<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>λ</mml:mi></mml:math>-prophage induction modeled as a cooperative failure mode of lytic repression

Chia, Nicholas; Golding, Ido; Goldenfeld, Nigel

doi:10.1103/physreve.80.030901

Cited by 7 publications

(8 citation statements)

References 41 publications

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“…It is believed that wild-type phage has quite a stable lysogenic state since lysogeny loss rate is even lower than 10 −8 per cell generation [27]. However, certain conditions such as the exposure to UV light [28,29], the presence of mitomycin C [30], or the starvation of the host cells [31] can induce phage from lysogeny to lysis. Mathematical models have been built to successfully explain stability and high efficiency of genetic switch.…”

Section: Phage Modelingmentioning

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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Section: Phage Modelingmentioning

confidence: 99%

Bifurcation Analysis for Phage Lambda with Binding Energy Uncertainty

Xue

et al. 2014

Computational Biology Journal

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“…The quantitative features of induction kinetics remain largely unexplored. One example of such feature is depicted in FIGURE 5A ((23); also see (54)). A culture of lysogens was irradiated with UV, and the fraction of cells induced (switched to lysis) was measured as a function of UV amount.…”

Section: The Life Cycle Of Bacteriophage Lambdamentioning

confidence: 99%

“…Using the formalism of reliability theory, one can write down the expected fraction of “failure” events as a function of the UV dose and arrive at the observed power law (FIGURE 5A). For more details see (23). …”

Section: The Life Cycle Of Bacteriophage Lambdamentioning

confidence: 99%

“…Experimental data (markers) is from wild type as well as different lambda mutants. Solid line is a fit to the theoretical expression using reliability theory (23), yielding a power law with an exponent of 4. Reprinted with permission from (23).…”

Section: Figurementioning

confidence: 99%

“…Solid line is a fit to the theoretical expression using reliability theory (23), yielding a power law with an exponent of 4. Reprinted with permission from (23). Copyright (2009) by the American Physical Society.…”

Section: Figurementioning

confidence: 99%

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Decision Making in Living Cells: Lessons from a Simple System

Golding

2011

Annu. Rev. Biophys.

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The life cycle of bacteriophage lambda serves as a simplified paradigm for cell-fate decisions. The ongoing quantitative, high-resolution experimental investigation of this life cycle has produced some important insights in recent years. These insights have to do with the way cells choose among alternative fates, how they maintain long-term memory of their gene-expression state, and how they switch from one stable state to another. The recent studies have also highlighted the role of spatiotemporal effects in cellular processes and the importance of distinguishing chemical stochasticity from possible “hidden variables” in cellular decision making.

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Single-molecule imaging of LexA degradation in Escherichia coli elucidates regulatory mechanisms and heterogeneity of the SOS response

Jones

Uphoff

2021

Nat Microbiol

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The bacterial SOS response stands as a paradigm of gene networks controlled by a master transcriptional regulator. Self-cleavage of the SOS repressor, LexA, induces a wide range of cell functions that are critical for survival and adaptation when bacteria experience stress conditions 1 , including DNA repair 2 , mutagenesis 3 , 4 , horizontal gene transfer 5 – 7 , filamentous growth, and the induction of bacterial toxins 8 – 12 , toxin-antitoxin systems 13 , virulence factors 6 , 14 , and prophages 15 – 17 . SOS induction is also implicated in biofilm formation and antibiotic persistence 11 , 18 – 20 . Considering the fitness burden of these functions, it is surprising that the expression of LexA-regulated genes is highly variable across cells 10 , 21 – 23 and that cell subpopulations induce the SOS response spontaneously even in the absence of stress exposure 9 , 11 , 12 , 16 , 24 , 25 . Whether this reflects a population survival strategy or a regulatory inaccuracy is unclear, as are the mechanisms underlying SOS heterogeneity. Here, we developed a single-molecule imaging approach based on a HaloTag fusion to directly monitor LexA inside live Escherichia coli cells, demonstrating the existence of 3 main states of LexA: DNA-bound stationary molecules, free LexA and degraded LexA species. These analyses elucidate the mechanisms by which DNA-binding and degradation of LexA regulate the SOS response in vivo. We show that self-cleavage of LexA occurs frequently throughout the population during unperturbed growth, rather than being restricted to a subpopulation of cells, which causes substantial cell-to-cell variation in LexA abundances. LexA variability underlies SOS gene expression heterogeneity and triggers spontaneous SOS pulses, which enhance bacterial survival in anticipation of stress.

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λ-prophage induction modeled as a cooperative failure mode of lytic repression

Cited by 7 publications

References 41 publications

Bifurcation Analysis for Phage Lambda with Binding Energy Uncertainty

Bifurcation Analysis for Phage Lambda with Binding Energy Uncertainty

Decision Making in Living Cells: Lessons from a Simple System

Single-molecule imaging of LexA degradation in Escherichia coli elucidates regulatory mechanisms and heterogeneity of the SOS response

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