Skip to main content
Log in

Time series characterization of simulated microtubule dynamics in the nerve growth cone

  • Research Articles
  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

The process of neurite outgrowth is critically dependent on proper microtubule assembly. However, characterizing the dynamics of microtubule assembly and their quantitative relationship to neurite outgrowth is a difficult task. The difficulty can be reduced by using time series analysis which has broad application in characterizing the dynamics of stochastic, or “noisy,” behaviors. Here we apply time series analysis to quantitatively compare simulated microtubule assembly and neurite outgrowth in vitro. Microtubule length life histories were simulated assuming constant growth and shrinkage rates coupled with random selection of growth and shrinkage times, a formulation based on the dynamic instability model of microtubules assembly. Net length displacements of simulated microtubules were calculated at discrete, evenly spaced times, and the resulting time series were characterized by both spectral and autocorrelation analysis. Depending on the sampling rate and the dynamic parameters, simulated microtubules exhibited significant autocorrelation and periodicity. To make a comparison to neurite outgrowth, we characterized the dynamic behavior of simulated microtubule populations and found it was not significantly different from that of single microtubules. The net displacements of rat superior cervical ganglion neurite tips were measured and characterized using time series methods. Their behavior was consistent with the microtubule dynamics for appropriate simulation parameters and sampling rates. Our results show that time series analysis can provide a useful tool for quantitative characterization of microtubule dynamics and neurite outgrowth and for assessing the relationship between them.

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. Alberts, B., D. Bray, J. Lewis, M. Raff, K. Roberts, and J. D. Watson. Molecular Biology of the Cell (3rd ed.). New York: Garland Publishing, 1994, 1294 pp.

    Google Scholar 

  2. Baas, P. W., and F. J. Ahmad. The transport properties of axonal microtubules establish their polarity orientation.J. Cell Biol. 120:1427–1437, 1993.

    Article  PubMed  CAS  Google Scholar 

  3. Bamburg, J. R., D. Bray, and K. Chapman. Assembly of microtubules at the tip of growing axons.Nature 321:788–790, 1986.

    Article  PubMed  CAS  Google Scholar 

  4. Bayley, P. M., M. J. Schilstra, and S. R. Martin. A simple formulation of microtubule dynamics: quantitative implications of the dynamic instability of microtubule populationsin vivo andin vitro.J. Cell Sci. 93:241–254, 1989.

    PubMed  Google Scholar 

  5. Bayley, P. M., M. J. Schilstra, and S. R. Martin. Microtubule dynamic instability: Numerical simulation of microtubule transition properties using a Lateral Cap model.J. Cell Sci. 95:33–48, 1990.

    PubMed  Google Scholar 

  6. Belmont, L. D., A. A. Hyman, K. E. Swain, and T. J. Mitchison. Real-time visualization of cell cycle-dependent changes in microtubule dynamics in cytoplasmic extracts.Cell 62:579–589, 1990.

    Article  PubMed  CAS  Google Scholar 

  7. Bloomfield, P. Fourier Analysis of Time Series: An Introduction. New York: John Wiley & Sons, 1976, 258 pp.

    Google Scholar 

  8. Brown, A., T. Slaughter, and M. M. Black. Newly assembled microtubules are concentrated in the proximal and distal regions of growing axons.J. Cell Biol. 119:867–882, 1992.

    Article  PubMed  CAS  Google Scholar 

  9. Buettner, H. M., R. Pittman, and J. Ivins. A model of neurite extension across regions of nonpermissive substrate: Simulations based on experimental measurement of growth cone motility and filopodia dynamics.Dev. Biol. 163:407–422, 1994.

    Article  PubMed  CAS  Google Scholar 

  10. Cassimeris, L., N. K. Pryer, and E. D. Salmon. Real-time observations of microtubule dynamic instability in living cells.J. Cell Biol. 107:2223–2231, 1988.

    Article  PubMed  CAS  Google Scholar 

  11. Chen, Y., and T. L. Hill. Use of Monte Carlo calculations in the study of microtubule subunit kinetics.Proc. Natl. Acad. Sci. USA 80:7520–7523, 1983.

    Article  PubMed  CAS  Google Scholar 

  12. Chen, Y., and T. L. Hill. Monte Carlo study of the GTP cap in a five-start helix model of a microtubule.Proc. Natl. Acad. Sci. USA 82:1131–1135, 1985.

    Article  PubMed  CAS  Google Scholar 

  13. Dogterom, M., and S. Leibler. Physical aspects of the growth and regulation of microtubule structures.Phys. Rev. Lett. 70:1347–1350, 1993.

