Statistical Inference of Peroxisome Dynamics

Cyril Galitzine, Pierre M.Jean Beltran, Ileana M. Cristea, Olga Vitek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 22nd Annual International Conference, RECOMB 2018, Proceedings
EditorsBenjamin J. Raphael
PublisherSpringer Verlag
Pages54-74
Number of pages21
ISBN (Print)9783319899282
DOIs
StatePublished - Jan 1 2018
Event22nd International Conference on Research in Computational Molecular Biology, RECOMB 2018 - Paris, France
Duration: Apr 21 2018Apr 24 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10812 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Research in Computational Molecular Biology, RECOMB 2018
CountryFrance
CityParis
Period4/21/184/24/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Bayesian inference
  • Organelles
  • Particle filter
  • Peroxisomes
  • Replicate
  • Stochastic differential equation
  • Stochastic process

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  • Cite this

    Galitzine, C., Beltran, P. M. J., Cristea, I. M., & Vitek, O. (2018). Statistical Inference of Peroxisome Dynamics. In B. J. Raphael (Ed.), Research in Computational Molecular Biology - 22nd Annual International Conference, RECOMB 2018, Proceedings (pp. 54-74). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10812 LNBI). Springer Verlag. https://doi.org/10.1007/978-3-319-89929-9_4