Periodic Reporting for period 2 - FlowMem (Flow network morphology as memory map: Principles of fluid flow driven dynamic memory in living tubular networks)
Reporting period: 2023-04-01 to 2024-09-30
Fluid flows through tubular networks are crucial for life as they are the dominant means of substance and signal transport. In living networks – across organisms as disparate as animals and fungi, alterations of flows drive dynamic adaptation of tube diameters which in turn alters transport performance. In effect, local transient stimuli that affect flows are memorized as long-lived alterations to tube diameters across the network. FlowMem aims to identify the physical principles behind fluid flows driving dynamic memory storage in network morphology. FlowMem will thereby uncover how to control network morphology and performance by applied flow-altering stimuli, which promises significant advances in important challenges of the future: treatment of vascular diseases and tumour development, encoding complex behaviour in soft robotics and self-optimizing porous media.
The dynamic nature of flows and networks’ complex morphologies requires a combined experimental and theoretical approach to address: What are the physical mechanisms of how flows in living tubular networks can encode and store information about stimuli? How do memories impact network performance? As experimental model system FlowMem choose the slime mould Physarum polycephalum. It is ideally suited as a starting point, as it reduces the problem in its complexity to just a tubular network. This model allows us to follow with unprecedented level of detail how stimuli transiently perturb network-wide flows – flows that subsequently drive long-term changes in network morphology. Theoretical models here verify mechanisms and allow investigation of impact on network function. Identified principles of dynamic memory formation are applied to study consequences of mini-stroke stimuli and possible treatment in brain microvasculature and to design self-optimizing porous media. The general principles FlowMem discovers are advancing physics and biology with far-reaching implications in medicine and engineering.
The dynamic nature of flows and networks’ complex morphologies requires a combined experimental and theoretical approach to address: What are the physical mechanisms of how flows in living tubular networks can encode and store information about stimuli? How do memories impact network performance? As experimental model system FlowMem choose the slime mould Physarum polycephalum. It is ideally suited as a starting point, as it reduces the problem in its complexity to just a tubular network. This model allows us to follow with unprecedented level of detail how stimuli transiently perturb network-wide flows – flows that subsequently drive long-term changes in network morphology. Theoretical models here verify mechanisms and allow investigation of impact on network function. Identified principles of dynamic memory formation are applied to study consequences of mini-stroke stimuli and possible treatment in brain microvasculature and to design self-optimizing porous media. The general principles FlowMem discovers are advancing physics and biology with far-reaching implications in medicine and engineering.
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.
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.
At the current state of FlowMem five out of the eight publications accepted in peer-reviewed journal mark significant advancements.
1) Bhattacharyya et al Phys. Rev. Lett. 2022
Living flow networks such as our vasculature but also plant and slime mold networks continuously adapt to optimize their architecture. We, here, show, that adaptation to intermittent changes in flow magnitude imprints a lasting memory on the network architecture retrievable long after the flow change. In fact, eroding veins are key to this unexpected memory formation as we formalize in an analytic theory.
2) Meigel et al Nat. Commun. 2022
Dispersive transport through complex media is influenced by their microscopic, porous structure. Considering the statistics of pore-junction units, in contrast to individual pores, we, here, combine experiment, simulation and theory to now successfully predict macroscopic transport characteristics from morphology.
3) Marbach et al eLife 2023
Investigating rearrangement of vascular networks using Physarum polycephalum as model system we provide compelling theoretical and experimental evidence to demonstrate how the fluid flow locally deforms the veins and ultimately dictates a global remodelling of network architecture. Our work lays the foundation to understand vascular remodelling in disease and how to program networks by hijacking the built-in self-organization of living networks
4) LeVerge-Serandour, Annu. Rev. Condens. Matter Phys. 2024
Reviewing state-of-the-art on mechanics, migration, network re-organization and behaviour of the model organism Physarum polycephalum we provide a rounded physics perspective to uncover the physical mechanisms driving this enigmatic organisms’ complex repertoire of behaviours despite its make-up of a single, multi-nucleated cell.
5) Tröger, Proc. Natl. Acad. Sci. USA 2024
Linking self-avoidance of the unicellular Physarum polycephalum during migration to network size we uncover that more efficient space exploration mechanisms typical for higher life forms or collective cell behaviour is here established merely by cell size making path-marking more robust. Our work underlines the evolutionary advantage of macroscopic cell size as a potential driver at the evolution of multicellularity.
Until the end of the project we expect to show how memory determines functionality of vasculature, slime mould behaviour and flows in porous media.
1) Bhattacharyya et al Phys. Rev. Lett. 2022
Living flow networks such as our vasculature but also plant and slime mold networks continuously adapt to optimize their architecture. We, here, show, that adaptation to intermittent changes in flow magnitude imprints a lasting memory on the network architecture retrievable long after the flow change. In fact, eroding veins are key to this unexpected memory formation as we formalize in an analytic theory.
2) Meigel et al Nat. Commun. 2022
Dispersive transport through complex media is influenced by their microscopic, porous structure. Considering the statistics of pore-junction units, in contrast to individual pores, we, here, combine experiment, simulation and theory to now successfully predict macroscopic transport characteristics from morphology.
3) Marbach et al eLife 2023
Investigating rearrangement of vascular networks using Physarum polycephalum as model system we provide compelling theoretical and experimental evidence to demonstrate how the fluid flow locally deforms the veins and ultimately dictates a global remodelling of network architecture. Our work lays the foundation to understand vascular remodelling in disease and how to program networks by hijacking the built-in self-organization of living networks
4) LeVerge-Serandour, Annu. Rev. Condens. Matter Phys. 2024
Reviewing state-of-the-art on mechanics, migration, network re-organization and behaviour of the model organism Physarum polycephalum we provide a rounded physics perspective to uncover the physical mechanisms driving this enigmatic organisms’ complex repertoire of behaviours despite its make-up of a single, multi-nucleated cell.
5) Tröger, Proc. Natl. Acad. Sci. USA 2024
Linking self-avoidance of the unicellular Physarum polycephalum during migration to network size we uncover that more efficient space exploration mechanisms typical for higher life forms or collective cell behaviour is here established merely by cell size making path-marking more robust. Our work underlines the evolutionary advantage of macroscopic cell size as a potential driver at the evolution of multicellularity.
Until the end of the project we expect to show how memory determines functionality of vasculature, slime mould behaviour and flows in porous media.