    Article  PubMed  CAS  Google Scholar 

  14. Dunn, G. A., and A. F. Brown. A unified approach to analyzing cell motility.J. Cell Sci. Suppl. 8:81–102, 1987.

    PubMed  CAS  Google Scholar 

  15. Durbin, J. The fitting of time-series models.Intl. Stat. Rev. 28:233–244, 1960.

    Google Scholar 

  16. Gildersleeve, R. F., A. R. Cross, K. E. Cullen, A. P. Fagen, and R. C. Williams Jr. Microtubules grow and shorten at intrinsically variable rates.J. Biol. Chem. 267: 7995–8006, 1992.

    PubMed  CAS  Google Scholar 

  17. Gliksman, N. R., S. F. Parsons, and E. D. Salmon. Okadaic acid induces interphase to mitotic-like microtubule dynamic instability by inactivating rescue.J. Cell Biol. 119: 1271–1276, 1992.

    Article  PubMed  CAS  Google Scholar 

  18. Gliksman, N. R., E. D. Salmon, and R. A. Walker. Relating the kinetic parameters of dynamic instability to the cell: a computer simulation approach.J. Cell Biol. 105:30a, 1987.

    Google Scholar 

  19. Gliksman, N. R., R. V. Skibbens, and E. D. Salmon. How the transition frequencies of microtubule dynamic instability (nucleation, catastrophe, and rescue) regulate microtubule dynamics in interphase and mitosis: analysis using a Monte Carlo computer simulation.Mol. Biol. Cell 4:1035–1050, 1993.

    PubMed  CAS  Google Scholar 

  20. Goldberg, D. J., and D. W. Burmeister. Stages in axon formation: observations of growth ofAplysia axons in culture using video-enhanced contrast-differential interference contrast microscopy.J. Cell Biol. 103:1921–1931, 1986.

    Article  PubMed  CAS  Google Scholar 

  21. Goldberg, D. J., and D. W. Burmeister. Looking into growth cones.Trends Neurosci. 12:503–506, 1988.

    Article  Google Scholar 

  22. Heidemann, S. R., and J. R. McIntosh. Visualization of the structural polarity of microtubules.Nature 286:517–519, 1980.

    Article  PubMed  CAS  Google Scholar 

  23. Hill, T. L. Introductory analysis of the GTP-cap phase-change kinetics at the end of a microtubule.Proc. Natl. Acad. Sci. USA 81:6728–6732, 1984.

    Article  PubMed  CAS  Google Scholar 

  24. Hill, T. L. Linear Aggregation Theory in Cell Biology. New York: Springer-Verlag, 1987, 305 pp.

    Google Scholar 

  25. Hill, T. L., and Y. Chen. Phase changes at the end of a microtubule with a GTP gap.Proc. Natl. Acad. Sci. USA 81:5772–5776, 1984.

    Article  PubMed  CAS  Google Scholar 

  26. Hoffman, P. N., and R. J. Lasek. The slow component of axonal transport: Identification of major structural polypeptides of the axon and their generality among mammalian neurons.J. Cell Biol. 66:351–366, 1975.

    Article  PubMed  CAS  Google Scholar 

  27. Horio, T., and H. Hotani. Visualization of the dynamic instability of individual microtubules.Nature 321:605–607, 1986.

    Article  PubMed  CAS  Google Scholar 

  28. Katz, M. J., E. B. George, and L. J. Gilbert. Axonal elongation as a stochastic walk.Cell Motil. 4:351–370, 1984.

    Article  PubMed  CAS  Google Scholar 

  29. Marsh, L., and P. C. Letourneau. Growth of neurites without filopodial or lamellipodial activity in the presence of cytochalasin B.J. Cell Biol. 99:2041–2047, 1984.

    Article  PubMed  CAS  Google Scholar 

  30. Martin, S. R., M. J. Schilstra, and P. M. Bayley. Dynamic instability of microtubules: Monte Carlo simulation and application to different types of microtubule lattice.Biophys. J. 65:578–596, 1993.

    Article  PubMed  CAS  Google Scholar 

  31. Mitchison, T. J., and M. W. Kirschner. Dynamic instability of microtubule growth.Nature 312:237–242, 1984.

    Article  PubMed  CAS  Google Scholar 

  32. Mitchison, T. J., and M. W. Kirschner. Some thoughts on the partitioning of tubulin between monomer and polymer under conditions of dynamic instability.Cell Biophys. 11: 35–55, 1987.

    PubMed  CAS  Google Scholar 

  33. O'Brien, E. T., E. D. Salmon, R. A. Walker, and H. P. Erickson. Effects of magnesium on the dynamic instability of individual microtubules.Biochemistry 29:6648–6656, 1990.

