ERC FUNDED PROJECTS

According to the World Health Organization more than 285 million people worldwide are visually impaired. In a world where graphics and online content (images, webpages) become increasingly important the inability to perceive information visually is the primary inhibitor for inclusion. In contrast to display technology for sighted people, tactile displays which translate text and graphics to touchable pixels (taxels) have seen little progress in recent decades. So-called Braille lines which display only a single line of text are still the norm. The reason why graphical tactile displays do not exist is the lack of a suitable actuator technology which allows generating massively parallelized individually addressable cost-effective taxel arrays. This ERC Consolidator project aims at a revolution in microactuator array technology with a fundamentally new concept termed the Capillary Lock Actuator (CaLA). CaLA is a novel bistable massively parallelizable microfluidic microactuator which overcomes many of the limitations currently associated with microactuators. It can be operated with low-voltage control signals and requires virtually no power for actuation. CaLA harnesses three concepts inherent to microfluidics: positive capillary pressure, segmented flow and controllable locally confined changes in wetting. The project will use CaLA actuator arrays for setting up the very first portable tactile graphic display with 30.000 individually addressable taxels thereby significantly outperforming the state-of-the-art. It will be based on manufacturing techniques for highly complex microstructures in glass invented by my group. CaLA will be a significant breakthrough in actuator technology and enabling for many applications in microsystem technology. Most importantly, it will be a significant step towards making the information technology inclusive for the visually impaired by providing the first robust cost-effective solution to large-scale tactile displays.

1 999 750 €

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

My vision is to address a clinical problem with a novel and transformative approach. Using my unique expertise in cell biology, tissue engineering and translational research I will design technology platforms to test new treatments for human pancreatic cancer. Pancreatic tumours are cancers of unmet medical need with 85% of patients dying within 9 months of diagnosis. To find better therapies, we need patient-specific models that mimic the biology of tumour tissues and target interactions between malignant and non-malignant cells. Biomimetic tissue engineering is a powerful approach to generate 3D cancer models, however, only a few scientists use these technologies. Most 3D cultures of human cells include reconstituted matrices that originate from murine tumours containing undefined amounts of extracellular matrix and growth factors. There is no tissue-engineered 3D model that allows control over patient-specific and biomechanical characteristics of the pancreatic tumour microenvironment. I hypothesise that 3D approaches that replicate the native tissue composition and biomechanical properties will behave like real tumours to provide clinically predictive platforms and to test novel treatments that target both malignant and non-malignant cells. To test my hypothesis, I will: •3D-print matrix and cellular elements of the microenvironment of human pancreatic tumours •Develop a cancer-on-a-chip model of liver metastasis •Compare the crosstalk of malignant and other microenvironment components with the human disease •Validate my new platforms with treatments in clinical trials and test novel combination treatments that slow down or reduce tumour growth. In a multidisciplinary project, I will use: •3D printing to build platforms composed of hydrogels, fibrous scaffolds and patient-derived cells •Extracellular matrix molecules for chemical crosslinking into hydrogels •Cancer-on-a-chip models to study tumour metastasis •Imaging, biomechanical and multi-omics analyses.

2 000 000 €

Start date: 2021-04-01, End date: 2026-03-31

Abiotic stress is a major threat to global crop yields and this problem is likely to be exacerbated in the future. Therefore, it is very important to engineer crop plants with improved stress tolerance. A large body of research has focussed on the immediate stress responses. However, in nature stress is frequently chronic or recurring, suggesting that temporal dynamics are an important, but under-researched, component of plant stress responses. Indeed, plants can be primed by a stress exposure such that they respond more efficiently to the next stress incident. Such stress priming and memory may be particularly beneficial to plants due to their sessile life style. Typically, the memory of priming lasts for several days after the end of the stress. During the past few years, my group has initiated a molecular analysis of heat stress memory in Arabidopsis thaliana. Heat stress memory is associated with sustained gene induction and transcriptional memory and we have demonstrated that this involves lasting chromatin changes. The underlying molecular mechanisms, however, remain poorly understood. Here, I propose to combine mechanistic dissection of heat stress memory in A. thaliana with concomitant translation of the results into the temperate cereal crop barley. In particular, we will study the following questions: What is the role of chromatin during heat stress memory? How do the transcription factors involved mediate memory-specific outputs? How does nucleosome positioning affect heat stress memory? How do histone modifications during stress memory interact with transcription, chromatin and nuclear organization? Is heat stress memory conserved in temperate cereal species? Can we engineer plants with improved stress memory? Using existing tools and new methodologies, the proposed analyses will yield unprecedented insight into the long-term adaptation of plants to abiotic stress and open up approaches for breeding of stress-tolerant crops.

1 998 525 €

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

Every year chronic infections in patients due to biofilm formation of pathogenic bacteria are a multi-billion Euro burden to national healthcare systems. Despite improvements in technology and medical services, morbidity and mortality due to chronic infections have remained unchanged over the past decades. The emergence of a chronic infection disease burden calls for the development of modern diagnostics for biofilm resistance profiling and new therapeutic strategies to eradicate biofilm-associated infections. However, many unsuccessful attempts to address this need teach us that alternative perspectives are needed to meet the challenges. The project is committed to develop innovative diagnostics and to strive for therapeutic solutions in patients suffering from biofilm-associated infections. The objective is to apply data-driven science to unlock the potential of microbial genomics. This new approach uses tools of advanced microbiological genomics and machine learning in genome-wide association studies on an existing unprecedentedly large dataset. This dataset has been generated in my group within the last five years and comprises sequence variation and gene expression information of a plethora of clinical Pseudomonas aeruginosa isolates. The wealth of patterns and characteristics of biofilm resistance are invisible at a smaller scale and will be interpreted within context and domain-specific knowledge. The unique combination of basic molecular biology research, technology-driven approaches and data-driven science allows pioneer research dedicated to advance strategies to combat biofilm-associated infections. My approach does not only provide a prediction of biofilm resistance based on the bacteria´s genotype but also holds promise to transform treatment paradigms for the management of chronic infections and by interference with bacterial stress responses will promote the effectiveness of antimicrobials in clinical use to eradicate biofilm infections.

1 998 750 €

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