ERC FUNDED PROJECTS

Most hereditary diseases in humans are genetically complex, resulting from combinations of mutations in multiple genes. However synthetic interactions between genes are very difficult to identify in population studies because of a lack of statistical power and we fundamentally do not understand how mutations interact to produce phenotypes. C. elegans is a unique animal in which genetic interactions can be rapidly identified in vivo using RNA interference, and we recently used this system to construct the first genetic interaction network for any animal, focused on signal transduction genes. The first objective of this proposal is to extend this work and map a comprehensive genetic interaction network for this model metazoan. This project will provide the first insights into the global properties of animal genetic interaction networks, and a comprehensive view of the functional relationships between genes in an animal. The second objective of the proposal is to use C. elegans to develop and validate experimentally integrated gene networks that connect genes to phenotypes and predict genetic interactions on a genome-wide scale. The methods that we develop and validate in C. elegans will then be applied to predict phenotypes and interactions for human genes. The final objective is to dissect the molecular mechanisms underlying genetic interactions, and to understand how these interactions evolve. The combined aim of these three objectives is to generate a framework for understanding and predicting how mutations interact to produce phenotypes, including in human disease.

1 100 000 €

Start date: 2008-09-01, End date: 2014-04-30

For many years, the ubiquitin-26S proteasome degradation pathway was considered the primary route for proteasomal degradation. However, it is now becoming clear that proteins can also be targeted for degradation by a ubiquitin-independent mechanism mediated by the core 20S proteasome itself. Although initially believed to be limited to rare exceptions, degradation by the 20S proteasome is now understood to have a wide range of substrates, many of which are key regulatory proteins. Despite its importance, little is known about the mechanisms that control 20S proteasomal degradation, unlike the extensive knowledge acquired over the years concerning degradation by the 26S proteasome. Our overall aim is to reveal the multiple regulatory levels that coordinate the 20S proteasome degradation route. To achieve this goal we will carry out a comprehensive research program characterizing three distinct levels of 20S proteasome regulation: Intra-molecular regulation- Revealing the intrinsic molecular switch that activates the latent 20S proteasome. Inter-molecular regulation- Identifying novel proteins that bind the 20S proteasome to regulate its activity and characterizing their mechanism of function. Cellular regulatory networks- Unraveling the cellular cues and multiple pathways that influence 20S proteasome activity using a novel systematic and unbiased screening approach. Our experimental strategy involves the combination of biochemical approaches with native mass spectrometry, cross-linking and fluorescence measurements, complemented by cell biology analyses and high-throughput screening. Such a multidisciplinary approach, integrating in vitro and in vivo findings, will likely provide the much needed knowledge on the 20S proteasome degradation route. When completed, we anticipate that this work will be part of a new paradigm – no longer perceiving the 20S proteasome mediated degradation as a simple and passive event but rather a tightly regulated and coordinated process.

1 500 000 €

Start date: 2015-04-01, End date: 2020-03-31

Despite their amazing success, we believe that computer vision algorithms have only scratched the surface of what can be done in terms of modeling and understanding our world from images. We believe that novel image analysis techniques will be a major enabler and driving force behind next-generation technologies, enhancing everyday life and opening up radically new possibilities. And we believe that the key to achieving this is to develop algorithms for reconstructing and analyzing the 3D structure of our world. In this project, we will focus on three lines of research: A) We will develop algorithms for 3D reconstruction from standard color cameras and from RGB-D cameras. In particular, we will promote real-time-capable direct and dense methods. In contrast to the classical two-stage approach of sparse feature-point based motion estimation and subsequent dense reconstruction, these methods optimally exploit all color information to jointly estimate dense geometry and camera motion. B) We will develop algorithms for 3D shape analysis, including rigid and non-rigid matching, decomposition and interpretation of 3D shapes. We will focus on algorithms which are optimal or near-optimal. One of the major computational challenges lies in generalizing existing 2D shape analysis techniques to shapes in 3D and 4D (temporal evolutions of 3D shape). C) We will develop shape priors for 3D reconstruction. These can be learned from sample shapes or acquired during the reconstruction process. For example, when reconstructing a larger office algorithms may exploit the geometric self-similarity of the scene, storing a model of a chair and its multiple instances only once rather than multiple times. Advancing the state of the art in geometric reconstruction and geometric analysis will have a profound impact well beyond computer vision. We strongly believe that we have the necessary competence to pursue this project. Preliminary results have been well received by the community.

2 000 000 €

Start date: 2015-09-01, End date: 2020-08-31

The world has a significant medical challenge in repairing injured or diseased joints. Joint degeneration and its related pain is a major socio-economic burden that will increase over the next decade and is currently addressed by implanting a metal prosthesis. For the long term, the ideal solution to joint injury is to successfully regenerate rather than replace the damaged cartilage with synthetic implants. Recent advances in key technologies are now bringing this “holy grail” within reach; regenerative approaches, based on cell therapy, are already clinically available albeit only for smaller focal cartilage defects. One of these key technologies is three-dimensional (3D) bio-printing, which provides a greatly controlled placement and organization of living constructs through the layer-by-layer deposition of materials and cells. These tissue constructs can be applied as tissue models for research and screening. However, the lack of biomechanical properties of these tissue constructs has hampered their application to the regeneration of damaged, degenerated or diseased tissue. Having established a cartilage-focussed research laboratory in the University Medical Center Utrecht, I have addressed this biomechanical limitation of hydrogels through the use of hydrogel composites. Specifically, I have pioneered a 3D bio-printing technology that combines accurately printed small diameter thermoplast filaments with cell invasive hydrogels to form strong fibre-reinforced constructs. This, in combination with bioreactor technology, is the key to the generation of larger, complex tissue constructs with cartilage-like biomechanical resilience. With 3D-JOINT I will use my in-depth bio-printing and bioreactor knowledge and experience to develop a multi-phasic 3D-printed biological replacement of the joint.

1 998 871 €

Start date: 2015-07-01, End date: 2020-06-30