ERC FUNDED PROJECTS

Man and man-made electronic systems share the same ecosystem, and yet work radically differently. Human metabolism uses ion gradients across insulated membranes to simultaneously process slow analog chemical reactions and communicate information in multicellular systems via soluble/volatile molecular signals. By contrast, electronic systems use multicore central processing units to control the flow of electrons through insulated metal wires with gigahertz frequency and communicate information across networks via wired/wireless connections. With the advent of the internet of things, networks of interconnected electronic devices will reach the processing complexity of living systems, yet they remain largely incompatible with biological systems. Wearable electronics can profile physical parameters such as steps and heartbeat, and Google’s proposal to develop glucose-monitoring contact lenses has triggered a wave of interest in harnessing the full potential of bioelectronics for medical applications. Yet this vision remains limited to diagnostics. Capitalizing on our mind-controlled and smartphone-adjustable optogenetic drug-dosing devices, ElectroGene will establish the foundations of electrogenetics, the science of creating electro-genetic interfaces that enable direct two-way communication between electronic devices and living cells. ElectroGene consists of three pillars, (i) voltage-triggered gene expression, (ii) genetically programmed electronics and (iii) wireless-powered implants providing closed-loop bioelectronic control, which allow real-time monitoring of metabolic conditions (diagnosis), enable remote-controlled production and dosing of protein therapeutics by implanted designer cells (treatment), and manage closed-loop control between cells and electronics, thus linking diagnosis and therapy to block disease onset (prevention). ElectroGene design principles and devices will be validated in proof-of-concept preclinical studies for the treatment of diabetes.

2 500 000 €

Start date: 2018-11-01, End date: 2023-10-31

H-unique will be the first multimodal automated interrogation of visible hand anatomy, through analysis and interpretation of human variation. It will be an interdisciplinary project, supported by anatomists, anthropologists, geneticists, bioinformaticians, image analysts and computer scientists. We will investigate inherent and acquired variation in search of uniqueness, as the hand retains and displays a multiplicity of anatomical variants formed by different aetiologies (genetics, development, environment, accident etc). Hard biometrics, such as fingerprints, are well understood and some soft biometrics are gaining traction within both biometric and forensic domains (e.g. superficial vein pattern, skin crease pattern, morphometry, scars, tattoos and pigmentation pattern). A combinatorial approach of soft and hard biometrics has not been previously attempted from images of the hand. We will pioneer the development of new methods that will release the full extent of variation locked within the visible anatomy of the human hand and reconstruct its discriminatory profile as a retro-engineered multimodal biometric. A significant step change is required in the science to both reliably and repeatably extract and compare anatomical information from large numbers of images especially when the hand is not in a standard position or when either the resolution or lighting in the image is not ideal. Large datasets are vital for this work to be legally admissible. Through citizen engagement with science, this research will collect images from over 5,000 participants, creating an active, open source, ground-truth dataset. It will examine and address the effects of variable image conditions on data extraction and will design algorithms that permit auto-pattern searching across large numbers of stored images of variable quality. This will provide a major novel breakthrough in the study of anatomical variation, with wide-ranging, interdisciplinary and transdisciplinary impact.

2 495 378 €

Start date: 2019-01-01, End date: 2023-12-31

The IntelliAQ project will develop novel approaches for the analysis and synthesis of global air quality data based on deep neural networks. The foundation of this project is the world’s largest collection of surface air quality measurements, which was recently assembled by the principal investigator and plays a pivotal role in the ongoing first comprehensive Tropospheric Ozone Assessment Report (TOAR). This database will be complemented with data from the world’s leading effort to collect global air pollutant measurements in near realtime and combined with high-resolution geodata, weather information, and satellite retrievals of atmospheric composition in order to characterize individual measurement locations and regional air pollution patterns. State-of-the-art deep learning methods will be applied to this unprecedented dataset in order to 1) fill observation gaps in space and time, 2) provide short-term forecasts of air quality, and 3) assess the quality of air pollutant information from diverse measurements. The combination of diverse data sources is unique, and the project will be the first to apply the full potential of deep neural networks on global air quality data. The achievement of the three IntelliAQ objectives will shift the analysis of global air pollutant observations to a new level and provide a basis for the future development of innovative air quality services with robust scientific underpinning. Due to the heterogeneity of the multivariate data, lack of structure, and generally unknown uncertainty of the input data, the project also poses challenges for existing deep learning methods, and will thus lead to new developments in this field. Direct outcomes of the project will be a substantial improvement of global air quality information including methods to assess the quality of air pollution measurements, and a new data-driven method for forecasting air quality at local scales.

