Fast solver for diffusive transport times on dynamic intracellular networks

TL;DR

This paper introduces a fast computational method to estimate mean first passage times for diffusive particles on dynamic intracellular networks, validated on synthetic and live-cell data to understand transport in cellular environments.

Contribution

It presents a novel efficient algorithm for calculating diffusive transport times on time-evolving intracellular networks, bridging imaging data and network theory.

Findings

Method accurately computes transport times on synthetic networks.

Application to live-cell ER networks demonstrates biological relevance.

Provides insights into how network dynamics influence intracellular transport.

Abstract

The transport of particles in cells is influenced by the properties of intracellular networks they traverse while searching for localized target regions or reaction partners. Moreover, given the rapid turnover in many intracellular structures, it is crucial to understand how temporal changes in the network structure affect diffusive transport. In this work, we use network theory to characterize complex intracellular biological environments across scales. We develop an efficient computational method to compute the mean first passage times for simulating a particle diffusing along two-dimensional planar networks extracted from fluorescence microscopy imaging. We first benchmark this methodology in the context of synthetic networks, and subsequently apply it to live-cell data from endoplasmic reticulum tubular networks.