Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : CLAR (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and Analytical Chemical Sciences
Entité participante : Université de Vienne — Pays : Autriche — Rôle : PI
Acronyme de l'entité : Uni Wien
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2955000.0 €

Molecular interactions are at the basis of all biological processes and often include specific interactions between macromolecules (protein, RNA, DNA) and small-molecule ligands, such as cofactors, hormones, drugs, or metabolites. Detailed and quantitative knowledge of these interactions is critical for a molecular understanding of these biological processes and developing new therapeutic solutions. A complete comprehension of molecular recognition requires a full characterization of the geometry and dynamics of the molecular complex. This includes not only the internal dynamics of each partner but also the dynamics at the interface, which to date remain mostly unexplored experimentally. The physical chemistry frame for studying intra- and inter-molecular interactions is thermodynamics. The extent to which two molecules interact is dictated by the Gibbs energy change (ΔG) of the interactions, which is composed of enthalpic (ΔH) and entropic (ΔS) terms. X-ray crystallographic and NMR structures provide a detailed description of the static interactions associated with enthalpic contributions. However, up to now, the entropic components remain difficult to address experimentally.  The overarching goal of this proposal is to develop a calorimeter at atomic resolution. To achieve that goal, a new NMR spectroscopy approach, relying mainly on the nuclear Overhauser effect, will be developed. It is anticipated that quantitative thermodynamic measurements within molecules and molecular complexes will open a new avenue in the fundamental understanding of how atomistic mechanism(s) create a function. Beyond the fundamental findings, we foresee applications in translational medicine, drug design, and computer-assisted molecular design.

Mots-clés : bionmr spectroscopy,thermodynamics of protein and protein ligand complexes,entropy
Projet ERC : INSTant CARMA (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : KU Leuven — Pays : Belgique — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1273500.0 €

Osteoarthritis (OA) is a highly prevalent disease, affecting 1 in 8 adults worldwide and is projected to increase by 60% in the next two decades. Due to the intricate interplay between multiple influencing factors such as mechanical loading and inflammation playing out over multiple length scales and tissue compartments, currently no disease-modifying drugs are available on the market. The INSTAnt CARMA project aims to harness the power of in silico trials to study OA onset and progression, as well as design endogenous (drugs) and exogenous (tissue engineered) therapies. A multiscale multifactorial in silico model of the knee joint will be established and validated with dedicated in vitro and in vivo experiments. A continuous effort following targeted and untargeted approaches will take place to explore biological hypotheses and implement relevant model extensions. In parallel, computational upscaling tools will be implemented to prepare the model for execution of in silico trial simulations on virtual patient cohorts. The first in silico trial will investigate the effect of model-predicted drug combinations after experimental confirmation in a rodent model. The second trial will seek to optimize tissue engineered constructs for deep osteochondral defect regeneration, followed by experimental validation in a small and large animal model. To realize its goals, INSTAnt CARMA can build on the well-received and published work on a wide range of in silico, in vitro and in vivo tools developed by the applicant and her team. Additionally, the work will benefit from the applicant’s extensive network and involvement in regulatory science for in silico medicine. This project will further accelerate the uptake of in silico testing strategies in the development of medical therapies in general and osteoarthritis in particular.

Mots-clés : in silico medicine,osteoarthritis,tissue engineering,advanced therapy medicinal product,computer modeling and simulation
Projet ERC : MORETHANMONEY (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : KU Leuven — Pays : Belgique — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Digital technologies, the Covid crisis, rising incomes, and changing work attitudes all contribute to the recent increase in the importance of certain nonmonetary aspects of work - most prominently flexibility and work meaning. What does this mean for gender differences on the labor market and for the future of work? MORETHANMONEY will integrate flexibility and work meaning into the theory and empirics of family labor supply and retirement choices. In three subprojects, I will develop models that combine behavioral, labor and family economics, collect unique data sets that combine administrative, survey and experimental parts, and use structural and reduced-form estimation techniques. A) I will use models of collective labor supply and newly collected data to analyze the choice of hours worked, flexibility, and time spent with children at the family-level. Choosing flexibility may reduce wages and decision power and thus affect the distribution of family resources. A hidden cost of recent policies aimed at increasing flexibility may be that wages and bargaining power of women decrease. B) I will combine the labor economics (flexibility) with the behavioral (meaning of work) perspective and collect data on trade-offs between meaning, flexibility, and wages consistently in several countries. Choosing occupations and sectors with high meaning (like health and education) may restrict the flexibility choice and force women to reduce their labor supply. C) I will take the long-term perspective. While wages translate into retirement incomes, nonwage attributes do not, but they may cause an increase in labor supply. I will model and collect data on trade-offs between wages, hours worked, and nonwage attributes and estimate their effect on retirement decisions over the life cycle. This will greatly increase our understanding of the large differences in retirement incomes between men and women.

