Fundamentals of the logarithmic measure for revealing multimodal diffusion

Research output: Contribution to journalResearch articleContributedpeer-review


  • Benjamin A. Dalton - , Max-Planck-Institute for the Physics of Complex Systems, Max Planck Institute of Molecular Cell Biology and Genetics, Center for Systems Biology Dresden (CSBD), Dresden University of Technology, Free University of Berlin (Author)
  • Ivo F. Sbalzarini - , Chair of Scientific Computing for Systems Biology, Clusters of Excellence PoL: Physics of Life, Max Planck Institute of Molecular Cell Biology and Genetics, Center for Systems Biology Dresden (CSBD) (Author)
  • Itsuo Hanasaki - , Tokyo University of Agriculture and Technology (Author)


We develop a theoretical foundation for a time-series analysis method suitable for revealing the spectrum of diffusion coefficients in mixed Brownian systems, for which no prior knowledge of particle distinction is required. This method is directly relevant for particle tracking in biological systems, in which diffusion processes are often nonuniform. We transform Brownian data onto the logarithmic domain, in which the coefficients for individual modes of diffusion appear as distinct spectral peaks in the probability density. We refer to the method as the logarithmic measure of diffusion, or simply as the logarithmic measure. We provide a general protocol for deriving analytical expressions for the probability densities on the logarithmic domain. The protocol is applicable for any number of spatial dimensions with any number of diffusive states. The analytical form can be fitted to data to reveal multiple diffusive modes. We validate the theoretical distributions and benchmark the accuracy and sensitivity of the method by extracting multimodal diffusion coefficients from two-dimensional Brownian simulations of polydisperse filament bundles. Bundling the filaments allows us to control the system nonuniformity and hence quantify the sensitivity of the method. By exploiting the anisotropy of the simulated filaments, we generalize the logarithmic measure to rotational diffusion. By fitting the analytical forms to simulation data, we confirm the method's theoretical foundation. An error analysis in the single-mode regime shows that the proposed method is comparable in accuracy to the standard mean-squared displacement approach for evaluating diffusion coefficients. For the case of multimodal diffusion, we compare the logarithmic measure against other, more sophisticated methods, showing that both model selectivity and extraction accuracy are comparable for small data sets. Therefore, we suggest that the logarithmic measure, as a method for multimodal diffusion coefficient extraction, is ideally suited for small data sets, a condition often confronted in the experimental context. Finally, we critically discuss the proposed benefits of the method and its information content.


Original languageEnglish
Pages (from-to)829-843
Number of pages15
JournalBiophysical journal
Issue number5
Publication statusPublished - 2 Mar 2021

External IDs

PubMed 33453269
ORCID /0000-0003-4414-4340/work/142252158


ASJC Scopus subject areas