Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : 3D-FNPWriting (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Technical University of Darmstadt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499844.0 €

Membranes are key materials in our life. Nature offers high performance membranes relying on a parallel local regulation of nanopore structure, functional placement, membrane composition and architecture. Existing technological membranes are key materials in separation, recycling, sensing, energy conversion, being essential components for a sustainable future. But their performance is far away from their natural counterparts. One reason for this performance gap is the lack of 3D nanolocal control in membrane design. This applies to each individual nanopore but as well to the membrane architecture. This proposal aims to implement 3D printing (additive manufacturing, top down) and complex near-field and total internal reflection (TIR) high resolution microscopy induced polymer writing (bottom up) to nanolocally control in hierarchical nanoporous membranes spatially and independent of each other: porosity, pore functionalization, membrane architecture, composition. This disruptive technology platform will make accessible to date unachieved, highly accurate asymmetric nanopores and multifunctional, hierarchical membrane architecture/ composition and thus highly selective, directed, transport with tuneable rates. 3D-FNPWriting will demonstrate this for the increasing class of metal nanoparticle/ salt pollutants aiming for tuneable, selective, directed transport based monitoring and recycling instead of size-based filtration, accumulation into sewerage and distribution into nature. Specifically, the potential of this disruptive technology with respect to transport design will be demonstrated for a) a 3D-printed in-situ functionalized nanoporous fiber architecture and b) a printed, nanolocally near-field and TIR-microscopy polymer functionalized membrane representing a thin separation layer. This will open systematic understanding of nanolocal functional control on transport and new perspectives in water/ energy management for future smart industry/ homes.

Mots-clés : 3d nanolocal nanopore functionalization,high resolution microscopy induced polymer writing,membrane architecture printing,selective directed rate tunable transport,metal nanoparticle recycling
Projet ERC : EMOTION (Horizon 2020, appel 2014, Proof of concept grants)
Entité participante : Aalto University — Pays : Finlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149778.0 €

Memory technologies that are used in computers, smart phones, tablets, etc. can be coarsely divided into two classes: fast but volatile DRAMs (dynamic random access memory) and non-volatile flash memories. Racetrack memory based on the controlled motion of magnetic domain walls is a promising new technology for next generation memories, because it combines the speed of DRAMs and the non-volatile properties of flash memories. One key drawback, however, is the required use of intense electric currents to move magnetic domain walls along narrow wires, which causes undesirable heat dissipation and considerable power losses. With this ERC proof-of-concept proposal, we aim at exploring the empty technology space around a recent invention in our laboratory, namely the ability to drive magnetic domain walls using electric fields (i.e. voltages) rather than electric currents in ferroelectric-ferromagnetic bilayers. Since no electric currents are flowing through the insulating ferroelectric layer during prototype device operation, power consumption and heating are minimal. Moreover, electric-field control of magnetic domain wall motion in our experimental demonstrator is deterministic and completely reversible. Using the ERC proof-of-concept grant, time and money will be invested for obtaining IPR protection for this new technology, different ways for industrial collaboration will be investigated (e.g. licensing of the technology/IPR directly or founding of a technology licensing spin-out) and contacts will be sought. A market analysis will be performed and a commercialisation roadmap and IPR strategy will be created.

Mots-clés : nanotechnology,magnetic memory,electric field controlled magnetism
Projet ERC : IMPACT (Horizon 2020, appel 2018, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : KU Leuven — Pays : Belgique — Rôle : PI
Acronyme de l'entité : KUL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149688.0 €

