Skip to main content
Log in

A Markov Model for Kinesin

  • Published:
Journal of Statistical Physics Aims and scope Submit manuscript

Abstract

We investigate the validity of a Markov approach for the motility of kinesin. We show in detail how the various mechanochemical states and reaction rates that are experimentally measured, can be used to create a Markov-chain model. We compare the performance of this model to motility data and we find global similarities in the load and ATP-concentration dependency of speed and mean run length. We also discuss the relation between the experimentally found stalling behavior and thermodynamic expectations. Finally, the Markov chain modelling provides a way to calculate the mean entropy production and the (power) efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

REFERENCES

  1. M. E. Fisher and A. B. Kolomeisky, Proc. Nat. Ac. Sc. 98:7748(2001).

    Google Scholar 

  2. A. Mogilner, A. J. Fisher, and R. J. Baskin, J. Theor. Biol. 211:143–157 (2001).

    Google Scholar 

  3. K. Visscher, M. J. Schnitzer, and S. M. Block, Nature 400:184(1999).

    Google Scholar 

  4. M. J. Schnitzer, K. Visscher, and S. M. Block, Nature Cell B 2:718(2000).

    Google Scholar 

  5. C. Maes and K. Netočnčy, J. Stat. Phys. 110, 269–310 (2003).

    Google Scholar 

  6. G. M. Wang, E. M. Sevick, and E. Mittag, et al., Phys. Rev. Lett. 89:050601(2002).

    Google Scholar 

  7. T. L. Hill, Thermodynamics of Small Systems (Dover 2002).

  8. R. D. Vale and R. A. Milligan, Science 288:88(2000).

    Google Scholar 

  9. S. Rice, A. W. Lin, and D. Safer, et al., Nature 402:778(1999).

    Google Scholar 

  10. J. Howard, Mechanics of Motor Proteins and the Cytoskeleton (Sinauer, 2001), p. 231.

  11. M. J. Schnitzer and S. M. Block, Nature 388:386(1997).

    Google Scholar 

  12. W. Hua, E. C. Young, M. L. Flemming, and J. Gelles, Nature 388:390(1997).

    Google Scholar 

  13. W. Hua, J. Chung, and J. Gelles, Science 295:844(2002).

    Google Scholar 

  14. M. L. Moyer, S. P. Gilbert, and K. A. Johnson, Biochemistry 37:800(1998).

    Google Scholar 

  15. Hong Qian, J. Math. Chem. 27:219–234 (2000).

    Google Scholar 

  16. P. Reimann, Phys. Rep. 361:57(2002).

    Google Scholar 

  17. F. Jülicher, A. Ajdari, and J. Prost, Rev. Mod. Phys. 69:1269(1997).

    Google Scholar 

  18. M. E. Fisher and A. B. Kolomeisky, Proc. Nat. Ac. Sc. 96:6597(1999).

    Google Scholar 

  19. C. S. Peskin and G. Oster, Biophys. J. 68:202s(1995).

    Google Scholar 

  20. M. A. Leontovich, J. Experiment. Theoret. Phys. 5:211(1935).

    Google Scholar 

  21. D. A. McQuarrie, J. Appl. Prob. 4:413(1967).

    Google Scholar 

  22. C. Maes, F. Redig, and A. Van Moffaert, J. Math. Phys. 41:1528(2000).

    Google Scholar 

  23. J. Schnakenberg, Rev. Mod. Phys. 48:571(1976)

    Google Scholar 

  24. L. Romberg and R. D. Vale, Nature 361:168(1993).

    Google Scholar 

  25. C. M. Coppin, D. Pierce, L. Hsu, and R. D. Vale, Proc. Nat. Ac. Sc. 94:8539(1997).

    Google Scholar 

  26. I. Derényi, M. Bier, and R. D. Astumian, Phys. Rev. Lett. 83:903(1999).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Maes, C., van Wieren, M.H. A Markov Model for Kinesin. Journal of Statistical Physics 112, 329–355 (2003). https://doi.org/10.1023/A:1023691923564

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1023691923564

Navigation