ERC FUNDED PROJECTS

Molecular structure elucidation is of great importance in synthetic chemistry, pharmacy, life sciences, energy and environmental sciences, and technology applications. To date structure elucidation by atomic force microscopy (AFM) has been demonstrated for a few, small and mainly planar molecules. In this project high-risk, high-impact scientific questions will be solved using structure elucidation with the AFM employing a novel tool and novel methodologies. A combined low-temperature scanning tunneling microscope/atomic force microscope (LT-STM/AFM) with high throughput and in situ electrospray deposition method will be developed. Chemical resolution will be achieved by novel measurement techniques, in particular the usage of different and novel tip functionalizations and combination with Kelvin probe force microscopy. Elements will be identified using substructure recognition provided by a database that will be erected and by refined theory and simulations. The developed tools and techniques will be applied to molecules of increasing fragility, complexity, size, and three-dimensionality. In particular samples that are challenging to characterize with conventional methods will be studied. Complex molecular mixtures will be investigated molecule-by-molecule taking advantage of the single-molecule sensitivity. The absolute stereochemistry of molecules will be determined, resolving molecules with multiple stereocenters. The operation of single molecular machines as nanocars and molecular gears will be investigated. Reactive intermediates generated with atomic manipulation will be characterized and their on-surface reactivity will be studied by AFM.

2 000 000 €

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

"Many problems in computer science can be cast as state-space search, where the objective is to find a path from an initial state to a goal state in a directed graph called a ""state space"". State-space search is challenging due to the state explosion problem a.k.a. ""curse of dimensionality"": interesting state spaces are often astronomically large, defying brute-force exploration. State-space search has been a core research problem in Artificial Intelligence since its early days and is alive as ever. Every year, a substantial fraction of research published at the ICAPS and SoCS conferences is concerned with state-space search, and the topic is very active at general AI conferences such as IJCAI and AAAI. Algorithms in the A* family, dating back to 1968, are still the go-to approach for state-space search. A* is a graph search algorithm whose only ""intelligence"" stems from a so-called ""heuristic function"", which estimates the distance from a state to the nearest goal state. The efficiency of A* depends on the accuracy of this estimate, and decades of research have pushed the envelope in devising increasingly accurate estimates. In this project, we question the ""A* + distance estimator"" paradigm and explore three new directions that go beyond the classical approach: 1. We propose a new paradigm of declarative heuristics, where heuristic information is not represented as distance estimates, but as properties of solutions amenable to introspection and general reasoning. 2. We suggest moving the burden of creativity away from the human expert by casting heuristic design as a meta-optimization problem that can be solved automatically. 3. We propose abandoning the idea of exploring sequential paths in state spaces, instead transforming state-space search into combinatorial optimization problems with no explicit sequencing aspect. We argue that the ""curse of sequentiality"" is as bad as the curse of dimensionality and must be addressed head-on."

1 997 510 €

Start date: 2019-02-01, End date: 2024-01-31

Meltwater running off the flanks of the Greenland ice sheet contributes roughly 60% to its mass loss, the rest being due to calving. Only meltwater originating from below the elevation of the runoff limit leaves the ice sheet, contributing to mass loss; melt at higher elevations refreezes in the porous firn and does not drive mass loss. Therefore any shift in the runoff limit modifies mass loss and subsequent sea level rise. New evidence shows surface runoff at increasingly high elevations, outpacing the rate at which the equilibrium line elevation rises. This research proposal focuses on the runoff limit as a powerful yet poorly understood modulator of Greenland mass balance. We will track the runoff limit over the full satellite era using two of the largest and oldest remote sensing archives, Landsat and the Advanced Very High Resolution Radiometer (AVHRR). We will establish time series of the runoff limit for all regions of Greenland to identify the mechanisms driving fluctuations in the runoff limit. This newly gained process understanding and a wealth of in-situ measurements will then be used to build firn hydrology models capable of simulating runoff and the associated runoff limit over time. Eventually, the firn hydrology models will be applied to reconcile estimates of Greenland past, present and future mass balance. Covering the entire satellite era and all of Greenland, the focus on the runoff limit will constitute a paradigm shift leading to major advance in our understanding of how vulnerable the surface of the ice sheet reacts to climate change and how the changing surface impacts runoff and thus Greenland's role in the global sea level budget.

1 989 181 €

Start date: 2019-05-01, End date: 2024-04-30

Transient multivalent interactions are critical for biological processes such as signaling pathways controlling chromatin function. Chromatin, the nucleoprotein complex organizing the genome, is dynamically regulated by post-translational modifications (PTMs) of the chromatin fiber. Protein effectors interact with combinations of these PTMs through multivalent interactions, deposit novel PTMs, thereby propagate signaling cascades and remodel chromatin structure. To reveal the underlying molecular mechanisms, methods outside classical biochemistry are required, in particular due to the combinational complexity of chromatin PTMs and the transient supramolecular interactions crucial for their recognition. Here, we develop a novel approach, where we synthesize arrays of chemically defined designer chromatin fibers and use dynamic multiplex single-molecule imaging to dissect multivalent signaling processes in chromatin. Our studies target a key pathway, the DNA damage response (DDR), which regulates DNA repair processes central to cell survival and is critically implicated in cancer. Detailed knowledge is of utmost importance to develop targeted therapeutic interventions. We thus employ advanced peptide and protein chemistry to generate libraries of chromatin fibers of a defined PTM state that is encoded in the chromatin DNA. With the library immobilized in a flow cell, we use single-molecule detection to directly observe signaling processes by key DDR effectors in real time. Subsequent in situ polony decoding allows the identification of each chromatin fiber’s modification state, enabling broad sampling of signaling outcomes. Finally, we use dynamic computational models to integrate the effector-chromatin interaction network and test key mechanisms in cancer-based cell culture. Together, these methods will yield fundamental insight into chromatin and DDR signaling and will be of broad use for chemical and biomedical research with applications beyond the chromatin field.

1 999 815 €

Start date: 2017-05-01, End date: 2022-04-30