Mental health problems constitute a major individual, family and public health concern, affecting up to 30% of the population. Yet, both pharmacological and psychological interventions only reach modest effect sizes. There is a need for new and better personalised interventions. In my ERC 'INTERACT' project, we adopted a mobile Health (mHealth) approach for the treatment of psychotic disorder. We developed ACT-DL (Acceptance and Commitment in Daily Life), an app that complement face-to-face ACT sessions with ACT exercises in real-life, guiding individuals in the application of ACT in real-time. This mHealth approach a) extends the therapy beyond the clinical setting into real life, b) makes therapy more widely available, and c) provides therapy at specific moments of need. The problem that remains to be addressed in this Proof-of-Concept project is to bring ACT-DL to the clinical market. In IMPACT, we aim to transform the research app into a clinical ACT-DL app that can be implemented in a naturalistic clinical setting. In order to do that, we will: 1) adapt the ACT-DL app according to the feedback of the clinicians so that it reaches the flexibility needed in clinical practice, 2) develop an implementation strategy plan focusing on integration of ACT-DL in the existing health care infrastructure (data safety, data privacy, data integration and long-term sustainability) and on clinical uptake by clinicians, 3) run an implementation pilot to test acceptability, feasibility and generalisability, and 4) develop a business model. The implementation of ACT-DL in the naturalistic clinical setting will have clear societal and economic impact as it will improve the effectivity of treatment while we give patients a tool to help themselves. Furthermore, as very little mHealth approaches have been integrated in the clinical pathways of care within psychiatry, it will inform policy makers on the further development of mHealth approaches in this field.

Mots-clés : mhealth,psychotherapy,mental disorders,personalized intervention,daily life treatment
Projet ERC : I-RISK (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Erasmus MC — Pays : Pays-Bas — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 658918.0 €

Mental illnesses account for 32% of years lived with disability. Critically, 50% of mental illnesses are established before age 14 years. It is thus imperative to better understand the causes of early psychiatric symptoms in childhood if we are to design effective preventive interventions. Parental risk factors –e.g. psychiatric disorders and substance use– have consistently emerged as strong predictors of child psychiatric symptoms. However, we do not know whether such parental risk factors are causes or mere correlates of child symptoms and how this transmission of risk across generations occurs. The overarching aim of I-RISK is to identify causal intergenerational pathways linking parental risk factors to child psychiatric symptoms. I-RISK will implement and develop cutting-edge genetically informed methods to enable better causal inference in intergenerational research. Three work packages will address major limitations in state-of-the-art research: (i) no study to date has applied such causal inference methods to identify intergenerational risk factors for child psychiatric symptoms; (ii) the role of intermediate phenotypes, whether environmental (e.g. parenting) or biological (epigenetic and brain data), in explaining the intergenerational transmission of risk remains poorly understood; (iii) genetically informed intergenerational research is still in its infancy and key methodological developments are required to propel the field forward. I-RISK will generate new genomic data and bring together four of the largest child cohorts in Europe (pooled N >100,000). These cohorts feature genetic data in children and parents as well as unique data on environmental and biological intermediate phenotypes. I-RISK will pioneer a paradigm shift in intergenerational mental health research and provide unprecedented insights into the aetiology of child psychiatric symptoms, with the ultimate goal of breaking the intergenerational cycle of mental illness and inequalities.

Mots-clés : aetiology,risk factors,causal inference,psychiatric epidemiology,mental health,childhood,developmental psychopathology
Projet ERC : NICHOIDS (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : Politecnico di Milano — Pays : Italie — Rôle : PI
Acronyme de l'entité : POLIMI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 110000.0 €

Mesenchymal stem cells (MSCs) raise a great interest for regenerative medicine for many clinical applications such as orthopaedic, plastic and reconstructive surgery, within autoimmune or degenerative disease treatment and as immune-suppressive agents in organ transplantation. In stem cell therapy, cells are injected in the patient after an extensive in vitro manipulation aimed at obtaining a sufficient cell number able to guarantee the therapeutic effect. Currently, stem cell "manufacturing" implies the use of “feeder” cells and additives from animal sources, hampering the clinical use mainly for safety reasons. In the context of an ERC Consolidator grant (ERC-CoG) that I currently lead, I introduced a novel ground-breaking concept for an easy to use, repeatable, cost-effective, and safe substrate for stem cell expansion, called the “nichoid” substrate, capable of avoiding feeder cells and dangerous additives. The substrate is polymerized with a laser in a biocompatible resin using a frontier nano-fabrication technology called “two-photon laser polymerization” (2PP). To explore the possibility of an economic exploitation of this invention covered by an Italian Patent submitted in September 2015 and already extended as PCT, the goal of this PoC is to perform a technical and commercial feasibility to move the method developed in the PI’s laboratory during the ERC grant to the market. In particular, I plan to speed up the maturity level of the technology further developing the production process of individual nichoids (fabrication up-scaling by at least a factor of 10, allowing to cover a culture surface of 5 squared cm in five hours), setting of an actionable IPR strategy (including a market assessment), identification of the suitable exploitation strategy for valorising the patent/know how (licensing). The nichoid could drastically reduce down to few years the time to market of stem cell products that is currently 20 years.

