ERC FUNDED PROJECTS

Nature has evolved ultimate chemical functions based on controlling and altering conformation of its molecular machinery. Prominent examples include enzyme catalysis and information storage/duplication in nucleic acids. These achievements are based on large and complex yet remarkably defined structures obtained through folding of polymeric chains and a subtle interplay of non-covalent forces. Nature uses a limited set of building blocks – e.g. twenty amino-acids and four nucleobases – with specific abilities to impart well-defined folds. In the last decade, chemists have discovered foldamers: non-natural oligomers and polymers also prone to adopt folded structures. The emergence of foldamers has far reaching implications. A new major long term prospect is open to chemistry: the de novo synthesis of artificial objects resembling biopolymers in terms of their size, complexity, and efficiency at achieving defined functions, yet having chemical structures beyond the reach of biopolymers amenable to new properties and functions. The PI of this project has shown internationally recognized leadership in the development of a class of foldamers, aromatic oligoamides, whose features arguably make them the most suitable candidates to systematically explore what folded structures beyond biopolymers give access to. This project aims at developing methods to allow the routine fabrication of 20-40 units long aromatic oligoamide foldamers (6-15 kDa) designed to fold into artificial molecular containers having engineerable cavities and surfaces for molecular recognition of organic substrates, in particular large peptides and saccharides, polymers, and proteins. The methodology rests on modelling based design, multistep organic synthesis of heterocyclic monomers and their assembly into long sequences, structural elucidation using, among other techniques, x-ray crystallography, and the physico-chemical characterization of molecular recognition events.

2 496 216 €

Start date: 2013-06-01, End date: 2018-05-31

The amazing rate of progress in the computer technologies and telecommunications presents many new challenges for Optimization Theory. New problems are usually very big in size, very special in structure and possibly have a distributed data support. This makes them unsolvable by the standard optimization methods. In these situations, old theoretical models, based on the hidden Black-Box information, cannot work. New theoretical and algorithmic solutions are urgently needed. In this project we will concentrate on development of fast optimization methods for problems of big and very big size. All the new methods will be endowed with provable efficiency guarantees for large classes of optimization problems, arising in practical applications. Our main tool is the acceleration technique developed for the standard Black-Box methods as applied to smooth convex functions. However, we will have to adapt it to deal with different situations. The first line of development will be based on the smoothing technique as applied to a non-smooth functions. We propose to substantially extend this approach to generate approximate solutions in relative scale. The second line of research will be related to applying acceleration techniques to the second-order methods minimizing functions with sparse Hessians. Finally, we aim to develop fast gradient methods for huge-scale problems. The size of these problems is so big that even the usual vector operations are extremely expensive. Thus, we propose to develop new methods with sublinear iteration costs. In our approach, the main source for achieving improvements will be the proper use of problem structure. Our overall aim is to be able to solve in a routine way many important problems, which currently look unsolvable. Moreover, the theoretical development of Convex Optimization will reach the state, when there is no gap between theory and practice: the theoretically most efficient methods will definitely outperform any homebred heuristics.

2 090 038 €

Start date: 2018-09-01, End date: 2023-08-31

Amorphous and evolutionary DNA nanotechnology (AEDNA) explores novel conceptual directions and applications for DNA nanotechnology, which are based on intelligent, DNA-programmed soft hybrid materials, and the utilization of evolutionary principles for the optimization of nucleic acid nanocomponents. Amorphous DNA nanotechnology first aims at the creation of cell-sized, DNA-programmed microgels – DNA cells – with sensor, computation, communication, and actuator functions. Interacting DNA cells will be arranged into chemical cell consortia and artificial tissues using microfluidics, micromanipulation and 3D bioprinting techniques. Spatially distributed chemical circuits will then be utilized to establish collective behaviors such as quorum sensing, pattern formation, and self-differentiation within these consortia and tissues. The approach will be further scaled up to produce multicomponent DNA gel compositions that become active and differentiate upon mixing. In evolutionary nanotechnology, techniques derived from directed molecular evolution experiments will be applied to optimize the arrangement of functional nucleic acids on DNA and RNA nanoscaffolds. Compartmentalization and microfluidics will be utilized to screen for nucleic acid nanostructures capable of superstructure formation, and also for the development of ligand-sensitive components for molecular programming. An evolutionary approach will then be applied to amorphous DNA cells, resulting in DNA cell populations which contain individuals with different molecular identities. The proposal will pave the way for the creation of macroscopic materials with DNA-programmed intelligence, resulting in novel applications for DNA nanotechnology and molecular programming in diverse fields such as environmental and biological sensing, biocatalysis, smart adaptive materials, and soft robotics.