2 498 761 €

Start date: 2018-10-01, End date: 2023-09-30

IRMIDYN will study the dynamics of redox-driven iron mineral transformation processes in soils and sediments and impacts on nutrient and trace element behavior using a novel approach based on enriched stable isotopes (e.g., 57Fe, 33S, 67Zn, 113Cd, 198Hg) in combination with innovative experiments and cutting-edge analytical techniques, most importantly 57Fe Mössbauer and Raman micro-spectroscopy and imaging. The thermodynamic stability and occurrence of iron minerals in sufficiently stable Earth surface environments is fairly well understood and supported by field observations. However, the kinetics of iron mineral recrystallization and transformation processes under rapidly changing redox conditions is far less understood, and has to date mostly been studied in in mixed reactors with pure minerals or sediment slurries, but rarely in-situ in complex soils and sediments. Thus, we do not know if and how fast certain iron mineral recrystallization and transformation processes observed in the laboratory actually occur in soils and sediments, and which environmental factors control the transformation rates and products. Redox-driven iron mineral recrystallization and transformation processes are key to understanding the biogeochemical cycles of C, N, P, S, and many trace elements (e.g., As, Zn, Cd, Hg, U). In face of current global challenges caused by massive anthropogenic changes in biogeochemical cycles of nutrients and toxic elements, it is paramount that we begin to understand and quantify the dynamics of these processes in-situ and learn how we can apply our mechanistic (but often reductionist) knowledge to the natural environment. This project will take a large step toward a better understanding of iron mineral dynamics in redox-affected Earth surface environments, with wide implications in biogeochemistry and other fields including environmental engineering, corrosion sciences, archaeology and cultural heritage sciences, and planetary sciences.

3 154 658 €

Start date: 2018-11-01, End date: 2023-10-31

Proteins are the most versatile and complex smart nanomaterials, forming molecular machines and performing numerous functions from structure building, recognition, catalysis to locomotion. Nature however explored only a tiny fraction of possible protein sequences and structures. Design of proteins with new, in nature unseen shapes and features, offers high rewards for medicine, technology and science. In 2013 my group pioneered the design of a new type of modular coiled-coil protein origami (CCPO) folds. This type of de novo designed proteins are defined by the sequence of coiled-coil (CC) dimer-forming modules that are concatenated by flexible linkers into a single polypeptide chain that self-assembles into a polyhedral cage based on pairwise CC interactions. This is in contrast to naturally evolved proteins where their fold is defined by a compact hydrophobic core. We recently demonstrated the robustness of this strategy by the largest de novo designed single chain protein, construction of tetrahedral, pyramid, trigonal prism and bipyramid cages that self-assemble in vivo. This proposal builds on unique advantages of CCPOs and represents a new frontier of this branch of protein design science. I propose to introduce functional domains into selected positions of CCPO cages, implement new types of building modules that will enable regulated CCPO assembly and disassembly, test new strategies of caging and release of cargo molecules for targeted delivery, design knotted and crosslinked protein cages and introduce toehold displacement for the regulated structural rearrangement of CCPOs required for designed molecular machines, which will be demonstrated on protein nanotweezers. Technology for the positional combinatorial library-based single pot assembly of CCPO genes will provide high throughput of CCPO variants. Project will result in new methodology, understanding of potentials of CCPOs for designed molecular machines and in demonstration of different applications.

2 497 125 €

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

Climatic variations at decadal scales, such as phases of accelerated warming, weak monsoons, or widespread subtropical drought, have profound effects on society and the economy. Understanding such variations requires insights from the past. However, no data sets of past climate are available to study decadal variability of large-scale climate with state-of-the-art diagnostic methods. Currently available data sets are limited to statistical reconstructions of local or regional surface climate. The PALAEO-RA project will produce the first ever comprehensive, 3-dimensional, physically consistent reconstruction of the global climate system at a monthly scale for the past six centuries. This palaeoreanalysis is based on combining information from early instrumental measurements, historical documents (e.g., capitalizing on large amounts of newly available data from China), and proxies (e.g., tree rings) with a large ensemble of climate model simulations. To achieve this novel combination, a completely new data assimilation system for palaeoclimatological data will be developed. The unique data sets produced in this project will become reference data sets for studying past climatic variations (i) for diagnostic studies of interannual-to-decadal variability, (ii) as a benchmark for model simulations and (iii) for climate impact studies. Using the data produced, the project will analyse episodes of slowed or accelerated global warming, decadal subtropical drought periods, episodes of expanding or contracting tropics, slowed or strengthened monsoons, changes in storm tracks, blocking and associated weather extremes, and links between Arctic and midlatitude climate. The analyses will provide new insights into the processes governing decadal variability of weather and climate.