Mots-clés : stem cells,adult,mesenchymal,cultivation,substrate,nanofabrication,laser,two photon polymerization,parallelization,up scaling,clinical translation,stem cell therapy,cell factory
Projet ERC : NoMePaCa (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Université Catholique de Louvain — Pays : Belgique — Rôle : PI
Acronyme de l'entité : UCL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1989103.0 €

Metabolic adaptations in central carbon metabolism play a key role in cancer. Yet, the success of therapeutic interventions in major pathways has been limited, although some of the changes have been known to exist for almost 100 years. Biochemical textbooks present intermediary metabolism as something canonical, and the molecular identity of most enzymes required for the production of known intermediary metabolites is indeed known. Yet, the function of many putative enzymes is still unknown, indicating that novel metabolic pathways containing so far unknown metabolites exist. We have recently discovered a novel metabolic pathway containing two metabolites that have never been described before. Preliminary data indicate that this pathway might play an important role in a group of cancers sharing specific mutations. Furthermore, genetic inactivation of a component of this pathway in mice is compatible with normal development, indicating that pharmacological inhibition should be well tolerated. In the present project, we will use a multi-dimensional approach combining biochemical, genetic and pharmacological techniques, to identify missing components of this metabolic pathway and assess its role in cellular metabolism and cancer development. In the process of this, we will develop tools that will allow us to test whether this pathway can be targeted in vivo. Thus, our work will lead to the description of a novel metabolic pathway, should reveal novel regulatory circuits and might open novel therapeutic avenues in cancer and beyond.

Mots-clés : cancer metabolism
Projet ERC : MapMe (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 755022.23 €

Metabolic engineering creates improved microbes for industrial biotechnology. Rational design of industrial microbes centres on modifications of genes with known roles in the production pathway of interest. However, genes that are unrelated to the production pathway are also known to substantially impact productivity. To date there are no methods that allow the prediction of such distal genes on a rational basis. Effects of distal genes are indirect, and mediated through regulatory interactions between metabolites and proteins, most of which are currently unknown even in the well-studied microbe Escherichia coli. The lack of knowledge of metabolite-protein interactions thus effectively prohibits systematic exploration of distal regulatory relationships, with the consequence that models used to predict metabolic engineering targets are severely limited and rarely applied in industrial biotechnology. Previously, we used small-scale metabolic models and metabolomics to infer metabolite-protein interactions in specific pathways. In this project we propose a novel genome-wide endeavor to map metabolite-protein interactions across the entire metabolic network. For this purpose, we will use the recently developed CRISPR interference system to quantitatively perturb >900 single metabolic genes in E. coli. Combining the metabolomics data of these >900 gene perturbations with a genome-scale metabolic model will enable us to infer functionally relevant metabolite-protein interactions. Finally, we will apply this knowledge to the model-guided metabolic engineering of superior E. coli strains. Specifically, we want E. coli to cease growth upon induction and focus all its metabolic resources towards synthesis of succinic acid. This controlled uncoupling of growth from overproduction on a rational basis will break new grounds in metabolic engineering and opens up novel applications in industrial biotechnology.
Projet ERC : PACA-MET (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Technical University of Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1995875.0 €

