Abstract

A wide variety of phenomena in cellular and molecular biology involve anomalous transport, where the statistical behavior of cell and molecule motility deviates from the standard models of simple diffusion or ballistic transport. Recent research has shown that anomalous transport is often heterogeneous in both time and space. Consequently, using single anomalous exponents and generalized diffusion coefficients fails to describe many critical phenomena in these fields adequately. In this presentation, I will provide a brief review of the heterogeneous anomalous transport (HAT) field, focusing on the main theoretical tools for data analysis, such as robust statistical methods, analytic theory, and generative theoretical models based on simulations. Special emphasis will be placed on high-throughput analysis techniques leveraging machine learning and neural networks. Additionally, I will present specific examples from cellular and molecular biology, including embryonic cells, bacterial cells, and endosomal transport.