ERC FUNDED PROJECTS

Although we have extensive knowledge of many important processes in cell biology, including information on many of the molecules involved and the physical interactions among them, we still do not understand most of the dynamical features that are the essence of living systems. This is particularly true for the actin cytoskeleton, a major component of the internal architecture of eukaryotic cells. In living cells, actin networks constantly assemble and disassemble filaments while maintaining an apparent stable structure, suggesting a perfect balance between the two processes. Such behaviors are called “dynamic steady states”. They confer upon actin networks a high degree of plasticity allowing them to adapt in response to external changes and enable cells to adjust to their environments. Despite their fundamental importance in the regulation of cell physiology, the basic mechanisms that control the coordinated dynamics of co-existing actin networks are poorly understood. In the AAA project, first, we will characterize the parameters that allow the coupling among co-existing actin networks at steady state. In vitro reconstituted systems will be used to control the actin nucleation patterns, the closed volume of the reaction chamber and the physical interaction of the networks. We hope to unravel the mechanism allowing the global coherence of a dynamic actin cytoskeleton. Second, we will use our unique capacity to perform dynamic micropatterning, to add or remove actin nucleation sites in real time, in order to investigate the ability of dynamic networks to adapt to changes and the role of coupled network dynamics in this emergent property. In this part, in vitro experiments will be complemented by the analysis of actin network remodeling in living cells. In the end, our project will provide a comprehensive understanding of how the adaptive response of the cytoskeleton derives from the complex interplay between its biochemical, structural and mechanical properties.

2 349 898 €

Start date: 2017-09-01, End date: 2022-08-31

Fundamental interactions in nature are well described by quantum gauge fields in 4 space-time dimensions (4d). When the strength of gauge interaction is weak the Feynman perturbation techniques are very efficient for the description of most of the experimentally observable consequences of the Standard model and for the study of high energy processes in QCD. But in the intermediate and strong coupling regime, such as the relatively small energies in QCD, the perturbation theory fails leaving us with no reliable analytic methods (except the Monte-Carlo simulation). The project aims at working out new analytic and computational methods for strongly coupled gauge theories in 4d. We will employ for that two important discoveries: 1) the gauge-string duality (AdS/CFT correspondence) relating certain strongly coupled gauge Conformal Field Theories to the weakly coupled string theories on Anty-deSitter space; 2) the solvability, or integrability of maximally supersymmetric (N=4) 4d super Yang-Mills (SYM) theory in multicolor limit. Integrability made possible pioneering exact numerical and analytic results in the N=4 multicolor SYM at any coupling, effectively summing up all 4d Feynman diagrams. Recently, we conjectured a system of functional equations - the AdS/CFT Y-system – for the exact spectrum of anomalous dimensions of all local operators in N=4 SYM. The conjecture has passed all available checks. My project is aimed at the understanding of origins of this, still mysterious integrability. Deriving the AdS/CFT Y-system from the first principles on both sides of gauge-string duality should provide a long-awaited proof of the AdS/CFT correspondence itself. I plan to use the Y-system to study the systematic weak and strong coupling expansions and the so called BFKL limit, as well as for calculation of multi-point correlation functions of N=4 SYM. We hope on new insights into the strong coupling dynamics of less supersymmetric gauge theories and of QCD.

1 456 140 €

Start date: 2013-11-01, End date: 2018-10-31

A massive and ever growing amount of digital image and video content is available today, on sites such as Flickr and YouTube, in audiovisual archives such as those of BBC and INA, and in personal collections. In most cases, it comes with additional information, such as text, audio or other metadata, that forms a rather sparse and noisy, yet rich and diverse source of annotation, ideally suited to emerging weakly supervised and active machine learning technology. The ALLEGRO project will take visual recognition to the next level by using this largely untapped source of data to automatically learn visual models. The main research objective of our project is the development of new algorithms and computer software capable of autonomously exploring evolving data collections, selecting the relevant information, and determining the visual models most appropriate for different object, scene, and activity categories. An emphasis will be put on learning visual models from video, a particularly rich source of information, and on the representation of human activities, one of today's most challenging problems in computer vision. Although this project addresses fundamental research issues, it is expected to result in significant advances in high-impact applications that range from visual mining of the Web and automated annotation and organization of family photo and video albums to large-scale information retrieval in television archives.

2 493 322 €

Start date: 2013-04-01, End date: 2019-03-31

Malaria parasite infection in humans has been called “the strongest known force for evolutionary selection in the recent history of the human genome”, and I hypothesize that a similar statement may apply to the mosquito vector, which is the definitive host of the malaria parasite. We previously discovered efficient malaria-resistance mechanisms in natural populations of the African malaria vector, Anopheles gambiae. Aim 1 of the proposed project will implement a novel genetic mapping design to systematically survey the mosquito population for common and rare genetic variants of strong effect against the human malaria parasite, Plasmodium falciparum. A product of the mapping design will be living mosquito families carrying the resistance loci. Aim 2 will use the segregating families to functionally dissect the underlying molecular mechanisms controlled by the loci, including determination of the pathogen specificity spectra of the host-defense traits. Aim 3 targets arbovirus transmission, where Anopheles mosquitoes transmit human malaria but not arboviruses such as Dengue and Chikungunya, even though the two mosquitoes bite the same people and are exposed to the same pathogens, often in malaria-arbovirus co-infections. We will use deep-sequencing to detect processing of the arbovirus dsRNA intermediates of replication produced by the RNAi pathway of the mosquitoes. The results will reveal important new information about differences in the efficiency and quality of the RNAi response between mosquitoes, which is likely to underlie at least part of the host specificity of arbovirus transmission. The 3 Aims will make significant contributions to understanding malaria and arbovirus transmission, major global public health problems, will aid the development of a next generation of vector surveillance and control tools, and will produce a definitive description of the major genetic factors influencing host-pathogen interactions in mosquito immunity.

2 307 800 €

Start date: 2013-03-01, End date: 2018-02-28