Paper: Sep 14,2024
q-bio.CB
ID:2409.09342
Persistent pseudopod splitting is an effective chemotaxis strategy in shallow gradients
Single-cell organisms and various cell types use a range of motility modes
when following a chemical gradient, but it is unclear which mode is best suited
for different gradients. Here, we model directional decision-making in
chemotactic amoeboid cells as a stimulus-dependent actin recruitment contest.
Pseudopods extending from the cell body compete for a finite actin pool to push
the cell in their direction until one pseudopod wins and determines the
direction of movement. Our minimal model provides a quantitative understanding
of the strategies cells use to reach the physical limit of accurate chemotaxis,
aligning with data without explicit gradient sensing or cellular memory for
persistence. To generalize our model, we employ reinforcement learning
optimization to study the effect of pseudopod suppression, a simple but
effective cellular algorithm by which cells can suppress possible directions of
movement. Different pseudopod-based chemotaxis strategies emerge naturally
depending on the environment and its dynamics. For instance, in static
gradients, cells can react faster at the cost of pseudopod accuracy, which is
particularly useful in noisy, shallow gradients where it paradoxically
increases chemotactic accuracy. In contrast, in dynamics gradients, cells form
\textit{de novo} pseudopods. Overall, our work demonstrates mechanical
intelligence for high chemotaxis performance with minimal cellular regulation.
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Paper Author: Albert Alonso,Julius B. Kirkegaard,Robert G. Endres
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