Statistical Inference of Peroxisome Dynamics

Abstract

The regulation of organelle abundance sustains critical biological processes, such as metabolism and energy production. Biochemical models mathematically express these temporal changes in terms of reactions, and their rates. The rate parameters are critical components of the models, and must be experimentally inferred. However, the existing methods for rate inference are limited, and not directly applicable to organelle dynamics. This manuscript introduces a novel approach that integrates modeling, inference and experimentation, and incorporates biological replicates, to accurately infer the rates. The approach relies on a biochemical model in form of a stochastic differential equation, and on a parallel implementation of inference with particle filter. It also relies on a novel microscopy workflow that monitors organelles over long periods of time in cell culture. Evaluations on simulated datasets demonstrated the advantages of this approach in terms of increased accuracy and shortened computation time. An application to imaging of peroxisomes determined that fission, rather than de novo generation, is predominant in maintaining the organelle level under basal conditions. This biological insight serves as a starting point for a system view of organelle regulation in cells.