Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Evolchip (Horizon 2020, appel 2017, Proof of concept grants)
Entité participante : Freie Universität Berlin — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : FU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 148500.0 €

Antibiotic resistance is rife (fig. 1), yet notoriously difficult to predict. Here I propose to assess the applied potential of an easy to use device, ‘EVOLchip’ that allows to test antibiotic treatment schemes within a few weeks for their risk of inducing antimicrobial resistance. If successful, this would allow to use current antibiotics much longer and to reuse old antibiotics in combination. Such tests can be conducted at hospital level by any trained medical microbiologist and hence be implemented into routine antibiotic stewardship. The proposed project entails (a) a praxis test with an enduser, a large university hospital, (b) an IPstrategy (c) a market and competitor analysis (d) a finance plan (e) a strategy to engage industrial partners and end-users.

Mots-clés : antibiotic resistance,antibiotic stewardship,sustainable antibiotic stewardship,combination therapy,microfludice
Projet ERC : ABRSEIST (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : University of Copenhagen — Pays : Danemark — Rôle : Partner
Acronyme de l'entité : UCPH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 594923.75 €

Antibiotics have contributed to a tremendous increase in human well-being, saving many millions of lives. However, antibiotics become obsolete the more they are used as selection pressure promotes the development of resistant bacteria. The World Health Organization has proclaimed antibiotic resistance as a major global threat to public health. Today, 700,000 deaths per year are due to untreatable infections. To win the battle against antibiotic resistance, new policies affecting the supply and demand of existing and new drugs must be designed. I propose new research to identify and evaluate feasible and effective demand-side policy interventions targeting the relevant decision makers: physicians and patients. ABRSEIST will make use of a broad econometric toolset to identify mechanisms linking antibiotic resistance and consumption exploiting a unique combination of physician-patient-level antibiotic resistance, treatment, and socio-economic data. Using machine learning methods adapted for causal inference, theory-driven structural econometric analysis, and randomization in the field it will provide rigorous evidence on effective intervention designs. This research will improve our understanding of how prescribing, resistance, and the effect of antibiotic use on resistance, are distributed in the general population which has important implications for the design of targeted interventions. It will then estimate a structural model of general practitioners’ acquisition and use of information under uncertainty about resistance in prescription choice, allowing counterfactual analysis of information-improving policies such as mandatory diagnostic testing. The large-scale and structural econometric analyses allow flexible identification of physician heterogeneity, which ABRSEIST will exploit to design and evaluate targeted, randomized information nudges in the field. The result will be improved rational use and a toolset applicable in contexts of antibiotic prescribing.

Mots-clés : health economics,pharmaceutical demand,bayesian learning,machine learning,structural modeling,field experimentation
Projet ERC : FuncMAB (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1223000.0 €

Antibodies play an important role ensuring successful protection after vaccination. Upon injection, antigen-binding antibodies are generated to prime the host’s immune system for future encounters with the threat. These responses are highly heterogeneous, with each cell contributing with a single antibody variant to the complexity. Each antibody variant furthermore can recognize a different antigen/epitope with varying specificity and affinity. The immunological function induced is related to those parameters. Depending on the nature of the threat, required protective functional antibodies vary. Therefore, also each vaccination against those threads needs to trigger a specific functional antibody repertoire. Presently, induced functional antibody repertoires have not yet been studied sufficiently, mostly due to the lack of technologies that enable analysing these repertoires with high enough throughput and resolution. Consequently, the mechanisms behind the evolution of these functional repertoires, and the influence of vaccination on these repertoires remain poorly understood. An innovative technology combined with a methodical approach to vaccinations will enable the FuncMab research team to generate data sets needed for the understanding of immunological processes that result in different functional antibody repertoires. Herein, antibodies are analysed on the individual cell level in high-throughput using specific bioassays that target various antibody functions and their biophysical parameters, generating high-resolution data. These functional repertoires are followed over time and evolutionary changes can be linked to introduced vaccine variations, allowing a quantitative approach to study the changes within the repertoires. These in-depth data sets will not only allow understanding interactions between vaccine components and their generated immune responses, but also propels this project to the forefront of creating a new generation of successful vaccines

