Time series characterization of simulated microtubule dynamics in the nerve growth cone.
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 microtubule 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.
Odde
D J
DJ
Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08855-0909, USA.
Buettner
H M
HM
D000818:Animals; D002455:Cell Division; D002478:Cells, Cultured; D003198:Computer Simulation; D006801:Humans; D008870:Microtubules / Q000502:physiology*; D008962:Models, Theoretical; D009474:Neurons / Q000166:cytology* / Q000502:physiology; D013997:Time Factors