    Article  PubMed  Google Scholar 

  34. Okabe, S., and N. Hirokawa. Microtubule dynamics in nerve cells: analysis using microinjection of biotinylated tubulin into PC12 cells.J. Cell Biol. 107:651–664, 1988.

    Article  PubMed  CAS  Google Scholar 

  35. Okabe, S., and N. Hirokawa. Do photobleached fluorescent microtubules move?: Re-revaluation of fluorescence laser photobleaching bothin vitro and in growingXenopus axon.J. Cell Biol. 120:1177–1186, 1993.

    Article  PubMed  CAS  Google Scholar 

  36. Olkin, I., L. J. Gleser, and C. Derman. Probability Models and Applications. New York: Macmillan Publishing Co., Inc., 1980, 576 pp.

    Google Scholar 

  37. Partin, A. W., J. S. Schoeniger, J. L. Mohler, and D. S. Coffey. Fourier analysis of cell motility: Correlation of motility with metastatic potential.Proc. Natl. Acad. Sci. USA 86:1254–1258, 1989.

    Article  PubMed  CAS  Google Scholar 

  38. Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in Fortran (2nd ed.). New York: Press Syndicate of the University of Cambridge, 1992, 963 pp.

    Google Scholar 

  39. Pryer, N. K., R. A. Walker, V. P. Skeen, B. D. Bourns, M. F. Sobeiro, and E. D. Salmon. Brain microtubule-associated proteins modulate microtubule dynamic instabilityin vitro.J. Cell Sci. 103:965–976, 1992.

    PubMed  CAS  Google Scholar 

  40. Reinsch, S. S., T. J. Mitchison, and M. W. Kirschner. Microtubule polymer assembly and transport during axonal elongation.J. Cell Biol. 115:365–379, 1991.

    Article  PubMed  CAS  Google Scholar 

  41. Sammak, P. J., and G. G. Borisy. Direct observation of microtubule dynamics in living cells.Nature 332:724–726, 1988.

    Article  PubMed  CAS  Google Scholar 

  42. Schilstra, M. J., P. M. Bayley, and S. R. Martin. The effect of solution composition on microtubule dynamic instability.Biochem. J. 277:839–847, 1991.

    PubMed  CAS  Google Scholar 

  43. Schulze, E., and M. Kirschner. New features of microtubule behaviour observedin vivo.Nature 334:356–359, 1988.

    Article  PubMed  CAS  Google Scholar 

  44. Shelden, E., and P. Wadsworth. Observation and quantification of individual microtubule behaviorin vivo: microtubule dynamics are cell-type specific.J. Cell Biol. 120:935–945, 1993.

    Article  PubMed  CAS  Google Scholar 

  45. Simon, J. R., S. F. Parsons, and E. D. Salmon. Buffer conditions and non-tubulin factors critically affect the microtubule dynamic instability of sea urchin egg tubulin.Cell Motil. Cytoskel. 21:1–14, 1992.

    Article  CAS  Google Scholar 

  46. Tanaka, E. M., and M. W. Kirschner. Microtubule behavior in the growth cones of living neurons during axon elongation.J. Cell Biol. 115:345–363, 1991.

    Article  PubMed  CAS  Google Scholar 

  47. Verde, F., M. Dogterom, E. Stelzer, E. Karsenti, and S. Leibler. Control of microtubule dynamics and length by cyclin A- and cyclin B-dependent kinases inXenopus egg extracts.J. Cell Biol. 118:1097–1108, 1992.

    Article  PubMed  CAS  Google Scholar 

  48. Walker, R. A., E. T. O'Brien, N. K. Pryer, M. F. Soboeiro, W. A. Voter, H. P. Erickson, and E. D. Salmon. Dynamic instability of individual microtubules analyzed by video light microscopy: rate constants and transition frequencies.J. Cell Biol. 107:1437–1448, 1988.

    Article  PubMed  CAS  Google Scholar 

  49. Wei, W. W. S. Time Series Analysis: Univariate and Multivariate Methods. Reading, MA: Addison-Wesley, 1990. 478 pp.

    Google Scholar 

  50. Yu, W., V. E. Centonze, F. J. Ahmad, and P. W. Baas. Microtubule nucleation and release from the neuronal centrosome.J. Cell Biol. 122:349–359, 1993.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Odde, D.J., Buettner, H.M. Time series characterization of simulated microtubule dynamics in the nerve growth cone. Ann Biomed Eng 23, 268–286 (1995). https://doi.org/10.1007/BF02584428

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02584428

Keywords

Navigation