Computational Model for Autophagic Vesicle Dynamics in Single Cells


Macroautophagy (autophagy) is a cellular recycling program essential for homeostasis and survival during cytotoxic stress. This process, which has an emerging role in disease etiology and treatment, is executed in four stages through the coordinated action of more than 30 proteins. An effective strategy for studying complicated cellular processes, such as autophagy, involves the construction and analysis of mathematical or computational models. When developed and refined from experimental knowledge, these models can be used to interrogate signaling pathways, formulate novel hypotheses about systems, and make predictions about cell signaling changes induced by specific interventions. Here, we present the development of a computational model describing autophagic vesicle dynamics in a mammalian system. We used time-resolved, live-cell microscopy to measure the synthesis and turnover of autophagic vesicles in single cells. The stochastically simulated model was consistent with data acquired during conditions of both basal and chemicallyinduced autophagy. The model was tested by genetic modulation of autophagic machinery and found to accurately predict vesicle dynamics observed experimentally. Furthermore, the model generated an unforeseen prediction about vesicle size that is consistent with both published findings and our experimental observations. Taken together, this model is accurate and useful and can serve as the foundation for future efforts aimed at quantitative characterization of autophagy.


Chemical and Biochemical Engineering


This work was supported by the Department of Defense Prostate Cancer Research Program of the Office of Congressionally Directed Medical Research Programs PC081089 to J.P.M. This work was also supported by NIH grant GM085273 and by DOE contract DE-AC52-06NA25396.

Keywords and Phrases

Autophagy; Computational; Gillespie's Method; LC3; Live-Cell Imaging; Mathematical; Microscopy; Quantitative Biology; Single Cell; Systems Biology

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Article - Journal

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© 2013 Taylor & Francis, All rights reserved.

Publication Date

01 Jan 2013