Metastasis is the major cause of death in pancreatic ductal adenocarcinoma (PDAC). International sequencing efforts on >800 human primaries gave comprehensive insights into PDAC genetics. In contrast, equivalent studies for “metastasis genetics” were not possible, largely because of a lack of metastatic tissue resources, particularly of treatment-naive ones. Another bottleneck is the scarcity of adequate experimental models recapitulating the multi-step nature of metastasis. As a consequence, the molecular basis of metastasis remains poorly understood. We developed unique resources and tools for metastasis research and propose to use them at three levels to systematically interrogate the molecular underpinnings of PDAC metastasis. We will first perform complementary genome-scale surveys for genes and pathways driving metastasis and metastatic organotropism. We will (i) sequence our unique, largely unpublished resource of 1200 metastatic mouse PDAC, (ii) will perform genome-wide in vivo metastasis screens using transposon tools and approaches, which we pioneered in mice, and (iii) will perturb the human metastasis transcriptome and epigenome. Second, we will validate newly discovered genes using human PDAC cohorts, and through functional studies in mice. We will deploy next-generation metastasis models based on advanced somatic genome engineering. They allow rapid functional studies at an organismal level, thus capturing the complexity of the metastatic cascade. Third, building on our recent discovery of two prototype PDAC metastasis drivers, we will perform in depth mechanistic studies to identify underlying molecular networks and vulnerabilities. This work will unravel - for the first time - comprehensive genetic and functional landscapes of PDAC metastasis. PACA-MET thus promises to uncover fundamental novel biological principles and identify therapeutic targets for one of biggest challenges in medicine.
Projet ERC : ENIGMA (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Delft University of Technology — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1422598.0 €

Micro and nanomechanical systems are being adopted in billions of products, that address a wide range of sensor and actuator applications in modern technology. The advent of graphene, and the ability to fabricate single atom thick membranes, promises further device downscaling, enabling ultimate sensing capabilities that until recently seemed utopical. But, these atomically thin membranes are in essence nonlinear and exhibit nonlinear dynamic behavior at forces of only a few pN, which needs to be understood to harness their full potential. Although the field of nonlinear dynamics dates back several centuries, its implications at the atomic scale have remained relatively unexplored. Thermal fluctuations due to Brownian motion and nanoscale forces become dominant at this scale, and when combined with graphene’s exotic elasticity, give rise to phenomena that are not observed before, and cannot be explained by classical approaches. Our poor understanding of these complex features at the same time, have made characterization of graphene very challenging. An example is its bending modulus that is evaluated orders of magnitude higher than theoretical predications, by the available experimental methods. In this project, I aim at providing full understanding of nonlinearities of these one atom thick membranes, not only to unveil the enigmatic behavior of graphene but also to improve current nanomaterial characterization methods. The distinguishing feature of my methodology is that on the one side, it will be based on atomistic simulations combined with modal order reduction techniques, to predict the complexities at the single atom level; on the other side, experimental nonlinear dynamic data will be analyzed for evaluating nonlinear effects and extracting material properties using nonlinear resonances in the MHz range. My methodology will have the potential to serve as the next generation of characterization techniques for nanomaterial science and nanomechanics communities.

Mots-clés : nonlinear structural dynamics,atomically thin membranes,reduced order modelling,experimental dynamics,nonlinear system identification,nano electromechanical systems
Projet ERC : MoP-MiP (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1499326.0 €

MicroRNAs, a class of transcript responsible for the fine regulation of gene expression, are emerging as promising biomarkers for cancer monitoring (diagnostic, prognostic and prediction of therapy response. The concentration profiling of – stably present – microRNAs from liquid biopsies represents a hopeful opportunity to fight these diseases, enabling large population screening for early cancer appearance and monitoring in real-time the treatment response. However, current microRNA detection methods do not meet performances in term of sensitivity, multiplexing and practicability to bring microRNA signatures in the front line of clinical applications. The objective of this research proposal is to develop novel approaches for microRNA profiling using concepts of molecular programming (MP). MP deals with the design of artificial DNA reaction networks capable of information-processing, including complex biosensing tasks. The team will investigate two major strategies: 1) the development of a digital and multiplex assay (Digiplex), where each microRNA is accurately quantified independently at the single molecule level using target-specific molecular program; 2) the exploration of molecular neural network (MolNNet) for concentration pattern recognition. It involves the conception of sophisticated molecular program architectures performing the integration of multiple microRNA concentrations, signal processing and reporting of the sample type (e.g. “healthy” or “diseased”). This project is highly interdisciplinary, gathering expertise in DNA nanotechnology, microfluidics, surface chemistry and machine learning. The expected outcomes include the advance of cutting-edge microRNA quantification technology combining single-molecule amplification with a multiplex readout (up to 100 targets). On the long term, the exploration of in moleculo neural networks is foreseen to trigger a whole new field of research, providing a ground-breaking approach for molecular diagnostics.

Mots-clés : microrna profiling,molecular programming,biosensors,microfluidics,dna nanotechnology,multiplex and digital assay,molecular neural network