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From single neuron to brain: multi-level strategy and digital twins for personalized prediction of treatment

Cel

Epilepsy is a common neurological disorder characterized by recurrent seizures. Treatment is typically trial-and-error and may take years before seizure control is achieved. The pathophysiology of epilepsy is poorly understood and the clinical diversity is striking. This makes it difficult to predict how a patient will respond to medication, which poses major challenges for treatment prognosis.
The advent of human induced pluripotent stem cells allowed the phenotype, molecular abnormalities, and drug responses of epilepsy to be investigated in vitro in a patient-specific manner. However, little is known about how to translate these in vitro findings to humans, so there is a big gap between clinical practice and research in epilepsy.
The main goal of this project is to close that gap – to enhance the prediction of personalized treatment in epilepsy by translating findings from in vitro models to patients. To achieve this, I will develop a multi-level strategy “from neuron, to network, to brain”, in which clinical features, neuronal network phenotypes, and single-cell abnormalities can be studied for individual patients. I will characterize the phenotype and response to medication at these different levels of complexity and will use digital twin approaches to bridge the information between the levels by unraveling cellular mechanisms underlying network and brain dynamics. For each individual patient, I will identify biomarkers for treatment prediction by defining the cellular mechanisms that correlate at the different levels. This approach will show how relevant in vitro findings are to patients. It ultimately will allow individual phenotypes to be investigated and treatment efficacy to be estimated in vitro, with a reliable translation of findings to the patient. If successful, this bioengineering tool will help to reduce the burden of drug screening for patients and will significantly improve treatment.

Dziedzina nauki (EuroSciVoc)

Projekt nie został jeszcze sklasyfikowany według klasyfikacji EuroSciVoc.
Wskaż dziedziny nauki, które twoim zdaniem są najbardziej istotne z punktu widzenia tego projektu i pomóż nam usprawnić naszą usługę klasyfikacji.

Słowa kluczowe

Program(-y)

Temat(-y)

System finansowania

HORIZON-ERC - HORIZON ERC Grants

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Zaproszenie do składania wniosków

(odnośnik otworzy się w nowym oknie) ERC-2026-STG

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Instytucja przyjmująca

UNIVERSITA DEGLI STUDI DI GENOVA
Wkład UE netto
€ 1 250 000,00
Koszt całkowity
€ 1 250 000,00

Beneficjenci (2)