Mots-clés : bioanalytical science,antibody producing cells
Projet ERC : APHIDTRAP (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : University of Dundee — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999992.0 €

Aphids are devastating insect pests of crops globally, and pose a major threat to food security. Crucially, there is a lack of durable genetic crop resistance against aphids, and current control relies almost exclusively on insecticides, which are costly and damaging to the environment and to which aphids develop resistance. These insects deliver proteins inside host plants, called effectors, to suppress the plant immune system and enhance susceptibility. I recently discovered that these effectors exhibit their activity via interacting with host plant proteins pointing to important conceptual parallels between plant-insect and plant-microbe interactions. This raises important new questions that urgently need to be addressed to enable development of novel protection strategies against aphids that are durable and sustainable. These are: What is the mechanistic and structural basis of aphid effector-triggered susceptibility? How can we interfere with aphid effector-triggered susceptibility? APHIDTRAP will address these questions using an innovative strategy: 1) I will introduce a structural biology approach to the insect effector biology field to reveal protein 3D structures of aphid effectors and their host protein targets in bound and unbound state, and determine how mutations in these proteins affect interactions and protein functions. 2) I will use both natural variants and mutants of effectors and host protein targets, combined with in planta functional assays to explore plant-aphid molecular co-evolution. 3) I will identify host protein target interactomes and investigate how mutations affect network functionality. 4) I will use information generated in 1-3 to develop and apply a synthetic biology approach to prevent aphid effector-triggered susceptibility in potato crop plants. APHIDTRAP’s vision is to elucidate the mechanisms that underlie susceptibility to aphids and investigate how we can interfere with these to reduce crop susceptibility to insect pests.

Mots-clés : aphid effector,structural biology,host susceptibility
Projet ERC : KissAndSpitRhoptry (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : Institut national de la santé et de la recherche médicale — Pays : France — Rôle : PI
Acronyme de l'entité : Inserm
Type d'entité : Recherche
Financement ERC de l'entité : 2496210.0 €

Apicomplexan are obligatory intracellular parasites. The ability of these parasites (Plasmodium, Toxoplasma) to cause disease depends on the coordinated secretion of specialized secretory organelles. The rhoptries are particularly important, because they act as the apicomplexan equivalent of bacterial secretion systems. They inject parasite proteins directly in the cytoplasm of host cells not only for invasion but also to hijack host functions crucial to establish and maintain infection. However, in contrast to bacteria where the secretion machinery has been resolved to atomic detail, how eukaryotic parasites secrete and inject rhoptry effectors into cells is an enigma. This proposal aims to dissect the mechanistic steps and the molecular components that assemble the rhoptry secretion machine. Our aims are: 1- To explore the mechanisms that trigger rhoptry exocytosis upon binding of the parasite to the host cell. 2- To provide insights into fusion machinery of rhoptry with the parasite plasma membrane. Our model is based on the discovery that free-living Ciliates and intracellular Apicomplexa share an evolutionarily conserved blueprint for their fusion mechanism. 3- To test and expand our hypothesis that rhoptries deliver their content through a transient pore formed into the host cell membrane. We will employ powerful experimental systems in Toxoplasma, Plasmodium and the Ciliate Tetrahymena, taking full advantage of the relative strength of each model. This comprehensive project will bring together comparative genomics, targeted and global genetics, biochemistry, high resolution imaging and electrophysiology. This project answers a question of fundamental biological importance. How can a parasite sense the host cell and inject virulence factors to attain control? Understanding this mechanism will guide future efforts to disrupt parasite infection and will contribute to broader understanding of fascinating questions of membrane fusion and export processes.

