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International Conference on Computational Methods in Systems Biology

CMSB 2018: Computational Methods in Systems Biology pp 129–145Cite as

Buffering Gene Expression Noise by MicroRNA Based Feedforward Regulation

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11095))

Abstract

Cells use various regulatory motifs, including feedforward loops, to control the intrinsic noise that arises in gene expression at low copy numbers. Here we study one such system, which is broadly inspired by the interaction between an mRNA molecule and an antagonistic microRNA molecule encoded by the same gene. The two reaction species are synchronously produced, individually degraded, and the second species (microRNA) exerts an antagonistic pressure on the first species (mRNA). Using linear-noise approximation, we show that the noise in the first species, which we quantify by the Fano factor, is sub-Poissonian, and exhibits a nonmonotonic response both to the species lifetime ratio and to the strength of the antagonistic interaction. Additionally, we use the Chemical Reaction Network Theory to prove that the first species distribution is Poissonian if the first species is much more stable than the second. Finally, we identify a special parametric regime, supporting a broad range of behaviour, in which the distribution can be analytically described in terms of the confluent hypergeometric limit function. We verify our analysis against large-scale kinetic Monte Carlo simulations. Our results indicate that, subject to specific physiological constraints, optimal parameter values can be found within the mRNA–microRNA motif that can benefit the cell by lowering the gene-expression noise.

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Acknowledgements

PB acknowledges support from the Slovak Research and Development Agency under the contract No. APVV-14-0378, the VEGA grant 1/0347/18, and the EraCoSysMed project 4D-Healing. AS is supported by the National Science Foundation grant ECCS-1711548.

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Authors and Affiliations

  1. Department of Applied Mathematics and Statistics, Comenius University, 84248, Bratislava, Slovakia

    Pavol Bokes & Michal Hojcka

  2. Mathematical Institute, Slovak Academy of Sciences, 81473, Bratislava, Slovakia

    Pavol Bokes

  3. Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, 19716, USA

    Abhyudai Singh

Authors
  1. Pavol Bokes
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  2. Michal Hojcka
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  3. Abhyudai Singh
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Corresponding author

Correspondence to Pavol Bokes .

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Editors and Affiliations

  1. Brno University of Technology, Brno, Czech Republic

    Milan Češka

  2. Masaryk University, Brno, Czech Republic

    David Šafránek

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Bokes, P., Hojcka, M., Singh, A. (2018). Buffering Gene Expression Noise by MicroRNA Based Feedforward Regulation. In: Češka, M., Šafránek, D. (eds) Computational Methods in Systems Biology. CMSB 2018. Lecture Notes in Computer Science(), vol 11095. Springer, Cham. https://doi.org/10.1007/978-3-319-99429-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-99429-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99428-4

  • Online ISBN: 978-3-319-99429-1

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