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HEStaff exchange2023–2027

SIMBAD · Statistical Inference from Multiscale Biological Data: theory, algorithms, applications

Horizon Europe — Marie Skłodowska-Curie Actions

Duration
2023-12-01 → 2027-11-30
EU contribution
€740,600
Participants
8
Scheme
HORIZON-TMA-MSCA-SE

Lines connect the coordinator with its partners.

Results in brief

Statistical Inference from Multiscale Biological Data: theory, algorithms, applications

High-throughput experimental techniques have transformed the life sciences, enabling quantitative investigation of biological systems across scales, from molecules and cells to patients and populations. These technologies generate massive, high-dimensional datasets, including protein sequence families, spatial maps of cellular microenvironments, heterogeneous clinical records, and large-scale epidemiological indicators. Beyond mechanistic insights, such data allow inference of previously unknown quantitative laws and organizational principles. This motivates inverse modelling: instead of building mechanistic models, statistical inference reconstructs the underlying probability distributions generating the data. These generative models support prediction, classification, and design—for example, predicting mutational effects, protein evolution, intercellular networks, disease progression, and epidemic dynamics. By focusing on essential features, inverse models are more generalisable than detailed, context-dependent models. Reliable inference faces challenges due to high dimensionality, multiple scales, and sparse sampling: (i) strong, evolution-shaped heterogeneity; (ii) model selection under computational constraints; (iii) overfitting in undersampled regimes; and (iv) limited data annotation, requiring semi-supervised methods. SIMBAD addresses these issues by developing statistical-physics-based frameworks for generative modeling of heterogeneous, high-dimensional data. It devises inference algorithms robust to undersampling and overfitting and applies them to four domains: protein evolution and design, cellular metabolic state inference, digital contact tracing and epidemiological networks, and survival analysis in medicine. Despite diverse applications, all share the same theoretical bottlenecks, enabling broadly applicable, high-impact methods.

Data: CORDIS, © European Union

Project objective

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

Original text from CORDIS.

Participants

  • POLITECNICO DI TORINO · TorinoCoordinatorItaly
  • Center for Molecular Immunology · HavanaCuba
  • IIGM FOUNDATION · TorinoItaly
  • SORBONNE UNIVERSITE · ParisFrance
  • STICHTING RADBOUD UNIVERSITEIT · NijmegenNetherlands
  • THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD · OxfordUnited Kingdom
  • UNIVERSIDAD DE LA HABANA · PLAZACuba
  • Universidad Nacional de General Sarmiento · Los Polvorines - Provincia De Buenos AiresArgentina

Links

Data: CORDIS, © European Union