Mots-clés : flexibility work meaning work mission family economics collective labor supply gender wage gaps retirement gaps data collection hypothetical choice experiment administrative data
Projet ERC : NEUROPEPTALK (Horizon Europe, appel 2025, Consolidator grants)
Entité participante : KU Leuven — Pays : Belgique — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2167296.0 €

Humanity has always been intrigued by the brain’s ceaseless capacity to learn and store experiences. However, the ability to acquire and retrieve learned information is dynamic and regulated by a bewildering variety of chemical messengers. Neuropeptides form the largest and most diverse group of neuromodulators and play important roles in learning. Growing evidence exists showing that they form dense signaling networks, with most neurons expressing many neuropeptides and receptors, but how this complex network is engaged in learning circuits has not been systematically explored in living animals. Our working hypothesis is that the concerted actions and integration of neuropeptide signals mediates flexible control of learning circuits.  We will test this hypothesis in the small and well-described nervous system of C. elegans, building on system-wide peptide network maps recently generated by my group. Given their high diversity, such a controlled context for penetrating the complex neuropeptide “language” in learning circuits is preferable. We will first identify the molecular pathways by which C. elegans neuropeptide GPCRs modulate neural activity, as this will provide the foundation to dissect their concerted actions. We will then systematically test how neuropeptide pathways impact short-term aversive learning in defined gustatory and olfactory circuits, study their impact on whole-circuit activity, and determine their temporal dynamics. Pertinent questions we will address include: ‘What are the unique versus redundant effects of neuropeptides in a learning context?’; ‘How do cellular networks encoding aversive experience respond to and integrate neuropeptidergic network activity?’ and ‘When do neuropeptidergic networks act to potentiate or attenuate learned responses?’  We expect on the long term, this project will impact our understanding of how neuropeptide networks influence and organize learning, forgetting, and adaptive behavioral responses in general.

Mots-clés : neuropeptide signaling networks learning caenorhabditis elegans g protein coupled receptors
Projet ERC : STITCH (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : KU Leuven — Pays : Belgique — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2380750.0 €

When knitting meets fluid dynamics. Knitting generates fabrics by meticulously guiding yarn into interlocking loops and stitches. Inspired by the legacy of this ancient craft, we propose to study the formation and deformation of complex entangled assemblies “stitched” by the liquid rope coiling instability. We will study the periodic stichlike patterns forming when a viscous thread falls and buckles onto a moving substrate, such as the coils created when honey is poured onto toast. Here, we focus on the case of solidifying liquids that “freeze” the patterns formed by these flows into structured solids. In turn, these structures can be leveraged to achieve tunable mechanical properties. This approach is an instability-augmented version of 3D printing where fluid dynamics co-fabricates the parts, e.g., by coiling and layering fused thermoplastics filaments. Despite their singular potential for materials’ design and fabrication, our understanding of these complex flows remains sparse, leaving outstanding scientific questions unanswered. STITCH will fill this gap of knowledge and elucidate the intricate relation between flow, form, and function.  We will integrate rigorous experimentation and modelling techniques to rationalize how our liquid patterns fuse into resilient solid composite materials. This deeper understanding will allow us to tame instabilities in solidifying melts, serving as the foundation for engineering precisely crafted architected solids across length scales, potentially impacting diverse sectors, from tissue engineering to construction. We will leverage our findings to push innovative frontiers in manufacturing with the ambitious and high-risk goal of developing aerial printing methodologies, where one or more drones work together to build from the sky. Our fundamental work is dedicated to materializing this nascent paradigm, bringing it from the realm of science fiction into concrete reality.

