Interplay between intrinsic noise and the stochasticity of the cell cycle in bacterial colonies

Oriol Canela-Xandri, Francesc Sagués, Javier Buceta

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Herein we report on the effects that different stochastic contributions induce in bacterial colonies in terms of protein concentration and production. In particular, we consider for what we believe to be the first time cell-to-cell diversity due to the unavoidable randomness of the cell-cycle duration and its interplay with other noise sources. To that end, we model a recent experimental setup that implements a protein dilution protocol by means of division events to characterize the gene regulatory function at the single cell level. This approach allows us to investigate the effect of different stochastic terms upon the total randomness experimentally reported for the gene regulatory function. In addition, we show that the interplay between intrinsic fluctuations and the stochasticity of the cell-cycle duration leads to different constructive roles. On the one hand, we show that there is an optimal value of protein concentration (alternatively an optimal value of the cell cycle phase) such that the noise in protein concentration attains a minimum. On the other hand, we reveal that there is an optimal value of the stochasticity of the cell cycle duration such that the coherence of the protein production with respect to the colony average production is maximized. The latter can be considered as a novel example of the recently reported phenomenon of diversity induced resonance.
Original languageEnglish
Pages (from-to)2459-68
Number of pages10
JournalBiophysical Journal
Volume98
Issue number11
DOIs
Publication statusPublished - 2 Jun 2010

Keywords / Materials (for Non-textual outputs)

  • Algorithms
  • Bacteria
  • Bacterial Proteins
  • Cell Cycle
  • Cell Division
  • Gene Expression Regulation
  • Models, Biological
  • Protein Biosynthesis
  • Stochastic Processes
  • Time Factors

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