Extracting Functional Information from Single Particle Trajectories

Abstract

Single particle tracking (SPT) experiments offer a powerful means of probing the diffusive characteristics of biomolecules. A common result from SPT is a globally averaged anomalous diffusion exponent. However, methods of calculating the average exponent are typically unable to determine the local, time-dependent characteristics. We have developed a simple classification algorithm to calculate the time-dependent anomalous diffusion exponent of single stochastic trajectories. Classification is based upon the behavior of a renormalization group operator rather than the commonly used mean-square displacement. We present both simulated and experimental examples of the proposed method. Furthermore, we hypothesize that time-dependent fluctuations of the anomalous diffusion exponent will correlate with functional activity in certain cases. We present preliminary results supporting this hypothesis.