100 years after Smoluchowski: stochastic processes in cell biology

TL;DR

This paper reviews the application of Smoluchowski's stochastic process approach in cell biology, highlighting its role in analyzing molecular trajectories, diffusive motion, and gene regulation, advancing large data analysis in cellular systems.

Contribution

It provides a comprehensive review of Smoluchowski's stochastic processes and demonstrates their diverse applications in cellular biology modeling and data analysis.

Findings

Stochastic processes are fundamental in analyzing molecular trajectories.

Applications include diffusion modeling, nuclear organization, and gene regulation.

The approach enhances understanding of cellular dynamics through large data analysis.

Abstract

100 years after Smoluchowski introduces his approach to stochastic processes, they are now at the basis of mathematical and physical modeling in cellular biology: they are used for example to analyse and to extract features from large number (tens of thousands) of single molecular trajectories or to study the diffusive motion of molecules, proteins or receptors. Stochastic modeling is a new step in large data analysis that serves extracting cell biology concepts. We review here the Smoluchowski's approach to stochastic processes and provide several applications for coarse-graining diffusion, studying polymer models for understanding nuclear organization and finally, we discuss the stochastic jump dynamics of telomeres across cell division and stochastic gene regulation.