Hysteretic and graded responses in bacterial two‐component signal transduction

Igoshin, Oleg A.; Alves, Rui; Savageau, Michael A.

doi:10.1111/j.1365-2958.2008.06221.x

Cited by 64 publications

(117 citation statements)

References 86 publications

(131 reference statements)

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“…Our results and those of Igoshin et al 9 and of Kierzek et al 15 indicate that this property can lead to bistability.…”

Section: Discussionsupporting

confidence: 63%

“…Batchelor & Goulian 7 provided experimental evidence for this surprising behaviour. Hoyle et al 8 and Igoshin et al 9 analysed the bistability of two-component signalling transduction systems when coupled to autoregulatory gene expression. Hoyle et al 8 further demonstrated this behaviour experimentally, using a synthetic two-component system.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic simulation of prokaryotic two-component signalling indicates stochasticity-induced active-state locking and growth-rate dependent bistability

et al. 2014

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be Signal transduction by prokaryotes almost exclusively relies on two-component systems for sensing and responding to (extracellular) signals. Here, we use stochastic models of two-component systems to better understand the impact of stochasticity on the fidelity and robustness of signal transmission, the outcome of autoregulatory gene expression and the influence of cell growth and division. We report that two-component systems are remarkably robust against copy number fluctuations of the signalling proteins they are composed of, which enhances signal transmission fidelity. Furthermore, we find that due to stochasticity these systems can get locked in an active state for extended time periods when (initially high) signal levels drop to zero. This behaviour can contribute to a bet-hedging adaptation strategy, aiding survival in fluctuating environments. Additionally, autoregulatory gene expression can cause two-component systems to become bistable at realistic parameter values. As a result, two sub-populations of cells can co-exist-active and inactive cells, which contributes to fitness in unpredictable environments.Bistability proved robust with respect to cell growth and division, and is tunable by the growth rate.In conclusion, our results indicate how single cells can cope with the inevitable stochasticity occurring in the activity of their two-component systems. They are robust to disadvantageous fluctuations that scramble signal transduction and they exploit beneficial stochasticity that generates fitness-enhancing heterogeneity across an isogenic population of cells.

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“…Our results and those of Igoshin et al 9 and of Kierzek et al 15 indicate that this property can lead to bistability.…”

Section: Discussionsupporting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Stochastic simulation of prokaryotic two-component signalling indicates stochasticity-induced active-state locking and growth-rate dependent bistability

et al. 2014

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“…Multiple mechanisms have been proposed for the EnvZ/ OmpR system [3,12,32]. We will consider the mechanism given in figure 2 which was proposed in [1,12].…”

Section: Envz/ompr Signalling Systemmentioning

confidence: 99%

Stochastic analysis of biochemical reaction networks with absolute concentration robustness

Anderson

Enciso

Johnston

2014

J. R. Soc. Interface.

133

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It has recently been shown that structural conditions on the reaction network, rather than a 'fine-tuning' of system parameters, often suffice to impart 'absolute concentration robustness' (ACR) on a wide class of biologically relevant, deterministically modelled mass-action systems. We show here that fundamentally different conclusions about the long-term behaviour of such systems are reached if the systems are instead modelled with stochastic dynamics and a discrete state space. Specifically, we characterize a large class of models that exhibit convergence to a positive robust equilibrium in the deterministic setting, whereas trajectories of the corresponding stochastic models are necessarily absorbed by a set of states that reside on the boundary of the state space, i.e. the system undergoes an extinction event. If the time to extinction is large relative to the relevant timescales of the system, the process will appear to settle down to a stationary distribution long before the inevitable extinction will occur. This quasi-stationary distribution is considered for two systems taken from the literature, and results consistent with ACR are recovered by showing that the quasi-stationary distribution of the robust species approaches a Poisson distribution.

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“…Therefore, the expression levels of the states do not affect the frequency and bias of switching. Systems that generate bimodality through feedback can, under the right conditions, produce some behaviours that have been observed, such as a graded unimodal response [41][42][43][44][45][46] . However, the feedback makes it more difficult to tune phenotypic variation and mean expression.…”

Section: Discussionmentioning

confidence: 99%

Modulating the frequency and bias of stochastic switching to control phenotypic variation

et al. 2014

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Mechanisms that control cell-to-cell variation in gene expression ('phenotypic variation') can determine a population's growth rate, robustness, adaptability and capacity for complex behaviours. Here we describe a general strategy (termed FABMOS) for tuning the phenotypic variation and mean expression of cell populations by modulating the frequency and bias of stochastic transitions between 'OFF' and 'ON' expression states of a genetic switch. We validated the strategy experimentally using a synthetic fim switch in Escherichia coli. Modulating the frequency of switching can generate a bimodal (low frequency) or a unimodal (high frequency) population distribution with the same mean expression. Modulating the bias as well as the frequency of switching can generate a spectrum of bimodal and unimodal distributions with the same mean expression. This remarkable control over phenotypic variation, which cannot be easily achieved with standard gene regulatory mechanisms, has many potential applications for synthetic biology, engineered microbial ecosystems and experimental evolution.

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Hysteretic and graded responses in bacterial two‐component signal transduction

Cited by 64 publications

References 86 publications

Stochastic simulation of prokaryotic two-component signalling indicates stochasticity-induced active-state locking and growth-rate dependent bistability

Stochastic simulation of prokaryotic two-component signalling indicates stochasticity-induced active-state locking and growth-rate dependent bistability

Stochastic analysis of biochemical reaction networks with absolute concentration robustness

Modulating the frequency and bias of stochastic switching to control phenotypic variation

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