Cellular memory in eukaryotic chemotaxis depends on the background chemoattractant concentration

Karmakar, Richa; Tang, Man-Ho; Yue, Haicen; Lombardo, Daniel M.; Karanam, Aravind; Camley, Brian A.; Groisman, Alex; Rappel, Wouter‐Jan

doi:10.1103/physreve.103.012402

Cited by 10 publications

(15 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, Eq. ( 7) tells that variations of chemoattractant and bacterial waves are directly linked, hence corroborating results obtained in the analysis of chemotactic Dictyostelium colonies [40]. The same study showed that by increasing background level, the directed propagation can be suppressed, due to memory inactivation.…”

Section: Analytical Solutions Through the Extended F-expansion Methodssupporting

confidence: 81%

“…We propose Eqs. (3) as a viable generalized chemotaxis model that incorporates several aspects of bacterial collective motion not discussed in previous models [3,15,[28][29][30][31][33][34][35][36][37][38][39][40][41][42].…”

Section: The Modelmentioning

confidence: 99%

“…1 at the time t = 100. Bell-shape waves have been predicted analytically [24,[26][27][28][29][30][36][37][38]42,47], numerically [16,19,28,30,36,40,43,48], and experimentally observed [15,19,20,30] in one dimensional chemotactic systems. Traveling waves in reactive systems are either matter carriers or information conveyors, the bellshaped profile obtained here may explain collective bands of bacteria usually observed in reactive systems.…”

Section: Existence and Dynamical Behavior Of Solutions Eqs(11)-(17)mentioning

confidence: 99%

See 2 more Smart Citations

Step, dip, and bell-shape traveling waves in a (2 + 1)-chemotaxis model with traction and long-range diffusion

Kuipou

Didier

Mohamadou

et al. 2021

Preprint

View full text Add to dashboard Cite

In this paper, a new (2 + 1)-dimensional chemotaxis model is introduced, the focus being the understanding of influences of cooperative mechanisms from traction forces, long-range diffusion to chemotaxis on the dynamical characteristics of waves and their transport. Applying the F-expansion method, three families of new traveling wave solutions of bacterial density and chemoattractant concentration are constructed, including step, dip, and bell-shape wave profiles. The dependence of the conditions of existence of our solutions with respect to the model parameters is fully clarified. We found that traction and long-range diffusion slow down the waves and entail the transport of a small number of particles. Surprisingly, the long-range diffusion increases the thickness of the wave but does not alter its magnitude. Amongst families of solutions constructed, dip waves travel faster may be used to explain fast coordination amongst particles. As they support the transport of large amounts of cells, step waves could explain the transport of particles in high dense media. Intensive numerical simulations corroborate with a pretty much accuracy our theoretical analysis, confirming the robustness of our predictions. Traction, long-range diffusion and chemotaxis deeply affect the wave dynamics, they must be taken into account for a better understanding of chemotaxis systems.

show abstract

Section: Analytical Solutions Through the Extended F-expansion Methodssupporting

confidence: 81%

Section: The Modelmentioning

confidence: 99%

Section: Existence and Dynamical Behavior Of Solutions Eqs(11)-(17)mentioning

confidence: 99%

See 1 more Smart Citation

Step, dip, and bell-shape traveling waves in a (2 + 1)-chemotaxis model with traction and long-range diffusion

Kuipou

Didier

Mohamadou

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Here, we note that the DAI distribution of the control is concentrated at the extremes of −1 and 1, this is an expected and well-known consequence of the cosine in its definition, as a uniform distribution of angles produce a nonuniform distribution of cosines that is more concentrated at the extremes. 9,55,65,66 Extracellular combination of the chemical and fluidic cues creates regions where the chemical cue becomes below the cellular sensing limit Chemical cues in the cellular microenvironment are transported by not only diffusion but also interstitial fluid flow. 56,57,67 To characterize this complex extra-cellular environment, the concentration profiles of a chemical cue in the presence of the flow on the microfluidic platform are measured and predicted by using FITC-conjugated dextran of 10 kDa in Fig.…”

Section: Creation Of a Cellular Microenvironment With Controlled Chem...mentioning

confidence: 99%

Cells function as a ternary logic gate to decide migration direction under integrated chemical and fluidic cues

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In order to quantify the cellular directional accuracy to an environmental cue, we use a directional accuracy index (DAI; see Materials and methods) as defined in Figure 1B. Here, we note that the DAI distribution of the control is concentrated at the extremes of -1 and 1, this is an expected and well-known consequence of the cosine in its definition, as a uniform distribution of angles produce a nonuniform distribution of cosines that is more concentrated at the extremes [38,47,50,51].…”

Section: Creation Of a Cellular Microenvironment With Controlled Chem...mentioning

confidence: 99%

Cells function as a ternary logic gate to decide migration direction under integrated chemical and fluidic cues

Moon

Saha

Mugler

et al. 2022

Preprint

View full text Add to dashboard Cite

Cells sense various environmental cues and process intracellular signals to decide their migration direction in many physiological and pathological processes. Although several signaling molecules have been identified in these directed migrations, it still remains elusive how cells decipher multiple cues, specifically chemical and fluidic cues. Here, we investigated the cellular signal processing machinery by reverse-engineering directed cell migration under integrated chemical and fluidic cues. We exposed controlled chemical and fluidic cues to cells using a microfluidic platform and analyzed the extracellular coupling of the cues with respect to the cellular detection limit. Then, the cell’s migratory behavior was reverse-engineered to build the cell’s intrinsic signal processing system as a logic gate. Our primary finding is that the cellular signal processing machinery functions as a ternary logic gate to decipher integrated chemical and fluidic cues. The proposed framework of the ternary logic gate suggests a systematic approach to understand how cells decode multiple cues to make decisions in migration.

show abstract

Cellular memory in eukaryotic chemotaxis depends on the background chemoattractant concentration

Cited by 10 publications

References 36 publications

Step, dip, and bell-shape traveling waves in a (2 + 1)-chemotaxis model with traction and long-range diffusion

Step, dip, and bell-shape traveling waves in a (2 + 1)-chemotaxis model with traction and long-range diffusion

Cells function as a ternary logic gate to decide migration direction under integrated chemical and fluidic cues

Cells function as a ternary logic gate to decide migration direction under integrated chemical and fluidic cues

Contact Info

Product

Resources

About