Noise-based switches and amplifiers for gene expression.

By Jeff Hasty, J. Pradines, M. Dolnik, James J. Collins. Proceedings of the National Academy of Sciences of the United States of America, 2000

639 citations recorded by the source corpus.

The regulation of cellular function is often controlled at the level of gene transcription. Such genetic regulation usually consists of interacting networks, whereby gene products from a single network can act to control their own expression or the production of protein in another network. Engineered control of cellular function through the design and manipulation of such networks lies within the constraints of current technology. Here we develop a model describing the regulation of gene expression and elucidate the effects of noise on the formulation. We consider a single network derived from bacteriophage lambda and construct a two-parameter deterministic model describing the temporal evolution of the concentration of lambda repressor protein. Bistability in the steady-state protein concentration arises naturally, and we show how the bistable regime is enhanced with the addition of the first operator site in the promotor region. We then show how additive and multiplicative external noise can be used to regulate expression. In the additive case, we demonstrate the utility of such control through the construction of a protein switch, whereby protein production is turned "on" and "off" by using short noise pulses. In the multiplicative case, we show that small deviations in the transcription rate can lead to large fluctuations in the production of protein, and we describe how these fluctuations can be used to amplify protein production significantly. These results suggest that an external noise source could be used as a switch and/or amplifier for gene expression. Such a development could have important implications for gene therapy.

Subfields: Genetics, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/c043a013194cc6f52a815e0ec42e4d5861d98942

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

In Statistical and Nonlinear Physics

Citations in the corpus

Physiological heterogeneity in biofilms (2008, background): “This variation could lead to a broad distribution of expression levels, or it could enable a bistable switch, in which genes are either 'on' or 'off' (Ref”
Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations. (2001, background): “The reaction given by Eq. 29 is generally assumed to be fast compared to all other reactions (McAdams and Arkin, 1997; Hasty et al., 2000, 2001a ).”
Control, exploitation and tolerance of intracellular noise (2002, background): “Feedback can also amplify the effects of noise by autocatalytic mechanism”
Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion (2001, background): “The continual switching from the off- to the on-state over the entire range of inducer concentration might classify the autocatalytic switch as a noise-based switch to differentiate it from the toggle switch (Hasty et al., 2000).”
Metabolomics in systems biology. (2003, methodology): “Here, fluctuations were added post-hoc to deterministic rate equations; therefore, the noise strength was an adjustable parameter (33, 35).”
Adaptive computation and machine learning (1998, background): “For instance, we do not entirely understand the role that noise plays in gene regulation [383, 243].”
A model of periodic oscillation for genetic regulatory systems (2002, background): “In particular, Hasty et al. showed numerically that a genetic oscillator can be constructed by utilizing hysteretic property of the phage circuitry with repressor as a function of time [12], [13].”
Stochastic gene expression and its consequences (2008, background): “In that case, occasional large fluctuations in gene expression can serve to randomly activate the switch and push the cell into the on state (Hasty et al., 2000).”
Stochastic switching as a survival strategy in fluctuating environments (2008, background): “Bistable gene networks provide promising experimental systems in which to implement discrete phenotypic states, and the stochastic nature of gene expression drives rare transitions between these state”