DNA looping provides stability and robustness to the bacteriophage λ switch

Morelli, Marco J.; Wolde, Pieter Rein ten; Allen, Rosalind J.

doi:10.1073/pnas.0810399106

Cited by 47 publications

(53 citation statements)

References 48 publications

(72 reference statements)

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“…(57) to obtain the results given in Eq. (22) in the main text. To complete the general discussion here, let us derive an expression for the variance of ∆W ln kα .…”

Section: Exact Results For Problems With Linear Propensitiesmentioning

confidence: 99%

“…In fact Plyasunov and Arkin [5] already discuss in which cases it may be more efficient to use finite-differencing. Because of their long relaxation times, genetic switches are notoriously difficult to study in stochastic simulations [22]. A plethora of sophisticated schemes have been developed to address this problem [23][24][25], some of which could perhaps be extended to incorporate trajectory reweighting.…”

Section: Discussionmentioning

confidence: 99%

“…Solid lines are theoretical predictions from Eq. (22). Parameters are k = 2.76 min −1 , λ = 0.12 min −1 , ρ = 3.2 min −1 , µ = 0.016 min −1 , corresponding to the cro gene in a recent model of phage lambda [16].…”

Section: A Constitutive Gene Expressionmentioning

confidence: 99%

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Steady-state parameter sensitivity in stochastic modeling via trajectory reweighting

Warren

Allen

2012

The Journal of Chemical Physics

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Parameter sensitivity analysis is a powerful tool in the building and analysis of biochemical network models. For stochastic simulations, parameter sensitivity analysis can be computationally expensive, requiring multiple simulations for perturbed values of the parameters. Here, we use trajectory reweighting to derive a method for computing sensitivity coefficients in stochastic simulations without explicitly perturbing the parameter values, avoiding the need for repeated simulations. The method allows the simultaneous computation of multiple sensitivity coefficients. Our approach recovers results originally obtained by application of the Girsanov measure transform in the general theory of stochastic processes [A. Plyasunov and A. P. Arkin, J. Comp. Phys. 221, 724 (2007)]. We build on these results to show how the method can be used to compute steady-state sensitivity coefficients from a single simulation run, and we present various efficiency improvements. For models of biochemical signaling networks the method has a particularly simple implementation. We demonstrate its application to a signaling network showing stochastic focussing and to a bistable genetic switch, and present exact results for models with linear propensity functions.

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“…(57) to obtain the results given in Eq. (22) in the main text. To complete the general discussion here, let us derive an expression for the variance of ∆W ln kα .…”

Section: Exact Results For Problems With Linear Propensitiesmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Steady-state parameter sensitivity in stochastic modeling via trajectory reweighting

Warren

Allen

2012

The Journal of Chemical Physics

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“…Genes encoding the mechanisms of horizontal transmissionare strongly repressed some time after transfer, presumably to lower theirburden on the host metabolism [50,51]. We have discussed above on how seamless integration of the MGE in the chromosome will lower its cost and therefore increase its fitness.…”

Section: Domestication Of Mobile Elementsmentioning

confidence: 99%

The chromosomal accommodation and domestication of mobile genetic elements

Touchon¹,

Bobay²,

Rocha³

2014

Current Opinion in Microbiology

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Prokaryotes are constantly being infected by large mobile genetic elements such as conjugative elements and temperate phages. The fitness of these elements is tightly linked with the evolutionary success of the host. This leads to selectionagainst disruptive effectstheir integration might have on the organization and structure of the chromosome. Seamless genetic accommodation of the mobile elements also involves silencing infectious mechanisms and expressing functions adaptive to the host.Ironically, these characteristics favor the host ability to domesticate the mobile element.Recent data suggests that the domestication of mobile elements might be frequent.Importantly, it might affect the evolution of chromosome organization and drive the diversification of social traits.2

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“…Our calculations are based on a phage OR model [3]. Although DNA looping formed by cooperations between the left operators and the right operators affects the stability of the genetic switch [25], the OR model only includes the effects of the right operators, which are believed to play a critical role for lysogeny maintenance [26]. We will calculate bistable ranges for all binding energies, which are crucial in determining the affinity that proteins bind to phage genes.…”

Section: Introductionmentioning

confidence: 99%

Bifurcation Analysis for Phage Lambda with Binding Energy Uncertainty

Xue

et al. 2014

Computational Biology Journal

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In a phage genetic switch model, bistable dynamical behavior can be destroyed due to the bifurcation caused by inappropriately chosen model parameters. Since the values of many parameters with biological significance often cannot be accurately acquired, it is thus of fundamental importance to analyze how and to which extent the system dynamics is influenced by model parameters, especially those parameters pertaining to binding energies. In this paper, we apply a Jacobian method to investigate the relation between bifurcation and parameter uncertainties on a phage OR model. By introducing bistable range as a measure of system robustness, we find that RNA polymerase binding energies have the minimum bistable ranges among all the binding energies, which implies that the uncertainties on these parameters tend to demolish the bistability more easily. Moreover, parameters describing mutual prohibition between proteins Cro and CI have finite bistable ranges, whereas those describing self-prohibition have infinity bistable ranges. Hence, the former are more sensitive to parameter uncertainties than the latter. These results help to understand the influence of parameter uncertainties on the bifurcation and thus bistability.

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DNA looping provides stability and robustness to the bacteriophage λ switch

Cited by 47 publications

References 48 publications

Steady-state parameter sensitivity in stochastic modeling via trajectory reweighting

Steady-state parameter sensitivity in stochastic modeling via trajectory reweighting

The chromosomal accommodation and domestication of mobile genetic elements

Bifurcation Analysis for Phage Lambda with Binding Energy Uncertainty

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