Cellular noise

Cellular noise is a generic term designating random fluctuations in the rates of biochemical reactions, which can cause non-deterministic heterogeneity among genetically identical cells. Such fluctuations can either be detrimental to the accuracy of biological function or favourable to the sensitivity or adaptability of biological processes.

Latest Research and Reviews

  • Research | | open

    How cellular noise impacts metabolic trade-offs remains unknown. Here, the authors use a quantitative single-cell mass imaging strategy to reveal that cellular noise impacts cellular biomass and triacylglycerol accumulation, as well as protein and fatty-acid recycling under starvation, differently.

    • A. E. Vasdekis
    • , H. Alanazi
    • , A. M. Silverman
    • , C. J. Williams
    • , A. J. Canul
    • , J. B. Cliff
    • , A. C. Dohnalkova
    •  & G. Stephanopoulos
  • Research | | open

    Noisy gene expression leading to phenotypic variability can help organisms to survive in changing environments. Here, Patange et al. show that noisy expression of a stress response regulator, RpoS, allows E. coli cells to modulate their growth rates to survive future adverse environments.

    • Om Patange
    • , Christian Schwall
    • , Matt Jones
    • , Casandra Villava
    • , Douglas A. Griffith
    • , Andrew Phillips
    •  & James C. W. Locke
  • Research | | open

    Gene regulatory circuits (GRCs) regulate many biological processes including cell cycle, cell differentiation, and phenotypic switching. Stochasticity in the gene expression impacts the dynamics and functions of such GRCs. Vivek Kohar and Mingyang Lu from Jackson Laboratory have developed a systems-biology modeling method stochastic random circuit perturbation (sRACIPE), which takes the GRC topology as the only input, and simulates an ensemble of models with random kinetic parameters at multiple noise levels. Statistical analysis of the generated gene expressions reveals the basin of attraction and stability of various phenotypic states and their changes associated with intrinsic and extrinsic noises. Application of the method to single cell expression data from synthetic circuits and epithelial-mesenchymal transition in squamous cell carcinoma shows its potential in yielding new insights on the structure and function of gene regulatory networks.

    • Vivek Kohar
    •  & Mingyang Lu
  • Research | | open

    The drivers of growth rate variability in bacteria are yet unknown. Here, the authors present a theory to predict the growth dynamics of individual cells and use a stochastic cell model integrating metabolism, gene expression and replication to identify the processes that underlie growth variation.

    • Philipp Thomas
    • , Guillaume Terradot
    • , Vincent Danos
    •  & Andrea Y. Weiße

News and Comment