2017/07/31 by Vincent Briane, Charles Kervrann, Myriam Vimond · 1 citation
Biochemistry, Genetics and Molecular Biology · Decision Sciences · Mathematics · Physics and Astronomy · #Algorithm #Anomalous diffusion #Artificial intelligence #Biological system #Biology #Computer science #Data mining #Diffusion #Diffusion and Search Dynamics #Inference #Mathematics #Mean squared displacement #Molecular dynamics #Monte Carlo method #Null (SQL) #Null hypothesis #Parametric statistics #Physics #Probabilistic and Robust Engineering Design #Statistical hypothesis testing #Statistical physics #Statistics #Stochastic processes and statistical mechanics #Test statistic #physics.bio-ph #stat.AP
paper · pdf · doi:10.1103/physreve.97.062121
published as Phys. Rev. E 97, 062121 (2018) · Revised introduction. A clearer and shorter description of the model (section 2)
arxiv created 2017/09/13 · openalex publication_date 2018/06/11 · arxiv updated 2018/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Recent advances in molecular biology and fluorescence microscopy imaging have made possible the inference of the dynamics of molecules in living cells. Such inference allows us to understand and determine the organization and function of the cell. The trajectories of particles (e.g., biomolecules) in living cells, computed with the help of object tracking methods, can be modeled with diffusion processes. Three types of diffusion are considered: (i) free diffusion, (ii) subdiffusion, and (iii) superdiffusion. The mean-square displacement (MSD) is generally used to discriminate the three types of particle dynamics. We propose here a nonparametric three-decision test as an alternative to the MSD method. The rejection of the null hypothesis, i.e., free diffusion, is accompanied by claims of the direction of the alternative (subdiffusion or superdiffusion). We study the asymptotic behavior of the test statistic under the null hypothesis and under parametric alternatives which are currently considered in the biophysics literature. In addition, we adapt the multiple-testing procedure of Benjamini and Hochberg to fit with the three-decision-test setting, in order to apply the test procedure to a collection of independent trajectories. The performance of our procedure is much better than the MSD method as confirmed by Monte Carlo experiments. The method is demonstrated on real data sets corresponding to protein dynamics observed in fluorescence microscopy.