Statistical parameter inference of bacterial swimming strategies

Seyrich, Maximilian; Alirezaeizanjani, Zahra; Beta, Carsten; Stark, Holger

doi:10.1088/1367-2630/aae72c

Cited by 16 publications

(20 citation statements)

References 46 publications

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“…Third, in our generalized Smoluchowski equation the swimming speed is a constant as it is commonly done for the E coli . bacterium also during chemotaxis [33,35,40]. However, some bacteria are known to couple their swimming speed to the concentration of a chemical field [84][85][86], a strategy which is called chemokinesis.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, it would be interesting to extend our approach to chemoattractants to which E.coli is not perfectly adapted such as serine [33,40,87]. For this chemoattractant the mean tumble rate drops as the concentration of serine increases.…”

Section: Discussionmentioning

confidence: 99%

“…This chemoattractant is widely used, e.g. in the seminal experiments of [33,34,38,62] and also in experiments of [39,40]. For the detailed derivation we refer to appendix A and present the final result…”

Section: Markovian Response Theory For Tumble Ratementioning

confidence: 99%

“…Bacteria perform chemotaxis, the ability to sense and respond to chemical gradients in order to find better living conditions. They realize the chemotactic drift motion along a chemical gradient by elongated run phases if the environment becomes more favorable while runs are shortened in the opposite case [39][40][41]. The internal chemotaxis machinery of the bacterium senses and compares the nutrient concentration in time, which is rationalized in a linear response theory for the tumble rate [34,[42][43][44].…”

Section: Introductionmentioning

confidence: 99%

“…The internal chemotaxis machinery of the bacterium senses and compares the nutrient concentration in time, which is rationalized in a linear response theory for the tumble rate [34,[42][43][44]. More recently, a second chemotaxis strategy, called angle bias, has been reported [36,39,40]. The mean reorientation angle during tumbling is reduced if the bacterium swims along a chemical gradient and increased in the opposite case.…”

Section: Introductionmentioning

confidence: 99%

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Traveling concentration pulses of bacteria in a generalized Keller–Segel model

2019

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We formulate a Markovian response theory for the tumble rate of a bacterium moving in a chemical field and use it in the Smoluchowski equation. Based on a multipole expansion for the one-particle distribution function and a reaction-diffusion equation for the chemoattractant field, we derive a polarization extended model, which also includes the recently discovered angle bias. In the adiabatic limit we recover a generalized Keller-Segel equation with diffusion and chemotactic coefficients that depend on the microscopic swimming parameters. Requiring the tumble rate to be positive, our model introduces an upper bound for the chemotactic drift velocity, which is no longer singular as in the original Keller-Segel model. Solving the Keller-Segel equations numerically, we identify traveling bacterial concentration pulses, for which we do not need a second, signaling chemical field nor a singular chemotactic drift velocity as demanded in earlier publications. We present an extensive study of the traveling pulses and demonstrate how their speeds, widths, and heights depend on the microscopic parameters. Most importantly, we discover a maximum number of bacteria that the pulse can sustain-the maximum carrying capacity. Finally, by tuning our parameters, we are able to match the experimental realization of the traveling bacterial pulse.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Markovian Response Theory For Tumble Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Traveling concentration pulses of bacteria in a generalized Keller–Segel model

2019

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Active oscillations in microscale navigation

Wan

2023

Anim Cogn

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Living organisms routinely navigate their surroundings in search of better conditions, more food, or to avoid predators. Typically, animals do so by integrating sensory cues from the environment with their locomotor apparatuses. For single cells or small organisms that possess motility, fundamental physical constraints imposed by their small size have led to alternative navigation strategies that are specific to the microscopic world. Intriguingly, underlying these myriad exploratory behaviours or sensory functions is the onset of periodic activity at multiple scales, such as the undulations of cilia and flagella, the vibrations of hair cells, or the oscillatory shape modes of migrating neutrophils. Here, I explore oscillatory dynamics in basal microeukaryotes and hypothesize that these active oscillations play a critical role in enhancing the fidelity of adaptive sensorimotor integration.

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Self-propelled slender objects can measure flow signals net of self-motion

Cavaiola

Mazzino

2021

Physics of Fluids

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The perception of hydrodynamic signals by self-propelled objects is a problem of paramount importance ranging from the field of bio-medical engineering to bio-inspired intelligent navigation. By means of a state-of-the-art fully resolved immersed boundary method, we propose different models for fully coupled self-propelled objects (swimmers, in short), behaving either as “pusher” or as “puller.” The proposed models have been tested against known analytical results in the limit of Stokes flow, finding excellent agreement. Once tested, our more realistic model has been exploited in a chaotic flow field up to a flow Reynolds number of 10, a swimming number ranging between zero (i.e., the swimmer is freely moving under the action of the underlying flow in the absence of propulsion) and one (i.e., the swimmer has a relative velocity with respect to the underlying flow velocity of the same order of magnitude as the underlying flow), and different swimmer inertia measured in terms of a suitable definition of the swimmer Stokes number. Our results show the following: (i) pusher and puller reach different swimming velocities for the same, given, propulsive force: while for pusher swimmers, an effective slender body theory captures the relationship between swimming velocity and propulsive force, this is not for puller swimmers. (ii) While swimming, pusher and puller swimmers possess a different distribution of the vorticity within the wake. (iii) For a wide range of flow/swimmer Reynolds numbers, both pusher and puller swimmers are able to sense hydrodynamic signals with good accuracy.

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Statistical parameter inference of bacterial swimming strategies

Cited by 16 publications

References 46 publications

Traveling concentration pulses of bacteria in a generalized Keller–Segel model

Traveling concentration pulses of bacteria in a generalized Keller–Segel model

Active oscillations in microscale navigation

Self-propelled slender objects can measure flow signals net of self-motion

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