A Mathematical Description of Bacterial Chemotaxis in Response to Two Stimuli

Park, Jeungeun; Aminzare, Zahra

doi:10.1007/s11538-021-00965-6

Cited by 5 publications

(2 citation statements)

References 62 publications

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“…Natural sources of chemoeffectors (attractants and repellents) will generally be sources of more than one, and the resulting presence of multiple concentration gradients creates additional opportunities for sophisticated signal processing. When faced with two opposing attractant gradients microbes must choose between the two, with outcomes potentially dependent on previous environmental exposure and resulting receptor expression [21][22][23] . In the presence of parallel attractant and repellant gradients, microbes must make a similar choice between net attraction or repulsion [24][25][26][27][28][29] , with chemotaxis to an intermediate distance from the source or a 'bet-hedging' split into attracted and repelled subpopulations also being possible outcomes 25 .…”

Section: Introductionmentioning

confidence: 99%

Repulsion from slow-diffusing nutrients improves chemotaxis towards moving sources

Bloxham,

Lee,

Gore

2024

Preprint

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Chemotaxis, or the following of chemical concentration gradients, is essential for microbes to locate nutrients. A microbe can swim in the direction of increasing nutrient concentrations to reach the source. However, microbes often display paradoxical behaviors, such as Escherichia coli being repulsed by several amino acids. Here, we explore chemotaxis towards a moving target and demonstrate that repulsion from a nutrient can actually improve chemotaxis towards its source. Because a moving source leaves most of the nutrient plume behind it, simply following the concentration gradient produces inefficient intercept trajectories. However, when attraction to a fast-diffusing molecule and repulsion from a slow-diffusing molecule are combined, motion in a new direction emerges and intercept times are significantly reduced. When the source is moving faster than the microbe can swim this differential strategy can even be essential to ever intercepting the source at all. Finally, we leverage existing data to show that E. coli is attracted to fast-diffusing amino acids and repulsed by slow-diffusing ones, suggesting it may utilize a differential strategy and providing a possible explanation for its repulsion from certain amino acids. Our results thus illuminate new possibilities in how microbes can integrate signals from multiple concentration gradients and propose a new strategy by which microbe may accomplish the difficult task of intercepting a moving target.

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Section: Introductionmentioning

confidence: 99%

Repulsion from slow-diffusing nutrients improves chemotaxis towards moving sources

Bloxham,

Lee,

Gore

2024

Preprint

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“…While several researchers have studied bacterial chemotactic responses in the presence of multiple stimuli (e.g., Kalinin et al, 2010; Mowbray & Koshland, 1987; Strauss et al, 1995), they did not implement a multiscale model to connect molecular level details of cell signal processing to predict bacteria population‐scale migration direction. Recently, researchers have been using mathematical models to correlate chemotaxis signaling at the molecular level to population density dynamics in the study of bacteria response to multiple stimuli (Park & Aminzare, 2022; Zhang et al, 2019). However, neither study investigated bacteria responses to a combination of attractant and repellent.…”

Section: Introductionmentioning

confidence: 99%

Escherichia coli chemotaxis to competing stimuli in a microfluidic device with a constant gradient

Zhao

Ford

2022

Biotech & Bioengineering

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In natural systems bacteria are exposed to many chemical stimulants; some attract chemotactic bacteria as they promote survival, while others repel bacteria because they inhibit survival. When faced with a mixture of chemoeffectors, it is not obvious which direction the population will migrate. Predicting this direction requires an understanding of how bacteria process information about their surroundings. We used a multiscale mathematical model to relate molecular level details of their twocomponent signaling system to the probability that an individual cell changes its swimming direction to the chemotactic velocity of a bacterial population. We used a microfluidic device designed to maintain a constant chemical gradient to compare model predictions to experimental observations. We obtained parameter values for the multiscale model of Escherichia coli chemotaxis to individual stimuli, α-methylaspartate and nickel ion, separately. Then without any additional fitting parameters, we predicted bacteria response to chemoeffector mixtures. Migration of E. coli toward α-methylaspartate was modulated by adding increasing concentrations of nickel ion. Thus, the migration direction was controlled by the relative concentrations of competing chemoeffectors in a predictable way. This study demonstrated the utility of a multiscale model to predict the migration direction of bacteria in the presence of competing chemoeffectors.

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