Along the aim of FlowMem to identify the principles of fluid flow driven dynamic memory in living tubular networks we focused in the first funding period onto the first three out of four objectives to reach our overarching aim. Specifically, for objective 1, we uncovered that changes in the tubular network architecture by dilation and shrinkage of individual tubes in response to a stimulus imprint a lasting memory (Bhattacharyya et al Phys. Rev. Lett. 2022). We showed that the irreversible pruning of tubes breaks ergodicity of continuously adapting network and thereby imprints their history in the orientation, width and location of persisting tubes. As changes in tube diameter arise in response to softening agents transported within the network-spanning flows, we developed an analytical theory of transport in flow networks (Meigel et al Nat. Comm. 2022). Our analytical theory, validated by simulations and microfluidic experiments, uncovered that tube diameter statistics across junctions are determining transport and not tube diameter distributions alone, as previously commonly accepted.
Also, in the absence of stimuli living tubular networks adapt. We unraveled by experiments that it is flow shear force which is driving tubes to dilate or shrink (Marbach et al eLife 2023), the key goal of objective 2. In fact, we derived that flow shear force drives living tubular network to adapt from first principles starting with force balance at the tube wall, thereby providing physical foundation and mechanistic insight to almost century-old phenomenological Murray’s law (Marbach et al New. J. Phys. 2023). Unforeseen, the dynamical equations describing tube adaption reveal the coupling across the flow networks resolving why tube diameter alone is not predictive of tube dynamics but that the immediate network neighborhood determines the ultimate pruning or persistence of tubes. Studying tube pruning in response to stimuli revealed also the impact of network architecture on network adaptation (Chen et al. Phys. Biol 2023).
The theoretical insight on how living flow networks store memories also paved the way to investigate the memory capacity of networks, the intention of objective 3. We found that overall network age is limiting memory capacity because of the irreversible pruning of tubes (Bhattacharyya et al Phys. Rev. E. 2023). We summarized the implications of our findings on memory formation in the living tubular networks formed by Physarum polycephalum in a broad review focusing on the physical underpinnings of the model systems and its exciting avenues ahead (LeVerge-Serandour, Annu. Rev. Condens. Matter Phys. 2024).
The impact of dynamic memory on flow network performance, objective 4, is the current focus of our ongoing work. Here, we already uncovered that network size provides a fundamental advantage in organism foraging, as size is key to make external memory of Physarum polycephalum efficient in driving its self-avoidance during foraging (Tröger, Proc. Natl. Acad. Sci. USA 2024). We are currently preparing results on the impact of network architecture on foraging in Physarum polycephalum, the perfusion in brain microvasculature and human vascular adaptation in response to flow shear force for publication.