Mots-clés : apicomplexa,toxoplasma,plasmodium,secretory organelle,rhoptry,exocytosis,export,virulence factors
Projet ERC : AppSAM (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : University of Freiburg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499219.0 €

AppSAM will unlock the synthetic capability of S-adenosyl¬methionine (SAM)-dependent methyltransferases and radical SAM enzymes for application in environmentally friendly and fully sustainable reactions. The biotechnological application of these enzymes will provide access to chemo-, regio- and stereoselective methylations and alkylations, as well as to a wide range of complex rearrangement reactions that are currently not possible through traditional approaches. Methylation reactions are of particular interest due to their importance in epigenetics, cancer metabolism and the development of novel pharmaceuticals. As chemical methylation methods often involve toxic compounds and rarely exhibit the desired selectivity and specificity, there is an urgent need for new, environmentally friendly methodologies. The proposed project will meet these demands by the provision of modular in vitro and in vivo systems that can be tailored to specific applications. In the first phase of AppSAM, efficient in vitro SAM-regeneration systems will be developed for use with methyltransferases as well as radical SAM enzymes. To achieve this aim, enzymes from different biosynthetic pathways will be combined in multi-enzyme cascades; methods from enzyme and reaction engineering will be used for optimisation. The second phase of AppSAM will address the application on a preparative scale. This will include the isolation of pure product from the in vitro systems, reactions using immobilised enzymes and extracts from in vivo productions. In addition to E. coli, the methylotrophic bacterium Methylobacter extorquens AM1 will be used as a host for the in vivo systems. M. extorquens can use C1 building blocks such as methanol as the sole carbon source, thereby initiating the biotechnological methylation process from a green source material and making the process fully sustainable, as well as being compatible with an envisaged “methanol economy”.

Mots-clés : methylation,s adenosylmethionine,enzyme application
Projet ERC : KidsSurgicalPlan (Horizon 2020, appel 2014, Proof of concept grants)
Entité participante : Koç University — Pays : Turquie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Approximately 1 in 100 children are born with a clinically significant congenital heart defect representing 47,000 children each year in the EU. Survival of the newborn requires series of complex open-heart surgeries where optimal hemodynamic performance is essential for improved post-operative quality of life. This work proposes a new computationally-enhanced interactive system for patient-specific pre-surgical design and optimization of pediatric cardiovascular surgeries. With the proposed system, clinicians will be able to rapidly implement a contemplated intervention on a patient-specific 3D computer model without execution. A novel interface will allow clinicians to communicate their surgical intentions directly on the computer model analogous to the way they would communicate their intentions to other clinicians or bioengineers. Basic cardiovascular research on embryonic biomechanics that is being investigated in the parent ERC project will provide the essential foundation of the proposed predictive capability. This interactive framework will be deployed to the web for worldwide impact. Based on market analysis our proposed business model becomes profitable in a short time. PoC project will provide necessary resources for developing the prototype patient-specific surgical-planning framework, clarification of regulatory pathways and follow-up pre-clinical research design.

Mots-clés : congenital heart defects,blood flow,hemodynamics,vascular growth,pediatric cardiovascular surgeries,patient specific,cardiovascular fluid dynamics,cardiovascular medical devices
Projet ERC : ARBODYNAMIC (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : Institut Pasteur — Pays : France — Rôle : Partner
Type d'entité : Recherche
Financement ERC de l'entité : 36250.0 €

Arboviruses infect millions of people each year, however, mechanisms that drive viral emergence and maintenance remain largely unknown. A combination of host factors (e.g., human mobility), mosquito factors (e.g., abundance) and viral factors (e.g., transmissibility) interconnect to drive spread. Further, for endemic arboviruses, complex patterns of population immunity, built up over many years, appear key to the emergence of particular lineages. To disentangle the contribution of these different drivers, we need detailed data from the same pathogen system over a long time period from the same location. In addition, we need new methods, which can integrate these different data sources and allow appropriate mechanistic inferences. In this project, I will use the most globally prevalent arbovirus, dengue virus, as a case study. I will focus on Thailand where all four dengue serotypes have circulated endemically for decades and excellent long-term data and isolates exist, to address two fundamental questions: i) How do population-level patterns of immunity evolve over time and what is their impact on strain dynamics? I will use mechanistic models applied to historic serotype-specific case data to reconstruct the evolving immune profile of the population and explore the impact of immunity on viral diversity using sequences from archived isolates from each year over a 50-year period. ii) How do human behaviors, vector densities interact with immunity to dictate spread? I will work with geolocated full genome sequences from across Thailand and use detailed data on how people move, their contact patterns, their immunity profiles and mosquito distributions to study competing hypotheses of how arboviruses spread. I will compare the key drivers of dengue spread with that found for outbreaks of Zika and chikungunya. This proposal addresses fundamental questions about the mechanisms that drive arboviral emergence and spread that will be relevant across disease systems.

