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Mathematical Modeling of microRNA–Mediated Mechanisms of Translation Repression

  • Andrei Zinovyev
  • Nadya Morozova
  • Alexander N. Gorban
  • Annick Harel-Belan
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 774)

Abstract

MicroRNAs can affect the protein translation using nine mechanistically different mechanisms, including repression of initiation and degradation of the transcript. There is a hot debate in the current literature about which mechanism and in which situations has a dominant role in living cells. The worst, same experimental systems dealing with the same pairs of mRNA and miRNA can provide ambiguous evidences about which is the actual mechanism of translation repression observed in the experiment. We start with reviewing the current knowledge of various mechanisms of miRNA action and suggest that mathematical modeling can help resolving some of the controversial interpretations. We describe three simple mathematical models of miRNA translation that can be used as tools in interpreting the experimental data on the dynamics of protein synthesis. The most complex model developed by us includes all known mechanisms of miRNA action. It allowed us to study possible dynamical patterns corresponding to different miRNA-mediated mechanisms of translation repression and to suggest concrete recipes on determining the dominant mechanism of miRNA action in the form of kinetic signatures. Using computational experiments and systematizing existing evidences from the literature, we justify a hypothesis about co-existence of distinct miRNA-mediated mechanisms of translation repression. The actually observed mechanism will be that acting on or changing the sensitive parameters of the translation process. The limiting place can vary from one experimental setting to another. This model explains the majority of existing controversies reported.

Keywords

microRNA mechanism Translation Mathematical modeling Chemical kinetics Limiting step Relaxation time Dominant system 

Notes

Acknowledgements

This work was supported by a grant from the European Commission Sixth Framework Programme (Integrated Project SIROCCO, contract number LSHG-CT-2006-037900) to AHB, and from the Agence Nationale de la Recherche (project ANR-08-SYSC-003 CALAMAR) and from the Projet Incitatif Collaboratif “Bioinformatics and Biostatistics of Cancer” to Institut Curie. AZ is a member of the team “Systems Biology of Cancer”, labeled by the Ligue Nationale Contre le Cancer.

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© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Andrei Zinovyev
    • 1
    • 2
    • 3
  • Nadya Morozova
    • 4
  • Alexander N. Gorban
    • 5
  • Annick Harel-Belan
    • 4
  1. 1.Institut CurieParisFrance
  2. 2.INSERMParisFrance
  3. 3.Mines ParisTechFontainebleauFrance
  4. 4.Laboratoire Epigenetique et Cancer CEA SaclayCNRS FRE 3377Gif-sur-YvetteFrance
  5. 5.Centre for Mathematical ModellingUniversity of LeicesterLeicesterUK

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