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FlowMem

Financiado

Flow network morphology as memory map Principles of fluid flow driven dynamic m...

Flow network morphology as memory map Principles of fluid flow driven dynamic memory in living tubular networks 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 dynami... ver más

30/09/2026

TUM

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

TECHNISCHE UNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-11-04

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2020-STG: ERC STARTING GRANTS

I+D

Cerrada hace 5 años

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2 Participantes

TUM

1.50M€ | Lider

AIQEI SOFTWARE, S.L.

401.18K€ | Participante

Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 70 meses Fecha Inicio: 2020-11-04
Fecha Fin: 2026-09-30

Líder del proyecto

TECHNISCHE UNIVERSITAET MUENCHEN

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto 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. I aim to identify the physical principles behind fluid flows driving dynamic memory storage in network morphology. I 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 I 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 me 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. I will develop general principles advancing physics and biology with far-reaching implications in medicine and engineering.

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