2 157 698 €

Start date: 2016-06-01, End date: 2021-05-31

"This proposal aims at strengthening the connections between more fundamentally oriented areas of mathematics like algebra, geometry, analysis, and topology, and the more applied oriented and more recently emerging disciplines of discrete mathematics, optimization, and algorithmics. The overall goal of the project is to obtain, with methods from fundamental mathematics, new effective tools to unravel the complexity of structures like graphs, networks, codes, knots, polynomials, and tensors, and to get a grip on such complex structures by new efficient characterizations, sharper bounds, and faster algorithms. In the last few years, there have been several new developments where methods from representation theory, invariant theory, algebraic geometry, measure theory, functional analysis, and topology found new applications in discrete mathematics and optimization, both theoretically and algorithmically. Among the typical application areas are networks, coding, routing, timetabling, statistical and quantum physics, and computer science. The project focuses in particular on: A. Understanding partition functions with invariant theory and algebraic geometry B. Graph limits, regularity, Hilbert spaces, and low rank approximation of polynomials C. Reducing complexity in optimization by exploiting symmetry with representation theory D. Reducing complexity in discrete optimization by homotopy and cohomology These research modules are interconnected by themes like symmetry, regularity, and complexity, and by common methods from algebra, analysis, geometry, and topology."

2 001 598 €

Start date: 2014-01-01, End date: 2018-12-31

Toroidal processive enzymes (e.g. enzymes/proteins that are able to thread onto biopolymers and to perform stepwise reactions along the polymer chain) are among the most fascinating tools involved in the clockwork machinery of life. Processive catalysis is ubiquitous in Nature, viz. DNA polymerases, endo- and exo-nucleases and; it plays a crucial role in numerous events of the cell’s life, including most of the replication, transmission, and expression and repair processes of the genetic information. In the case of DNA polymerases the protein catalyst encircles the DNA and whilst moving along it, make copies of high fidelity. Although numerous works have been reported in relation with the synthesis of natural enzymes' analogues, very few efforts have been paid in comparison to mimic these processive properties. It is the goal of this proposal to rectify this oversight and unravel the essential components of Nature’s polymer catalysts. The individual projects are designed to specifically target the essential aspects of processive catalysis, i.e. rate of motion, rate of catalysis, and transfer of information. One project is aimed at extending the research into a processive catalytic system that is more suitable for industrial application. Two projects involve more farsighted studies and are designed to push the research way beyond the current boundaries into the area of Turing machines and bio-rotaxane catalysts which can modify DNA in a non-natural process. The vision of this proposal is to open up the field of ‘processive catalysis’ and invigorate the next generation of chemists to develop information transfer and toroidal processive catalysts. The construction of synthetic analogues of processive enzymes could open a gate toward a large range of applications, ranging from intelligent tailoring of polymers to information storage and processing.

1 603 699 €

Start date: 2012-02-01, End date: 2017-01-31

Algebras of operators on Hilbert spaces were originally introduced as the right framework for the mathematical description of quantum mechanics. In modern mathematics the scope has much broadened due to the highly versatile nature of operator algebras. They are particularly useful in the analysis of groups and their actions. Amenability is a finiteness property which occurs in many different contexts and which can be characterised in many different ways. We will analyse amenability in terms of approximation properties, in the frameworks of abstract C*-algebras, of topological dynamical systems, and of discrete groups. Such approximation properties will serve as bridging devices between these setups, and they will be used to systematically recover geometric information about the underlying structures. When passing from groups, and more generally from dynamical systems, to operator algebras, one loses information, but one gains new tools to isolate and analyse pertinent properties of the underlying structure. We will mostly be interested in the topological setting, and in the associated C*-algebras. Amenability of groups or of dynamical systems then translates into the completely positive approximation property. Systems of completely positive approximations store all the essential data about a C*-algebra, and sometimes one can arrange the systems so that one can directly read of such information. For transformation group C*-algebras, one can achieve this by using approximation properties of the underlying dynamics. To some extent one can even go back, and extract dynamical approximation properties from completely positive approximations of the C*-algebra. This interplay between approximation properties in topological dynamics and in noncommutative topology carries a surprisingly rich structure. It connects directly to the heart of the classification problem for nuclear C*-algebras on the one hand, and to central open questions on amenable dynamics on the other.

