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Leader cells in collective chemotaxis: optimality and tradeoffs Camley, Brian
Description
Clusters of cells can work together in order to follow a signal gradient, chemotaxing even when single cells do not. This behavior is robust over many cell types and many signals, including gradients of extracellular matrix stiffness (durotaxis) and electrical potential (galvanotaxis). Cells in different regions of collectively migrating neural crest streams show different gene expression profiles, suggesting that cells may specialize to leader and follower roles in collective chemotaxis. We use a simple mathematical model to understand when this specialization would be advantageous. In our model, leader cells sense the gradient with an accuracy that depends on the kinetics of ligand-receptor binding while follower cells attempt to follow the cluster's direction with a finite error. Intuitively, specialization into leaders and followers should be optimal when a few cells have much more information than the rest of the cluster, such as in the presence of a sharp transition from one chemical concentration to another. We do find this - but also find that high levels of specialization can be optimal in the opposite limit of a very shallow gradient. This occurs because in a sufficiently shallow gradient, each leader cell has such little information about the gradient direction that - after a sufficient number of leaders are created - adding leader cells adds more noise to the cluster motion than adding a follower cell. There is also an important tradeoff: clusters have to choose between speed in following a gradient and ability to reorient quickly. We find that clusters with only a few leaders can take orders of magnitude more time to reorient than all-leader clusters.
Item Metadata
Title |
Leader cells in collective chemotaxis: optimality and tradeoffs
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2019-06-17T11:19
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Description |
Clusters of cells can work together in order to follow a signal
gradient, chemotaxing even when single cells do not. This behavior is
robust over many cell types and many signals, including gradients of
extracellular matrix stiffness (durotaxis) and electrical potential
(galvanotaxis). Cells in different regions of collectively migrating
neural crest streams show different gene expression profiles,
suggesting that cells may specialize to leader and follower roles in
collective chemotaxis. We use a simple mathematical model to
understand when this specialization would be advantageous. In our
model, leader cells sense the gradient with an accuracy that depends
on the kinetics of ligand-receptor binding while follower cells
attempt to follow the cluster's direction with a finite error.
Intuitively, specialization into leaders and followers should be
optimal when a few cells have much more information than the rest of
the cluster, such as in the presence of a sharp transition from one
chemical concentration to another. We do find this - but also find
that high levels of specialization can be optimal in the opposite
limit of a very shallow gradient. This occurs because in a
sufficiently shallow gradient, each leader cell has such little
information about the gradient direction that - after a sufficient
number of leaders are created - adding leader cells adds more noise to
the cluster motion than adding a follower cell. There is also an
important tradeoff: clusters have to choose between speed in following
a gradient and ability to reorient quickly. We find that clusters with
only a few leaders can take orders of magnitude more time to reorient
than all-leader clusters.
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Extent |
44.0 minutes
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: Johns Hopkins University
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Series | |
Date Available |
2019-12-15
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0387110
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Researcher
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International