Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : SupraModel (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1474182.0 €

Supramolecular self-assembly is a fundamental process abundantly utilized by nature and emerging functional materials technologies ranging from drug delivery to soft semiconductor devices. Recently, an increased focus has been placed on the multicomponent peptide co-assembly as they often display unique emergent properties that can dramatically expand the functional utility of peptide-based materials. Still, the full potential is hindered by the combinatorial complexity of peptide-based materials and our inability to predict the co-assembled structures and, therefore, properties and functionality. Machine Learning models built on top of Molecular Dynamics simulations are ideally suited to decipher the co-assembly behavior. However, the existing molecular models either suffer from severe approximations disabling them to give accurate predictions or are computationally too expensive to transverse the material space. Addressing this trade-off, I aim to develop a computational framework for fast and accurate peptide co-assembly prediction using as a key strategy a multiscale construction of Graph Neural Network-based models that can predict the peptide co-assembly. This innovative approach will enable me to reach the following objectives: (1) obtain unprecedented molecular insight into the peptide co-assembly process inaccessible to experiments, (2) uncover novel candidate materials, and (3) provide rational design rules for multicomponent peptide-based supramolecular materials. In a broader context, increased insight into cooperative behavior will bring us closer to understanding and ultimately synthetically replicating the exceptional functionality of living systems, while the methodological advancements of data-driven molecular modeling will be of paramount importance in other areas of biomaterial engineering and beyond.

Mots-clés : biomaterials,machine learning,multiscale modeling and simulations,molecular dynamics
Projet ERC : ORIGIN (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Life Science
Panel : Integrative B iology : from Genes and Genomes to S ystems
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498939.0 €

Did you know that ~80% of all proteomic data is not utilized? Proteins play a vital role in all biological processes and organisms. We believe that different versions of a single gene product – protein isoforms – shape the molecular diversity of life. However, comprehensive evidence on protein-level is not available. Chromatography-coupled tandem mass spectrometry (LC-MS/MS) is the de-facto standard for measuring proteomes, but it is not good at identifying isoforms because at least 80% of the recorded information is never used. I argue that isoforms leave a deterministic multi-dimensional fingerprint (ORIGINs) representing their physicochemical properties in each proteomic measurement. Therefore, the central aim of this project is to discover and quantify protein isoforms systematically by a novel MS-based proteomics data analysis strategy. By tapping into the wealth of data the proteomics community has already amassed, I will train deep neural networks that allow the prediction of ORIGINs. Second, I will implement an innovative data analysis strategy that utilizes ORIGINs to identify and quantify isoforms. Third, I will demonstrate that ORIGINs can be used to substantially broaden our understanding of the molecular diversity of life by showcasing its application on four emerging and challenging questions in proteome research of varying biological and technical complexity. This will allow me to address a fundamental open question in biology: to which extent and prevalence isoforms are actually translated and what functional roles they might be associated with. ORIGINs will improve the sensitivity, biological resolution and accuracy at which proteins and their isoforms can be identified and quantified. Beyond this, the concept of ORIGINs can be applied to and improve any proteomics experiments, and thus holds the potential to revolutionize MS-based proteomics as a technology and elevate the whole field of protein-based research.
Projet ERC : SpatialSem (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Understanding the 3D spatial semantics of the world around us is core to visual perception and digitization -- real-world environments are spatially three-dimensional, and must be understood in its 3D context, even from 2D image observations. This will lead to spatially-grounded reasoning and higher-level perception of the world around us. Such 3D perception will provide the foundation for transformative, next-generation technology across machine perception, immersive communications, mixed reality, architectural or industrial modeling, and more. This will enable a new paradigm in semantic understanding that derives primarily from a spatially-consistent, 3D representation rather than relying on image-based reasoning that captures only projections of the world. However, 3D semantic reasoning from visual data such as RGB or RGB-D observations remains in its infancy, due to challenges in learning from limited amounts of real-world 3D data, and moreover, the complex, high-dimensional nature of the problem. In this proposal, we will develop new algorithmic approaches to effectively learn robust visual 3D perception, with new learning paradigms for features, representations, and operators, to encompass 3D semantic understanding.
Projet ERC : PRONANO (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1449239.82 €