1 596 017 €

Start date: 2019-10-01, End date: 2024-09-30

Many complex phenomena in the sciences are described by nonlinear partial differential equations, the solutions of which exhibit oscillations and concentration effects on multiple temporal or spatial scales. Our aim is to use methods from applied analysis to contribute to the understanding of the interplay of effects on different scales. The central question is to determine those quantities on the microscale which are needed to for the correct description of the macroscopic evolution. We aim to develop a mathematical framework for analyzing and modeling coupled systems with multiple scales. This will include Hamiltonian dynamics as well as different types of dissipation like gradient flows or rate-independent dynamics. The choice of models will be guided by specific applications in material modeling (e.g., thermoplasticity, pattern formation, porous media) and optoelectronics (pulse interaction, Maxwell-Bloch systems, semiconductors, quantum mechanics). The research will address mathematically fundamental issues like existence and stability of solutions but will mainly be devoted to the modeling of multiscale phenomena in evolution systems. We will focus on systems with geometric structures, where the dynamics is driven by functionals. Thus, we can go much beyond the classical theory of homogenization and singular perturbations. The novel features of our approach are - the combination of different dynamical effects in one framework, - the use of geometric and metric structures for coupled partial differential equations, - the exploitation of Gamma-convergence for evolution systems driven by functionals.

1 390 000 €

Start date: 2011-04-01, End date: 2017-03-31

Angiogenesis research has focused on the sprouting of new capillaries. The mechanisms of vessel maturation are much less well understood. Yet, the maintenance of a mature, quiescent, and organotypically-differentiated layer of endothelial cells (ECs) lining the inside of all blood vessels is vital for human health. The goal of ANGIOMATURE is to identify, validate, and implement novel mechanisms of vascular maturation and organotypic EC differentiation that are active during development, maintenance of vascular stability in adults, and undergo changes in aging. We recently identified previously unrecognized gene expression signatures of vascular maturation in a genome-wide screen of ECs isolated from newborn and adult mice. Epigenetic mechanisms were identified that control the EC transcriptome through gain and loss of DNA methylation as well as EC differentiation and signaling specification. These findings pave the way for groundbreaking novel opportunities to study vascular maturation. By characterizing functionally diverse types of blood vessels, including continuous ECs in lung and brain and sinusoidal ECs in liver and bone marrow, the ANGIOMATURE project will (1) determine up to single cell resolution the transcriptional and epigenetic program(s) of vascular maturation and organotypic differentiation during adolescence, (2) analyze the functional consequences of such program(s) in differentiated ECs and their adaptation to challenge, and (3) study changes of maturation and differentiation program(s) and vascular responses during aging. We will towards this end employ an interdisciplinary matrix of approaches involving omics, systems biology, conditional gene targeting, organoid cell culture, and experimental pathology to create a high-resolution structural and functional organotypic angioarchitectural map. The project will thereby yield transformative mechanistic insights into vital biological processes that are most important for human health and healthy aging.

2 338 918 €

Start date: 2018-08-01, End date: 2023-07-31

The proposal is built on the core idea to use an ensemble of multiple level self-organization processes to create a next generation photocatalytic platform that provides unprecedented property and reactivity control. As a main output, the project will yield a novel highly precise combined catalyst/photocatalyst assembly to: 1) provide a massive step ahead in photocatalytic applications such as direct solar hydrogen generation, pollution degradation (incl. CO2 decomposition), N2 fixation, or photocatalytic organic synthesis. It will drastically enhance efficiency and selectivity of photocatalytic reactions, and enable a high number of organic synthetic reactions to be carried out economically (and ecologically) via combined catalytic/photocatalytic pathways. Even more, it will establish an entirely new generation of “100% depoisoning”, anti-aggregation catalysts with substantially enhanced catalyst life-time. For this, a series of self-assembly processes on the mesoscale will be used to create highly uniform arrays of single-catalyst-particle-in-a-single-TiO2-cavity; target is a 100% reliable placement of a single <10 nm particle in a 10 nm cavity. Thus catalytic features of, for example Pt nanoparticles, can ideally interact with the photocatalytic properties of a TiO2 cavity. The cavity will be optimized for optical and electronic properties by doping and band-gap engineering; the geometry will be tuned to the range of a few nm.. This nanoscopic design yields to a radical change in the controllability of length and time-scales (reactant, charge carrier and ionic transport in the substrate) in combined photocatalytic/catalytic reactions. It is of key importance that all nanoscale assembly principles used in this work are scalable and allow to create square meters of nanoscopically ordered catalyst surfaces. We target to demonstrate the feasibility of the implementation of the nanoscale principles in a prototype macroscopic reactor.

2 427 000 €

Start date: 2014-03-01, End date: 2019-02-28