Mots-clés : mechanical instabilities,free surface flows,elastic threads
Projet ERC : TENACITY (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : KU Leuven — Pays : Belgique — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1988500.0 €

Creativity in language is ubiquitous. It is abundantly present in work with an explicit creative intention - such as literary novels or poems - but weighty doses of creativity also pervade everyday language use. We believe that a computational model of creativity that focuses on language will shed light on the enigmatic processes and interactions that come into play when we humans express ourselves in creative ways. Moreover, natural language generation systems - in order to produce realistic utterances - need to be endowed with a certain capacity for creativity. The main goal of this research project is to develop unsupervised models of language that exhibit creativity. In order to do so, we propose an integrated approach that combines a number of important and innovative techniques. First of all, we rely on constructs from linear algebra called tensors in order to express language content according to different parameters. Using tensors, we are able to induce latent semantics from multi-way co-occurrences of textual content, which can subsequently be used for the generation of creative expressions. Secondly, we rely on advanced machine learning techniques, notably neural networks. Neural network techniques have recently shown impressive performance in a number of natural language processing tasks. Yet, these techniques are mainly mimicking human language production, and thus are showing little creativity in language generation; by adapting neural network approaches in various ways, as well as integrating them with our tensor-based approach, we expect to develop algorithms that are able to grasp the meaning of textual content in a more profound and elaborate way, and at the same time are able to express it with creative intent. The project has the potential for groundbreaking results, not only because it would deepen our understanding of creativity, but also because of practical applications within the field of natural language processing.

Mots-clés : creativity,language generation,tensor factorization,neural networks
Projet ERC : PROSPER (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : Forschungszentrum Jülich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : FZJ
Type d'entité : Recherche
Financement ERC de l'entité : 1999485.0 €

Replacement of fossil chemicals with biological counterparts has been widely accepted as a vital pursuit to increase the sustainability of our chemical and material industries. Synthetic biology and metabolic engineering enable us to produce a plethora of chemicals with microbes, but the majority of these never make it past the proof-of-principle stage. This is especially the case for drop-in bulk aromatics like styrene or benzene. The main reason for this is that such products are too toxic to ordinary production microbes. In PROSPER I aim to overcome this hurdle and demonstrate the efficient microbial production of hydrophobic aromatic chemicals using solvent-tolerant Pseudomonas. I will engineer this unique extremophile to break the solubility barrier of these chemicals, forming a second phase of product. This second phase provides a virtually endless product sink and it enables extremely simple downstream recovery.  The bio-based production of a second phase of such chemicals has thus far never been shown. I believe that this relates to a fundamental problem in biotechnology: production tolerance, i.e., tolerance of the producing organism to the produced product, rather than to an externally added chemical (as it is usually studied). In PROSPER I intend to generate deep mechanistic insights into the processes governing both types of tolerance and to leverage these insights to open up a new field of biotechnological production of hydrophobic compounds. To achieve this, I will develop new methods to analyze intracellular solvent concentrations, build a Pseudomonas chassis with enhanced production tolerance to hydrophobic solvents, and enable production of solvents like styrene, ethylbenzene, and even benzene.  I am in a unique position to achieve this goal, with over 15 years of experience in the engineering of Pseudomonas as a workhorse in biotechnology, the study of solvent-tolerance, and the development and application of synthetic biology tools and metho

Mots-clés : pseudomonas,solvent tolerance,metabolic engineering,aromatics,synthetic biology
Projet ERC : INSIDE-BONE (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université de Vienne — Pays : Autriche — Rôle : PI
Acronyme de l'entité : Uni Wien
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1924258.0 €

Bones are pivotal to long-term health, providing structural support and facilitating mobility. However, various patient groups commonly develop torsional bony deformities, leading to numerous clinical problems. Understanding, monitoring, and ultimately modifying bone growth is crucial for improving the functional mobility of these patients and preserving lifelong health. Despite recognizing, since the 19th century, that bone growth is influenced by mechanical loads, the specific loading environments leading to typical growth or deformities remain unknown. This project fills this knowledge gap by examining the intricate relationships between mechanical loading, bone growth, and femoral morphology in typically developing children and those with torsional deformities. Quantifying an individual’s bone loads and predicting subsequent femur growth poses scientific and technical challenges. I will address these challenges by advancing state-of-the-art, multi-scale simulations to estimate muscle forces, joint loads, and growth plate stresses, and by refining a mechanobiological model to predict bone growth. The simulations will be personalized and validated using medical images and motion capture data – collected longitudinally – from growing children. Each simulation step will be extensively tested and validated. After testing, we will use this framework to investigate the effects of both surgical (i.e., derotation osteotomy) and non-surgical (i.e., gait retraining based on real-time biofeedback) interventions on growth plate loads, providing new insight into the capacity of these treatments to normalize bone growth.  This project, if successful, may lead to a paradigm shift away from reactive interventions to treat bony deformities involving invasive surgeries towards proactive non-invasive interventions to prevent the development of deformities. Even partial success promises ground-breaking insights into the intricate relationship between bone loads and morphology.

