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Estimating Effects of Extrinsic Noise on Model Genes and Circuits with Empirically Validated Kinetics

  • Samuel M. D. Oliveira
  • Mohamed N. M. Bahrudeen
  • Sofia Startceva
  • Andre S. Ribeiro
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 830)

Abstract

Recent studies of Escherichia coli transcription dynamics using time-lapse confocal microscopy and in vivo single-RNA detection confirmed that transcription initiation has two main rate-limiting steps. Here, we argue that this allows selective ‘tuning’ of the effects of extrinsic noise on a multi-scale level that ranges from individual genes to large-scale gene networks. First, using empirically validated stochastic models of transcription and translation, we show that the effects of RNA polymerase numbers’ cell-to-cell variability on the cell-to-cell diversity in RNA numbers decrease as the relative time-length of the open complex formation increases. Next, using a stochastic model of a 2-genes symmetric toggle switch, we show that the cell-to-cell diversity of the switching frequency due to cell-to-cell variability in RNA polymerase numbers also depends on the promoter kinetics. Finally, from the binarized protein numbers over time of 50-gene network models where genes interact by repression, we calculate the cell-to-cell variability of the mutual information and Lempel-Ziv complexity of the networks dynamics, and find that, while arising from the cell-to-cell variability in RNA polymerase numbers, these variability levels also depend on the promoter initiation kinetics. Given this, we hypothesize that E. coli may be capitalizing on the 2 rate-limiting steps’ nature of transcription initiation to tune the effects of extrinsic noise at the single gene, motifs, and large gene regulatory network levels.

Keywords

Transcription initiation Extrinsic noise Genetic circuits Mutual information Lempel-Ziv complexity 

Notes

Acknowledgements

Work supported by Academy of Finland (295027 ASR), Academy of Finland Key Project Funding (305342 ASR), Jane and Aatos Erkko Foundation (610536 ASR), Finnish Academy of Science and Letters (SO), and Tampere University of Technology President’s Graduate Program (SS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory of Biosystem Dynamics, BioMediTech InstituteTampere University of TechnologyTampereFinland
  2. 2.Multi-scaled Biodata Analysis and Modelling Research CommunityTampere University of TechnologyTampereFinland

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