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 first develop an efficient computational method to compute the mean first passage times for simulating one particle diffusing along two-dimensional planar networks extracted from fluorescence microscopy imaging. Applying it to live-cell data from endoplasmic reticulum (ER) tubular networks, we find that changes in the periphery regions, where nodes are sparsely connected, have a bigger impact in the diffusive transport. We then employ multi-particle stochastic simulations on various ER networks to further investigate how the structure of ER guides the

diffusive protein transport.