PRONANO will develop both theoretical and experimental methods to design autonomous nanoscale units that are able to carry out logic operations in order to self-assemble into distinct structures determined by an external stimuli. The nanounits will be programmed to assemble via a controlled self-assembly kinetic pathway, ultimately enabling programmable nanomatter. We will develop new algorithmic framework that will find the optimal set of interactions and logic gate controls required for the coordinated function of nanoparticles. We will use multiscale coarse-grained modeling to design and simulate interactions of nanostructures with the capabilities to carry out computation and communication with other nanoparticles in order to act as a programmable swarm. We will realize these nanostructures experimentally using DNA nanotechnology, creating a system that can dynamically react to an externally introduced stimulus that induces them to self-assemble into target finite-sized structures. This work will create new methods for nanotechnology that combine optimization theory, molecular simulations and experiments to study the kinetics and thermodynamics of hierarchical multicomponent assembly. As part of this effort, we will develop universal design rules to obtain a set of DNA nanostructures that can carry out computation and communication in order to achieve specific nanostructures as instructed by a biomolecule (DNA, RNA, or protein) that will act as external stimulus. Thus we will create a system which is both computationally tractable and can be realized and iterated experimentally, opening new venues for nanorobotics and self-organized systems. It will enable nanoscale construction of complex three-dimensional structures as a response to external conditions, with applications in molecular manufacturing, therapeutics, diagnostics and smart material construction.

Mots-clés : dna nanotechnology,self assembly,coarse grained modeling
Projet ERC : PROTEGO (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Humanitas University — Pays : Italie — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 300000.0 €

Aortic valve diseases are degenerative conditions that develop progressively and insidiously. Once symptoms become evident, life expectancy is significantly reduced. While treatments for these pathologies are widely available, there remains a remarkably high rate of procedural complications. These complications have been shown to have a negative impact on cardiac mortality and the likelihood of rehospitalization for heart failure. This underscores the need for further technological advancements. Protego's objective is to determine whether a combination of immunological and biomechanical profiles in patients with aortic valve diseases can effectively predict post-treatment prognosis. My goal is to develop an innovative, validated, and clinically applicable methodology that can identify the best treatment options and predict post-procedural outcomes while minimizing complications. This methodology will serve to determine the timing of treatment for patients with valvular aortic diseases and assess whether the proposed treatment is likely to be beneficial preoperatively, while also minimizing the risk of post-procedural complications. I will achieve this by combining imaging analysis, deep learning algorithms, in silico models, and in vitro tests. My approach involves the following key objectives: (i) creating a multi-physics digital twin of patients with aortic valve diseases, (ii) developing a validated, high-fidelity model for treatment with quantification of post-treatment outcomes and (iii) generating a proof of concept for a clinically applicable predictive model trained using both immunological profiles and biomechanical features of patients. This innovative approach will provide a deeper understanding of how clinical and biomechanical outcomes correlate with the amplification of inflammation, helping us comprehend the interaction between biomarkers and negative post-treatment prognosis in patients with aortic valve diseases.

Mots-clés : personalised medicine,digital human twin in healthcare,treatment,validation,verification,treatment selection support,aortic valve diseases,transcatheter aortic valve implantation
Projet ERC : MiST-MS (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Humanitas University — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Shifting from the traditional focus on central nervous system-targeting lymphocytes, MiST-MS posits that glial responses, particularly cellular senescence, are required to compartmentalized self-sustaining chronic inflammation in multiple sclerosis (MS) and related long-term clinical outcomes, including disability. MiST-MS is specifically designed to address the urgent need to identify new therapeutic targets to halt MS clinical progression through two interconnected research lines. The project will first identify cell-specific perturbations and molecular pathways associated with inflammation-driven early brain aging using MS human iPSC-derived microglia and genome-wide CRISPR-Cas9 screenings. By employing cutting-edge computational methodologies (single-cell CRISPR-seq, multi-omics integration, composite assessment, and cellular networking/signaling), MiST-MS offers a unique opportunity to identify and dissect mechanisms underlying microglia resilience versus vulnerability and their impact on stress responses, both independently and within the context of hiPSC-derived inflamed glia-enriched brain organoids. MiST-MS represents a ground-breaking modeling pipeline to study inflammation-driven early brain aging in MS. At the patient-level, since MS patients with evidence of smoldering inflammation (marked by the presence of chronic active lesions by in vivo MRI) exhibit poor tissue repair capacity and worse clinical outcomes, the project will also aim to identify common and divergent glial and neuronal responses between MS patients with and without chronic active lesions generating human iPSC lines for personalized medicine applications. In summary, MiST-MS is a highly innovative interdisciplinary project that provides an unprecedented understanding of microglia states in response to injury and streamlines the rational design of future therapies to mitigate chronic inflammation-driven premature brain aging and halt MS clinical progression.
Projet ERC : HEPMASITE (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université d'Hambourg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1495013.0 €