Mots-clés : biomechanics,gait analysis,musculoskeletal model,finite element model,mechanical loading,anteversion angle,torsional deformities,multiscale models,predictive models
Projet ERC : REAL-RL (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université Eberhard Karl de Tübingen — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998500.0 €

REAL-RL proposes a path to autonomous robots that learn from experience. By learning to solve new and challenging tasks and exploiting their specific capabilities, they could become ubiquitous assistants to humans in an uncountable number of tasks. Current control strategies for robots are developed only for particular tasks and are not versatile. To ensure their functioning, it is necessary to have highly accurate physical models that precisely match all the essential aspects of the real world. REAL-RL follows a different path: a learning approach to robot control. The dominant direction in the field uses model-free reinforcement learning methods that need an incredible number of interactions with the world – often prohibitive for real robots. As a bypass, simulations are used but require detailed knowledge of all possible situations that the robot might encounter. These problems are circumvented in REAL-RL by proposing a model-based approach. Models of the interaction with the world are learned from experience and will be used to plan and adapt behavior on the fly. This approach promises to be much more data-efficient and allows to transfer of valuable experience between tasks. Fundamental challenges in model-learning, safety-aware exploration and planning, and higher-order reasoning are identified and presented here with concrete novel solution ideas, such as a causal inductive bias for deep dynamics models, risk-aware real-time general trajectory optimization, and differentiable discrete planning. Critical stepping stones, such as probabilistic models and fast trajectory planning, have just been developed by the community, and the applicant. By aiming at a generic learning method that can be used to control any robot – rigid or soft – with legs, arms, or other end-effectors for manipulation and locomotion tasks, and make them improve with experience, the proposal develops a solid basis for future robotic applications.

Mots-clés : machine learning,model based reinforcement learning,planning,simulation to real transfer
Projet ERC : MinSynCell (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: B iological  Mechanisms , Structures  and Functions
Entité participante : Université de la Sarre — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1974661.06 €

A grand challenge in bottom-up synthetic biology is to design and construct synthetic cells with life-like properties from a minimal number of parts. Achieving this goal would be a major engineering feat and enable an understanding of how living systems work from the perspective of physical chemistry. Towards this, we have exploited bottom-up approaches and generated new insights into the impact of compartmentalization on the thermodynamics and kinetics of incorporated enzyme reactions. Our findings that dynamic coacervation can ignite dormant enzyme reactions provides the conceptual framework for our plan to build sustained out-of-equilibrium synthetic cellular systems. In MinSyn, the aims are to: 1) Define how molecular reaction networks are tuned by compartmentalization. 2) Build minimal synthetic compartments with self-sustained, out-of-equilibrium behaviour. 3) Utilize communication to coordinate reaction networks within populations of cells. Together, these objectives test our overarching hypothesis that sustained out-of-equilibrium systems can be established by interconnecting three features: molecular reaction networks, compartmentalization and communication. Key to this endeavour is our unique combination of chemical, biochemical and biophysical tools for quantitative characterization of synthetic cellular systems. We are primed to address the major engineering challenge of building sustained out-of-equilibrium synthetic cellular systems and to tackle a central problem in biological sciences: “How do biological cells and tissues sustain life from collections of non-living molecules?” Our interdisciplinary approach will provide novel tools to the community and represents a unique multidisciplinary approach that will ultimately define the chemico-physico parameters of life. This can lead to unprecedented opportunities to rationally engineer molecular systems which may supersede biological capabilities.

Mots-clés : bottom up synthetic biology,cell free expression systems,coacervates,synthetic cells