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International Conference on Research in Computational Molecular Biology

RECOMB 2018: Research in Computational Molecular Biology pp 54–74Cite as

Statistical Inference of Peroxisome Dynamics

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10812))

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.

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Acknowledgements

This work was supported in part by a Burroughs Wellcome travel grant (to C.G.), a Dodds Fellowship (to P.M.J.B.), NIH grants GM114141, HL127640, Mallinckrodt Scholar Award (to I.M.C.), and Sy and Laurie Sternberg award (to O.V.).

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Authors and Affiliations

  1. College of Science, College of Computer and Information Science, Northeastern University, Boston, 02115, USA

    Cyril Galitzine & Olga Vitek

  2. Lewis Thomas Laboratory, Department of Molecular Biology, Princeton University, Washington Road, Princeton, NJ, 08544, USA

    Pierre M. Jean Beltran & Ileana M. Cristea

Authors
  1. Cyril Galitzine
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  2. Pierre M. Jean Beltran
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  3. Ileana M. Cristea
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  4. Olga Vitek
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Corresponding author

Correspondence to Cyril Galitzine .

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Editors and Affiliations

  1. Computer Science Department, Princeton University, Princeton, New Jersey, USA

    Benjamin J. Raphael

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Galitzine, C., Beltran, P.M.J., Cristea, I.M., Vitek, O. (2018). Statistical Inference of Peroxisome Dynamics. In: Raphael, B. (eds) Research in Computational Molecular Biology. RECOMB 2018. Lecture Notes in Computer Science(), vol 10812. Springer, Cham. https://doi.org/10.1007/978-3-319-89929-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-89929-9_4

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