In the Middle Ages, philosophical activity undertaken in Hebrew was not conducted within an institutionalized environment. There were no universities, regulated curricula, or professors, but only small circles of scholars, most of whom we know nothing about. These scholars had to obtain their own copies of the works they wished to explore, sometimes even copying them themselves or with the help of others. Heavily involved in the production of the materials they were using, they often interfered with the texts they were studying, offering corrections, working notes, glosses, comparisons, and observations. As a result, the corpus of philosophical writings in Hebrew is incredibly diverse, and rarely is one copy similar to another. In this challenging landscape, the individual copy is our main entry point for understanding how Hebrew philosophy took place in the real world. The Hebrew philosophical manuscript is not a mere container of text; more than anything else, it is a site of engagement. Aside from a few notable exceptions, scholarship of philosophy in Hebrew is still focused on a relatively small number of major thinkers and works. Manuscripts are often stripped of their particularity and employed only for the sake of producing critical editions. The story of the silent, nameless majority - who enabled philosophical activity by tirelessly editing, studying, translating, revising, and producing the material Hebrew philosophical corpus we have today - has yet to be told. HEPMASITE is the first project to tackle the corpus of medieval Hebrew philosophical manuscripts in order to unravel the hidden history of Jewish philosophy enveloped within them. By employing narrative philology and a historiography of engagement - novel research methodologies that embrace textual particularity and fluidity - HEPMASITE will revolutionize the understanding of Jewish philosophy as it took place in the real world and as it was studied by actual people.

Mots-clés : jewish philosophy,hebrew manuscripts
Projet ERC : CompactBINARIES (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Université d'Hambourg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1685375.0 €

The accelerated expansion of the universe was one of the most striking discoveries in the 20th century and was only possible by using type Ia supernovae (SN Iae). Only the thermonuclear explosion of a white dwarf (WD) following the interaction in a binary system can explain the observed features. Several different binary scenarios are discussed including the merger and subsequent explosion of an ultracompact double white dwarf (double degenerate) or an explosion triggered by helium accretion from a helium star donor (double detonation). The Laser Interferometer Space Antenna (LISA) is a space-based gravitational wave (GW) detector, which is currently being built by ESA. LISA will be sensitive to measure GWs directly of thousands of Galactic binaries, allowing for multi-messenger studies. The PI proposes to conduct a detailed observational study to identify and classify at least a few hundred SN Ia progenitors and LISA GW sources to 1) derive compact binary space densities and predict rates for different SN Ia formation channels to solve the supernova Ia progenitor problem 2) provide the first observationally derived population of resolvable LISA sources and Galactic LISA foreground sources To achieve this ambitious project the PI will use ongoing and upcoming sky surveys such as ZTF, BlackGEM, Gaia, Vera Rubin Observatory and SDSS-V combined with large-scale multiwavelength surveys. The large number of sky surveys combined with recent developments in data processing, including dense Galactic Plane regions makes this project very timely. The results will calibrate theoretical models for SN Ia, common envelope evolution and will help to find the optimal data analysis methods for LISA so that its mission requirements can be achieved. The PI will successfully lead this project because of his i) profound experience in large-scale sky-surveys, ii) proprietary data access to BlackGEM data and iii) direct involvement in multi-messenger projects for LISA.