Mots-clés : arboviruses,human behavior,antigenic evolution,immunity
Projet ERC : Endogenous_Info (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1588958.75 €

Are financial markets informationally efficient? Are some economic agents more informed than others? What is the impact of such heterogeneity on asset prices? These questions, of great economic significance, have permeated academic and business circles over the last few decades. Despite significant progress on the theoretical and empirical fronts relatively little is known about how information is endogenously acquired and processed in markets by agents with differential access to information and facing heterogeneous opportunities. Using the novel setting of endogenous information acquisition with non-trivial heterogeneity the project has two goals: (A) to lay out micro foundations for informed trading in investment and corporate settings using the contexts of illegal insider trading and household finance; (B) to investigate macro implications of heterogeneous information for global economic phenomena such as income inequality, organizational design, and market power.

Mots-clés : information frictions,financial markets,asymmetric information,institutional investors,income inequality,insider trading
Projet ERC : ZETA-FM (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : University of Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UvA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Arithmetic geometry and the study of derived categories of coherent sheaves are two central areas of research in algebraic geometry. Despite their many points of contact, they have until recently remained largely disjoint. The zeta function of an algebraic variety over a finite field is one of the most studied invariants in arithmetic geometry, and a conjecture of Orlov predicts that this invariant can be detected by the derived category of coherent sheaves on the variety. In this project, I will prove this for large classes of varieties. To achieve this, I will enrich a wide range of techniques from arithmetic geometry with ideas that have classically been used in the study of derived categories. In this way, this project will also serve as a catalyst for further interaction between arithmetic geometry and derived categories.

Mots-clés : derived categories,arithmetic geometry,fourier mukai transforms,zeta functions
Projet ERC : ARiAT (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1421479.0 €

Arithmetic theories are logical theories for reasoning about number systems, such as the integers and reals. Such theories find a plethora of applications across computer science, including in algorithmic verification, artificial intelligence, and compiler optimisation. The appeal of arithmetic theories is their generality: once a problem has been formalised in a decidable such theory, a dedicated solver can in principle be used in a push-button fashion to obtain a solution. Arithmetic theories are also of great importance for showing decidability and complexity results in a variety of domains. Decision procedures for quantifier-free and linear fragments of arithmetic theories have been among the most intensively studied and impactful topics in theoretical computer science. However, emerging applications require more expressive theories, including support for quantifiers, counting, and non-linear functions. Unfortunately, the lack of understanding of the computational properties of such extensions means that existing decision procedures are not applicable or do not scale. The overall goal of this proposal is to advance the state-of-the-art in decision procedures for expressive arithmetic theories. To this end, starting with a recent breakthrough made by the PI, we will develop novel and optimal quantifier-elimination procedures for linear arithmetic theories, which we plan to eventually integrate into mainstream SMT solvers. Furthermore, we aim to improve complexity bounds and push the decidability frontier of extensions of arithmetic theories with counting and non-linear operations. The proposed research requires to tackle long-standing open problems---some of them being decades old. In short, the project will lay algorithmic foundations on which next-generation decision procedures and reasoners for arithmetic theories will be built.

Mots-clés : logic in computer science,arithmetic theories,decision procedures,algorithmic verification,quantifier elimination,counter machines