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SIMBAD

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Statistical Inference from Multiscale Biological Data: theory, algorithms, appli...

Statistical Inference from Multiscale Biological Data: theory, algorithms, applications The last two decades have witnessed giant experimental breakthroughs in different areas of the life sciences, from genomics to epidemiology. Thanks to modern high-throughput techniques, biological systems across multiple scales –f... ver más

30/11/2027

POLITO

741K€

Presupuesto del proyecto: 741K€

Líder del proyecto

POLITECNICO DI TORINO No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-10-20

HORIZON-MSCA-2022-SE-01-01: MSCA Staff Exchanges 2022 ExpectedOutcome:

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8 Participantes

POLITO

119.60K€ | Lider

UNIVERSIDAD DE LA HABANA

STICHTING RADBOUD UNIVERSITE...

248.40K€ | Participante

CAMERA DI COMMERCIO INDUSTRI...

115.00K€ | Participante

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Duración del proyecto: 49 meses Fecha Inicio: 2023-10-20
Fecha Fin: 2027-11-30

Líder del proyecto

POLITECNICO DI TORINO No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 741K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The last two decades have witnessed giant experimental breakthroughs in different areas of the life sciences, from genomics to epidemiology. Thanks to modern high-throughput techniques, biological systems across multiple scales –from single molecules up to entire populations– can now be probed quantitatively at high spatial and temporal resolutions. Besides enhancing our basic knowledge of a system’s constituents, these data potentially encode a plethora of information about the functional constraints that govern its evolution and the physical constraints that limit its performance, as well as about levels of organization, dynamical constraints or design principles that would be hard to identify from low-throughput data. Extracting this information is also crucial for applications ranging from the design of proteins with a desired functionality to the reconstruction of contacts during an epidemics. Inverse statistical mechanics attempts to do it by inferring generative models (Boltzmann distributions) from data using methods from the physics of disordered and random systems. Specific characteristics of biological data however, like strong undersampling and heterogeneity, limit the effectiveness of these tools. SIMBAD aims at developing a class of statistical inference techniques capable of overcoming these issues. In SIMBAD, theoretical work will supply concepts and methods to address four pressing problems (learning protein sequence landscapes, inverse modeling metabolic networks, inferring contact networks from epidemiological data, and improving survival analysis models), which in turn will guide the theory towards integration with the existing standards of each field. This effort promises to open new pathways for basic research to impact economic, technological and societal issues; the high- profile cross-disciplinary expertise represented in SIMBAD ensures instead for measurable and achievable objectives, placing SIMBAD in an ideal position to achieve its goals

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