Within the newly acquired ERC Starting Grant “FlowMem” we aim to identify the physical principles of how flow networks can retain memories.
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. We aim to identify the physical principles behind fluid flows driving dynamic memory storage in network morphology. We 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 we 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 will verify mechanisms and allow investigation of impact on network function. Identified principles of dynamic memory formation will be applied to study consequences of mini-stroke stimuli and possible treatment in brain microvasculature and to design self-optimizing porous media. We will develop general principles advancing physics and biology with far-reaching implications in medicine and engineering.
To join us on the project apply for a PhD or Post-doc position.