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Computational network-based models of tau progression in Alzheimer’s disease

Project description

Computational model of tau progression for Alzheimer’s

Alzheimer’s disease (AD) has become a primary global concern due to an ageing population. It involves the misfolding of beta-amyloid (Aβ) and tau proteins in the brain, which can be identified through PET scans. Recent focus has shifted from Aβ to tau due to its stronger link to cognitive decline. However, studying tau is complicated by its variability among individuals. With this in mind, the ERC-funded TauTime project aims to enhance our understanding of AD and develop tools for clinical trials and patient management. It will create TAU-TIME, a computational model that simulates tau progression using mechanistic knowledge and individualised data. This model will support biological hypothesis testing and serve as a predictive tool for personalised patient monitoring.

Objective

The aging of the human population has catapulted Alzheimer's disease (AD) from a growing global challenge to an urgent socioeconomic concern. The hallmark of AD is the misfolding and aggregation of proteins β-amyloid (Aβ) and tau in the brain, which can be imaged in living humans using positron emission tomography (PET). Recent clinical trials targeting Aβ have shown promising but modest clinical effects, leading to a shift in focus towards tau due to its closer association with neurodegeneration and cognitive decline. However, tau accumulation is spatially and temporally complex and varies across individuals, complicating its study and therapeutic monitoring. This proposal addresses the need for agile research to enhance our biological understanding of AD while building tools that can aid clinical trials and patient management. I aim to construct TAU-TIME, a computational biology model of tau progression that integrates mechanistic knowledge and individualized data. TAU-TIME will simulate tau accumulation at the individual level and will facilitate testing of biological hypotheses in the context of human AD, where experimental manipulation is challenging. It will also serve as a predictive tool for patient-tailored monitoring, symptom prediction, and enhancing clinical trial power. The proposal encompasses three objectives: 1) optimizing TAU-TIME through integration of spatially-resolved mesoscale human molecular data, 2) leverage individual biological variation at baseline toward patient-tailored models, and 3) benchmark TAU-TIME models on longitudinal tau-PET and cognitive data in a clinical context. These objectives will be achieved through exclusive access to existing data from 2000 patients with extensive neuroimaging and cognitive data, cerebrospinal proteomics, and longitudinal tau-PET measurements over four years. Through these objectives, I aim to enhance understanding of tau pathophysiology, investigate the factors contributing to tau accumulation, and de

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

(opens in new window) ERC-2025-STG

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Host institution

LUNDS UNIVERSITET
Net EU contribution
€ 1 437 500,00
Address
Paradisgatan 5c
22100 Lund
Sweden
Region
Södra Sverige Sydsverige Skåne län
Activity type
Higher or Secondary Education Establishments
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Total cost
€ 1 437 500,00

Beneficiaries (1)