Biological Physics and Morphogenesis Group, Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany.
Biological Physics and Morphogenesis Group, Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany;
Proc Natl Acad Sci U S A. 2021 Mar 9;118(10). doi: 10.1073/pnas.2007815118.
The concept of memory is traditionally associated with organisms possessing a nervous system. However, even very simple organisms store information about past experiences to thrive in a complex environment-successfully exploiting nutrient sources, avoiding danger, and warding off predators. How can simple organisms encode information about their environment? We here follow how the giant unicellular slime mold responds to a nutrient source. We find that the network-like body plan of the organism itself serves to encode the location of a nutrient source. The organism entirely consists of interlaced tubes of varying diameters. Now, we observe that these tubes grow and shrink in diameter in response to a nutrient source, thereby imprinting the nutrient's location in the tube diameter hierarchy. Combining theoretical model and experimental data, we reveal how memory is encoded: a nutrient source locally releases a softening agent that gets transported by the cytoplasmic flows within the tubular network. Tubes receiving a lot of softening agent grow in diameter at the expense of other tubes shrinking. Thereby, the tubes' capacities for flow-based transport get permanently upgraded toward the nutrient location, redirecting future decisions and migration. This demonstrates that nutrient location is stored in and retrieved from the networks' tube diameter hierarchy. Our findings explain how network-forming organisms like slime molds and fungi thrive in complex environments. We here identify a flow networks' version of associative memory-very likely of relevance for the plethora of living flow networks as well as for bioinspired design.
记忆的概念传统上与具有神经系统的生物有关。然而，即使是非常简单的生物也会储存有关过去经验的信息，以在复杂的环境中茁壮成长——成功地利用营养源、避免危险和抵御捕食者。简单的生物如何对环境进行编码信息？我们在这里跟随巨大的单细胞黏菌如何对营养源做出反应。我们发现，生物本身的网络状身体计划用于编码营养源的位置。该生物完全由不同直径的交织管组成。现在，我们观察到这些管的直径会响应营养源而生长和收缩，从而在管直径层次结构中记录下营养源的位置。通过理论模型和实验数据的结合，我们揭示了记忆是如何编码的：营养源局部释放一种软化剂，该软化剂通过管状网络中的细胞质流进行运输。接收大量软化剂的管会在直径上生长，而其他管会收缩。因此，管的基于流的传输能力会朝着营养源的方向永久升级，从而重新引导未来的决策和迁移。这表明营养源的位置存储在网络的管直径层次结构中，并从该结构中检索。我们的研究结果解释了网络形成的生物（如黏菌和真菌）如何在复杂环境中茁壮成长。我们在这里确定了一种关联记忆的流网络版本——它很可能与大量的活体流网络以及仿生设计有关。
