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On chaotic dynamics in transcription factors and the associated effects in differential gene regulation

Author

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  • Mathias L. Heltberg

    (University of Copenhagen)

  • Sandeep Krishna

    (National Centre for Biological Sciences-TIFR)

  • Mogens H. Jensen

    (University of Copenhagen)

Abstract

The control of proteins by a transcription factor with periodically varying concentration exhibits intriguing dynamical behaviour. Even though it is accepted that transcription factors vary their dynamics in response to different situations, insight into how this affects downstream genes is lacking. Here, we investigate how oscillations and chaotic dynamics in the transcription factor NF-κB can affect downstream protein production. We describe how it is possible to control the effective dynamics of the transcription factor by stimulating it with an oscillating ligand. We find that chaotic dynamics modulates gene expression and up-regulates certain families of low-affinity genes, even in the presence of extrinsic and intrinsic noise. Furthermore, this leads to an increase in the production of protein complexes and the efficiency of their assembly. Finally, we show how chaotic dynamics creates a heterogeneous population of cell states, and describe how this can be beneficial in multi-toxic environments.

Suggested Citation

  • Mathias L. Heltberg & Sandeep Krishna & Mogens H. Jensen, 2019. "On chaotic dynamics in transcription factors and the associated effects in differential gene regulation," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-018-07932-1
    DOI: 10.1038/s41467-018-07932-1
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    Cited by:

    1. Gong, Li-Hua & Luo, Hui-Xin & Wu, Rou-Qing & Zhou, Nan-Run, 2022. "New 4D chaotic system with hidden attractors and self-excited attractors and its application in image encryption based on RNG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    2. Martiny, Emil S. & Jensen, Mogens H. & Heltberg, Mathias S., 2022. "Detecting limit cycles in stochastic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    3. Demirkıran, Gökhan, 2022. "Piecewise parametric chaotic model of p53 network based on the identified unifying framework of divergent p53 dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

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