持续伪足分裂是浅梯度中一种有效的趋化策略
Persistent pseudopod splitting is an effective chemotaxis strategy in shallow gradients
AI总结：
本文通过肌动蛋白招募竞争模型和强化学习优化，揭示了变形虫细胞在不同梯度环境中采用伪足分裂、抑制及从头形成等策略实现高效趋化的机械智能机制。
摘要：
单细胞生物和多种细胞类型在追随化学梯度时会使用多种运动模式，但目前尚不清楚哪种模式最适合不同的梯度。在此，我们将趋化性变形虫细胞的定向决策建模为一种依赖刺激的肌动蛋白招募竞争。从细胞体伸出的伪足竞争有限的肌动蛋白池，以推动细胞朝其方向移动，直到一个伪足获胜并决定运动方向。我们的最小模型提供了对细胞用于达到精确趋化物理极限的策略的定量理解，并且与数据一致，无需显式的梯度感知或用于持久性的细胞记忆。为了推广我们的模型，我们采用强化学习优化来研究伪足抑制的效果，这是一种简单但有效的细胞算法，细胞可以通过该算法抑制可能的运动方向。不同的基于伪足的趋化策略会根据环境及其动态自然涌现。例如，在静态梯度中，细胞可以以牺牲伪足精度为代价更快地做出反应，这在嘈杂的浅梯度中尤其有用，因为在这种梯度中它反而会提高趋化精度。相比之下，在动态梯度中，细胞会从头形成伪足。总体而言，我们的工作展示了在最小细胞调控下实现高趋化性能的机械智能。
英文摘要：
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 de novo pseudopods. Overall, our work demonstrates mechanical intelligence for high chemotaxis performance with minimal cellular regulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