Mots-clés : compact binaries,white dwarfs,hot subdwarf stars,interacting binary systems,supernova ia progenitors,gravitational wave sources,late stellar evolution
Projet ERC : COLDSPOT (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université d'Hambourg — Pays : Allemagne — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 631100.0 €

The release of greenhouse gases from the terrestrial biosphere is of global importance. The Arctic is a net source of the greenhouse gases methane (CH4) and nitrous oxide (N2O), and a biogeochemically important ecosystem due to immense soil carbon and nitrogen pools and above-average warming. An observation-bias in Arctic greenhouse gas reporting towards high-emitting ‘hotspot’ sites is evident. In contrast, uptake of CH4 and N2O by Arctic soils, leading to greenhouse gas removal from the atmosphere, could partially compensate for carbon and nitrogen losses and completely redefine our baseline understanding of Arctic and global greenhouse gas budgets. No consideration has been given to understanding natural CH4 and N2O sinks in the Arctic, and the complex biogeochemical mechanisms governing these ‘coldspots’. I aim to show that Arctic greenhouse gas uptake matters, and that, contrary to traditional beliefs, intricate temporal uptake patterns exist and are driven by plant and microbial functioning. COLDSPOT will fulfil the scientific need to know when, where, and why Arctic soils act as a sink for CH4 and N2O, and will identify novel mechanisms underlying Arctic greenhouse gas uptake. High-resolution, laser-based measurements of soil CH4 and N2O uptake at field sites in Canada, Greenland, and Finland will be combined with machine learning tools and in-depth studies of environmental drivers in sophisticated experimental manipulations. COLDSPOT applies a multi-scale approach combining field-based measurements of multiple gas species with climate simulation experiments in state-of-the-art climate chambers in the laboratory, and investigations ranging from the microbial scale, via soil and plant processes to ecosystem-scale models. COLDSPOT will generate new process-understanding, fundamentally transform our perception of the magnitude and functioning of greenhouse gas sinks in the Arctic, and provide breakthrough insights for Arctic and global greenhouse gas research.

Mots-clés : arctic tundra,terrestrial biogeochemistry,soil incubations,permafrost soils,greenhouse gas fluxes,chamber measurements,methane,nitrogen cycle
Projet ERC : IMPACT (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Humanitas University — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1490000.0 €

Spontaneous activity is a prominent feature of the immature brain. Even before birth and in absence of stimuli, neurons organize in networks and spontaneously generate correlated activity. While spontaneous dynamics in the cerebral cortex have long been overlooked and considered just as epiphenomena, recent clinical data on preterm infants and preclinical studies have spurred a renewed interest for this early electrical activity. However, appreciation of the role of spontaneous activity during the perinatal stages remains elusive. Indeed, it is still unknown how spontaneous patterns arise, and whether, among the large variety of neuronal classes generated in the cerebral cortex, distinct subtypes can act as pacemaker (Pm) neurons, able to trigger some of these events. Defects arising from alterations in the early cortical spontaneous activity have never been thus far systematically addressed, yet they can affect local assembly and physiological behavioural states.  The IMPACT project aims to shed light on how cortical neuronal diversity influences early spontaneous activity, and to identify the molecular features and functional role of developing Pm neurons. By integrating innovative molecular strategies with in vivo optical recordings and behavioural assays, I will 1) characterise and spatially resolve the subtype-specific molecular footprints correlated with electrical profiles of neuron subtypes, with a special focus on Pm neurons; 2) assess molecular, cellular and circuit consequences of perturbations in Pm neuron activity; 3) identify novel functional modulators of Pm activity in the surrounding cerebrospinal fluid (CSF) around birth. IMPACT bears the ambition of filling the knowledge gap between the molecular and functional traits of developing cortical neurons, while unveiling the existence and critical role of the Pm neurons. Discovering new molecular players and modulators of early activity will inspire novel intervention strategies for perinatal disorders.

Mots-clés : cerebral cortex,neuronal diversity,spontaneous activity,circuit assembly,molecular identity