An overview of diffusion models for intracellular dynamics analysis

Abstract

We present an overview of diffusion models commonly used for quantifying the dynamics of intracellular particles (e.g. biomolecules) inside eukaryotic living cells. It is established that inference on the modes of mobility of molecules is central in cell biology since it reflects interactions between structures and determines functions of biomolecules in the cell. In that context, Brownian motion is a key component in short distance transportation (e.g. connectivity for signal transduction). Another dynamical process that has been heavily studied in the past decade is the motor-mediated transport (e.g. dynein, kinesin and myosin) of molecules. Primarily supported by actin filament and microtubule network, it ensures spatial organization and temporal synchronization in the intracellular mechanisms and structures. Nevertheless, the complexity of internal structures and molecular processes in the living cell influence the molecular dynamics and prevent the systematic application of pure Brownian or directed motion modeling. On the one hand, cytoskeleton density will hinder the free displacement of the particle, a phenomenon called subdiffusion. On the other hand, the cytoskeleton elasticity combined with thermal bending can contribute a phenomenon called superdiffusion. This paper discusses the basics of diffusion modes observed in eukariotic cells, by introducing the essential properties of these processes. Applications of diffusion models include protein trafficking and transport and membrane diffusion.

Spatial Birth–Death–Move Processes: Basic Properties and Estimation of their Intensity Functions

“…The motion can be different from a protein to another and some interactions may occur. Previous studies (Briane et al., 2019) have identified three main regimes: Brownian motion, superdiffusive motion (like a Brownian process with drift) and subdiffusive motion (like the Ornstein–Uhlenbeck process). The process ( Y t ) t ≥0 in this application could then be a vector of n such processes, and some interactions between these n processes may further been introduced.…”

“…During the sequence, proteins appear, disappear and move, in keeping with a birth–death–move process. Classical approaches either study the trajectories of each protein independently without considering spatial interactions (Briane et al., 2019; Pécot et al., 2018), or study the spatial configurations of proteins at some time points without temporal insight (Bolte & Cordelieres, 2006; Costes et al., 2004; Lagache et al., 2015; Lavancier et al., 2020). In contrast, our method allows to investigate the joint spatiotemporal dynamics of all proteins, a new approach.…”

Segmentation of the urothelium in optical coherence tomography images with dynamic contrast

“…IM originates from the motion of molecules and organelles in the cytoplasm of eukaryotic cells and is essential for the proper functioning of cells. 11 The technology of tracking IM can be divided into two categories. Fluorescence labeling methods, such as fluorescence photobleaching recovery kinetics, 12 fluorescence correlation spectroscopy, 13 and time-resolved fluorescence imaging, 14 use fluorescence to quantify IM by tracking specific molecules.…”

A computational approach for detecting micro-domains and confinement domains in cells: a simulation study

“…Biophysicists distinguish four main types of diffusion, namely Brownian motion (also referred to as free diffusion), superdiffusion, confined diffusion and anomalous diffusion. These different diffusion types correspond to specific biological scenarios as briefly described in [Briane et al, 2019a]. A particle evolving freely inside the cytosol or along the plasma membrane is modeled by free diffusion.…”