Mar 29. pii: S1567-7249(19)30315-0.
  • Puchowicz, M. J., Baker, J., Clarke, D. C. (2020) Development and field validation of an omni-domain power-duration model. Journal of Sports Sciences. 38(7):801-813.
  • Puchowicz, M. J., Mizelman, E., Yogev, A., Koehle, M. S., Townsend, N. E., Clarke, D. C. (2018) The critical power model as a potential tool for anti-doping. Frontiers in Physiology. 9: 643.
  • Morris, M. K., Clarke, D. C., Osimiri, L. C., Lauffenburger, D. A. (2016) Systematic analysis of quantitative logic model ensembles predicts drug combination effects on cell signaling networks. Clinical Pharmacology and Therapeutics – Pharmacometrics and Systems Pharmacology. 5(10): 544-553.
  • Landry, B. D., Clarke, D. C., Lee, M. J. Studying Cellular Signal Transduction with OMIC Technologies. Journal of Molecular Biology. 427(21): 3416-40
  • 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 310 – Exercise/Work Physiology
  • BPK 443 – Advanced Exercise Programming