Control Analysis of Bacterial Chemotaxis Signaling

Yi, Tau Mu; Andrews, Burton W.; Iglesias, Pablo A.

doi:10.1016/s0076-6879(06)22006-8

Cited by 10 publications

(11 citation statements)

References 14 publications

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“…Regulation is achieved through two negative feedback terms representing proportional feedback ( k 5 â ) and integral feedback ( k 3 ba ) [20]. The variable b is involved in the integral feedback control loop with the second differential equation ensuring that the average steady-state levels of a ( a̅ ) will tend to the fixed value a ss .…”

Section: Resultsmentioning

confidence: 99%

Modeling Robustness Tradeoffs in Yeast Cell Polarization Induced by Spatial Gradients

Chou

Nie

2008

PLoS ONE

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Cells localize (polarize) internal components to specific locations in response to external signals such as spatial gradients. For example, yeast cells form a mating projection toward the source of mating pheromone. There are specific challenges associated with cell polarization including amplification of shallow external gradients of ligand to produce steep internal gradients of protein components (e.g. localized distribution), response over a broad range of ligand concentrations, and tracking of moving signal sources. In this work, we investigated the tradeoffs among these performance objectives using a generic model that captures the basic spatial dynamics of polarization in yeast cells, which are small. We varied the positive feedback, cooperativity, and diffusion coefficients in the model to explore the nature of this tradeoff. Increasing the positive feedback gain resulted in better amplification, but also produced multiple steady-states and hysteresis that prevented the tracking of directional changes of the gradient. Feedforward/feedback coincidence detection in the positive feedback loop and multi-stage amplification both improved tracking with only a modest loss of amplification. Surprisingly, we found that introducing lateral surface diffusion increased the robustness of polarization and collapsed the multiple steady-states to a single steady-state at the cost of a reduction in polarization. Finally, in a more mechanistic model of yeast cell polarization, a surface diffusion coefficient between 0.01 and 0.001 µm2/s produced the best polarization performance, and this range is close to the measured value. The model also showed good gradient-sensitivity and dynamic range. This research is significant because it provides an in-depth analysis of the performance tradeoffs that confront biological systems that sense and respond to chemical spatial gradients, proposes strategies for balancing this tradeoff, highlights the critical role of lateral diffusion of proteins in the membrane on the robustness of polarization, and furnishes a framework for future spatial models of yeast cell polarization.

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Section: Resultsmentioning

confidence: 99%

Modeling Robustness Tradeoffs in Yeast Cell Polarization Induced by Spatial Gradients

Chou

Nie

2008

PLoS ONE

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“…The result is that receptors adapt to the same fixed activity level for any steady stimulus, which has been shown to be a requirement for optimal chemotactic performance in an unpredictable environment [13]. Control theorists recognize this mechanism for achieving precise adaptation as an example of integral feedback [14,15]. Several recent studies [16-20], have addressed the dynamics and degree of precision of adaptation and what these can teach us about, e.g ., receptor organization.…”

Section: Fundamental Limits Of Sensitivitymentioning

confidence: 99%

Responding to chemical gradients: bacterial chemotaxis

Sourjik

Wingreen

2012

Current Opinion in Cell Biology

470

425

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Chemotaxis allows bacteria to follow gradients of nutrients and other environmental stimuli. The bacterium Escherichia coli performs chemotaxis via a run-and-tumble strategy in which sensitive temporal comparisons lead to a biased random walk, with longer runs in the preferred gradient direction. The chemotaxis network of E. coli has developed over the years into one of the most thoroughly studied model systems for signal transduction and behaviour, yielding general insights into such properties of cellular networks as signal amplification, signal integration, and robustness. Despite its relative simplicity, the operation of the E. coli chemotaxis network is highly refined and evolutionarily optimized at many levels. For example, recent studies revealed that the network adjusts its signaling properties dependent on the extracellular environment, apparently to optimize chemotaxis under particular conditions. The network can even utilize potentially detrimental stochastic fluctuations in protein levels and reaction rates to maximize the chemotactic performance of the population.

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“…Regulation is achieved through two negative feedback terms representing proportional feedback (k 5 â) and integral feedback (k 3 ba) [20]. The variable b is involved in the integral feedback control loop with the second differential equation ensuring that the average steady-state levels of a (ā) will tend to the fixed value a ss .…”

Section: Lamentioning

confidence: 99%

Modeling Robustness Tradeoffs in Yeast Cell Polarization Induced by Spatial Gradients

Chou¹,

Nie²,

Yi³

2014

Investigations in Yeast Functional Genomics and Molecular Biology

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Cells localize (polarize) internal components to specific locations in response to external signals such as spatial gradients. For example, yeast cells form a mating projection toward the source of mating pheromone. There are specific challenges associated with cell polarization including amplification of shallow external gradients of ligand to produce steep internal gradients of protein components (e.g. localized distribution), response over a broad range of ligand concentrations, and tracking of moving signal sources. In this work, we investigated the tradeoffs among these performance objectives using a generic model that captures the basic spatial dynamics of polarization in yeast cells, which are small. We varied the positive feedback, cooperativity, and diffusion coefficients in the model to explore the nature of this tradeoff. Increasing the positive feedback gain resulted in better amplification, but also produced multiple steady-states and hysteresis that prevented the tracking of directional changes of the gradient. Feedforward/feedback coincidence detection in the positive feedback loop and multi-stage amplification both improved tracking with only a modest loss of amplification. Surprisingly, we found that introducing lateral surface diffusion increased the robustness of polarization and collapsed the multiple steady-states to a single steady-state at the cost of a reduction in polarization. Finally, in a more mechanistic model of yeast cell polarization, a surface diffusion coefficient between 0.01 and 0.001 mm 2 /s produced the best polarization performance, and this range is close to the measured value. The model also showed good gradient-sensitivity and dynamic range. This research is significant because it provides an in-depth analysis of the performance tradeoffs that confront biological systems that sense and respond to chemical spatial gradients, proposes strategies for balancing this tradeoff, highlights the critical role of lateral diffusion of proteins in the membrane on the robustness of polarization, and furnishes a framework for future spatial models of yeast cell polarization.

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Control Analysis of Bacterial Chemotaxis Signaling

Cited by 10 publications

References 14 publications

Modeling Robustness Tradeoffs in Yeast Cell Polarization Induced by Spatial Gradients

Modeling Robustness Tradeoffs in Yeast Cell Polarization Induced by Spatial Gradients

Responding to chemical gradients: bacterial chemotaxis

Modeling Robustness Tradeoffs in Yeast Cell Polarization Induced by Spatial Gradients

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