Role of feedback inhibition in stabilizing the classical operon

Bliss, Richard; Painter, Page R.; Marr, Allen G.

doi:10.1016/0022-5193(82)90098-4

Cited by 144 publications

(119 citation statements)

References 14 publications

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“…The first repressive model of transcriptional regulation where a gene's expression is inhibited by its own protein product was proposed. This simple model has provided a practical framework for a plethora of subsequent studies exploring the dynamics of negative-feedback genetic systems [19][20][21][22][23][24]. Here, we extended the Goodwin framework to describe feedback systems with multiple negative mechanisms.…”

Section: Feedback Motifs and Mathematical Descriptionmentioning

confidence: 99%

Regulation of oscillation dynamics in biochemical systems with dual negative feedback loops

Nguyen

2012

J. R. Soc. Interface.

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Feedback controls are central to cellular regulation. Negative-feedback mechanisms are well known to underline oscillatory dynamics. However, the presence of multiple negativefeedback mechanisms is common in oscillatory cellular systems, raising intriguing questions of how they cooperate to regulate oscillations. In this work, we studied the dynamical properties of a set of general biochemical motifs with dual, nested negative-feedback structures. We showed analytically and then confirmed numerically that, in these motifs, each negativefeedback loop exhibits distinctly different oscillation-controlling functions. The longer, outer feedback loop was found to promote oscillations, whereas the short, inner loop suppresses and can even eliminate oscillations. We found that the position of the inner loop within the coupled motifs affects its repression strength towards oscillatory dynamics. Bifurcation analysis indicated that emergence of oscillations may be a strict parametric requirement and thus evolutionarily tricky. Investigation of the quantitative features of oscillations (i.e. frequency, amplitude and mean value) revealed that coupling negative feedback provides robust tuning of the oscillation dynamics. Finally, we demonstrated that the mitogen-activated protein kinase (MAPK) cascades also display properties seen in the general nested feedback motifs. The findings and implications in this study provide novel understanding of biochemical negative-feedback regulation in a mixed wiring context.

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Section: Feedback Motifs and Mathematical Descriptionmentioning

confidence: 99%

Regulation of oscillation dynamics in biochemical systems with dual negative feedback loops

Nguyen

2012

J. R. Soc. Interface.

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“…The multiplicity and stability of steady states have been analysed, and conditions for the existence of periodic solutions have been determined. For the case n = 3 with f (x n ) repressive, Bliss et al (1982) carried out a detailed analysis. These authors allowed time delays between the production of x 3 (effector molecule) and its effect on the production of x 1 (mRNA), and also between the production of x 1 and the subsequent production of x 2 (gene product).…”

Section: 2mentioning

confidence: 99%

“…A non-linear degradation of x 3 (effector molecule) was also included. Bliss et al (1982) determined a condition on parameters that ensures the stability of the unique steady state of this model. These authors then chose parameters that allowed the model to describe the tryptophan operon of E. coli.…”

Section: 2mentioning

confidence: 99%

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Modeling Transcriptional Control in Gene Networks—Methods, Recent Results, and Future Directions

Smolen

Baxter

Byrne

2000

Bulletin of Mathematical Biology

280

161

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Mathematical models are useful for providing a framework for integrating data and gaining insights into the static and dynamic behavior of complex biological systems such as networks of interacting genes. We review the dynamic behaviors expected from model gene networks incorporating common biochemical motifs, and we compare current methods for modeling genetic networks. A common modeling technique, based on simply modeling genes as ON-OFF switches, is readily implemented and allows rapid numerical simulations. However, this method may predict dynamic solutions that do not correspond to those seen when systems are modeled with a more detailed method using ordinary differential equations. Until now, the majority of gene network modeling studies have focused on determining the types of dynamics that can be generated by common biochemical motifs such as feedback loops or protein oligomerization. For example, these elements can generate multiple stable states for gene product concentrations, state-dependent responses to stimuli, circadian rhythms and other oscillations, and optimal stimulus frequencies for maximal transcription. In the future, as new experimental techniques increase the ease of characterization of genetic networks, qualitative modeling will need to be supplanted by quantitative models for specific systems.

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“…Given the relative importance of feedback in generating sustained oscillations in a variety of biological signaling pathways, we numerically solved a PDE representing the Goodwin model of negative feedback inhibition. Although more advanced models of oscillatory feedback in biological systems are well-established, 39 the Goodwin model's simplicity and reliance on a small number of parameters facilities its use in the partial differential equation modeling described here. Moreover, by using a PDE formation of this reaction system, we could control the coupling between populations of enzymes in the Goodwin system by varying substrate diffusion rates and distances.…”

Section: A Spatially Decoupled Diffusion-influenced Reaction Networkmentioning

confidence: 99%

Enzyme localization, crowding, and buffers collectively modulate diffusion-influenced signal transduction: Insights from continuum diffusion modeling

Kekenes‐Huskey

Eun

McCammon

2015

The Journal of Chemical Physics

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Biochemical reaction networks consisting of coupled enzymes connect substrate signaling events with biological function. Substrates involved in these reactions can be strongly influenced by diffusion "barriers" arising from impenetrable cellular structures and macromolecules, as well as interactions with biomolecules, especially within crowded environments. For diffusion-influenced reactions, the spatial organization of diffusion barriers arising from intracellular structures, non-specific crowders, and specific-binders (buffers) strongly controls the temporal and spatial reaction kinetics. In this study, we use two prototypical biochemical reactions, a Goodwin oscillator, and a reaction with a periodic source/sink term to examine how a diffusion barrier that partitions substrates controls reaction behavior. Namely, we examine how conditions representative of a densely packed cytosol, including reduced accessible volume fraction, non-specific interactions, and buffers, impede diffusion over nanometer length-scales. We find that diffusion barriers can modulate the frequencies and amplitudes of coupled diffusion-influenced reaction networks, as well as give rise to "compartments" of decoupled reactant populations. These effects appear to be intensified in the presence of buffers localized to the diffusion barrier. These findings have strong implications for the role of the cellular environment in tuning the dynamics of signaling pathways. C 2015 AIP Publishing LLC. [http://dx

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Role of feedback inhibition in stabilizing the classical operon

Cited by 144 publications

References 14 publications

Regulation of oscillation dynamics in biochemical systems with dual negative feedback loops

Regulation of oscillation dynamics in biochemical systems with dual negative feedback loops

Modeling Transcriptional Control in Gene Networks—Methods, Recent Results, and Future Directions

Enzyme localization, crowding, and buffers collectively modulate diffusion-influenced signal transduction: Insights from continuum diffusion modeling

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