2 499 975 €

Start date: 2018-10-01, End date: 2023-09-30

Filamentous plant pathogens (fungi and oomycetes) cause the most devastating crop diseases and thus significantly threaten global food security. Essential components of their virulence arsenal are proteins called cytoplasmic effectors that are delivered inside plant cells to suppress immunity. One of the major scientific challenges in this field is understanding how effectors are secreted and translocated into host cells; a question that is hotly debated. An exciting breakthrough in my laboratory revealed that cytoplasmic effectors accumulate in extracellular vesicles (EVs), implicating this as a delivery route. This critical discovery establishes a vital need to address: • What proteins reside in EVs and how do EVs traffic them between pathogen and host cells? • How are EVs formed and how are effectors packaged into them? • What are the routes for uptake of cytoplasmic effectors into host cells and how do they reach their destination? Each question will be answered by a corresponding workpackage (WP) that brings challenging, innovative approaches to the study of molecular plant pathology. In WP1 proteomics and transgenic approaches will allow the EV proteome to be determined and high-throughput automated electron microscopy will resolve the 3-dimensional organisation of the interface between plant and pathogen. In WP2, new molecular cell biological approaches and genome editing will facilitate an understanding of effector secretory routes and EV biogenesis. In WP3, fusion or endocytosis of EVs with plant cells will be studied and the endocytic routes to delivery of effectors to their final destination will be defined. PathEVome will develop a ground-breaking understanding of effector delivery from filamentous pathogens to the inside of living plant cells. It will provide tools and approaches beyond the current state-of-the-art in infection cell biology that can be broadly adopted to study the roles of vesicular transport in causing disease.

2 468 260 €

Start date: 2018-10-01, End date: 2023-09-30

Seismic tremors form a broad class of signals generated by internal sources that are different from regular earthquakes. Volcanic tremors have been known for a long time, and tectonic tremors associated with seismogenic fault zones have been described more recently. While the physical origin of seismic tremors remains to be fully understood, they are related to slow transient energy release processes that occur in active geological systems during the accumulation of mechanical energy that is then released during catastrophic events, such as strong earthquakes or volcanic eruptions. Therefore, seismic tremors represent a unique source of information that can be used to understand the physics of these ‘preparation’ processes and to design new monitoring and forecasting approaches. Modern digital seismological networks record huge numbers of tremors in different active regions, and breakthroughs can be achieved with systematic exploration of these observations that includes data analysis and physical modeling. My goal is to undertake such an effort via the development of a new unified framework for the study of seismic tremors. I plan to combine advanced methods for data mining, signal processing, and numerical simulations of the generating processes, to apply these to different large datasets of volcanic and tectonic tremors. I will develop an innovative and holistic approach based on massive analysis of observations that requires high performance computing and will be combined with advanced physical modeling of the generating dynamical processes. This will produce the new framework that can be used on the one hand for an understanding of the physical tremor-generating mechanisms, and on other hand for the development of new adaptive methods for monitoring volcanoes and seismic faults. The implementation of these will involve machine learning approaches to gain information from continuous fluxes of data from dense seismological networks.

2 490 000 €

Start date: 2019-01-01, End date: 2023-12-31

Seismic observations imply that slab descent and plume ascent are impeded in the mid-mantle (MM) (depths of 660–1000 km, pressures of 23–40 GPa). A recent evaluation of viscosity variation suggested the presence of a viscosity increase or maximum in the MM that could drag the slab and plume motions. The viscosity variation may be caused by a change in the rheology of bridgmanite (Brg), the dominant mineral in the lower mantle (LM). The absence of seismic anisotropy suggests the dominance of diffusion creep in the majority of the LM. Element diffusivities and grain size are two essential factors of diffusion creep, and defect chemistry controls diffusivity. Hence, this project will determine defect chemistry, diffusivity and the grain growth rate of Brg. Since plume ascent originates in deep parts in the LM, these three properties need to be determined at pressures up to 80 GPa. Although use of a large-volume press (LVP) is vital for obtaining reliable high-pressure experimental data on mineral and rock properties, conventional LVP with carbide anvils can only generate 27 GPa. Recent LVP technology can generate over 100 GPa using sintered diamond (SD) anvils, but the process is currently very difficult for practical use. We developed a method to generate 50 GPa using hard carbide (HWC) anvils that allows practical investigation of Brg properties at mantle temperatures. We will investigate the three properties of Brg up to 50 GPa using LVP with HWC. We will develop LVP technology with SD to reliably generate pressures up to 80 GPa at mantle temperatures, and we will investigate the Brg properties under these conditions. These data will enable numerical modelling of slab and plume dynamics to explain the seismic observations. Through such modelling, we will investigate how materials are transported between the surface and deep mantle reservoirs, which can provide insight into Earth’s evolution and surface habitability.

2 642 120 €

Start date: 2018-10-01, End date: 2023-09-30