ERC FUNDED PROJECTS

"Background: Osteoarthritis (OA) is a common musculoskeletal disease occurring worldwide. Despite extensive research, etiology of OA is still poorly understood. Histopathological grading (HPG) of 2D tissue sections is the gold standard reference method for determination of OA stage. However, traditional 2D-HPG is destructive and based only on subjective visual evaluation. These limitations induce bias to clinical in vitro OA diagnostics and basic research that both rely strongly on HPG. Objectives: 1) To establish and validate the very first 3D-HPG of OA based on cutting-edge nano/micro-CT (Computed Tomography) technologies in vitro; 2) To use the established method to clarify the beginning phases of OA; and 3) To validate 3D-HPG of OA for in vivo use. Methods: Several hundreds of human osteochondral samples from patients undergoing total knee arthroplasty will be collected. The samples will be imaged in vitro with nano/micro-CT and clinical high-end extremity CT devices using specific contrast-agents to quantify tissue constituents and structure in 3D in large volume. From this information, a novel 3D-HPG is developed with statistical classification algorithms. Finally, the developed novel 3D-HPG of OA will be applied clinically in vivo. Significance: This is the very first study to establish 3D-HPG of OA pathology in vitro and in vivo. Furthermore, the developed technique hugely improves the understanding of the beginning phases of OA. Ultimately, the study will contribute for improving OA patients’ quality of life by slowing the disease progression, and for providing powerful tools to develop new OA therapies."

1 500 000 €

Start date: 2014-02-01, End date: 2019-01-31

Large protein complexes carry out some of the most complex functions in biology. Such structures are often assembled spontaneously from individual components through the process of self-assembly. If self-assembled protein complexes could be engineered from first principle it would enable a wide range of applications in biomedicine, nanotechnology and materials science. Recently, approaches to rationally design proteins to self-assembly into predefined structures have emerged. The highlight of this work is the design of protein cages that may be engineered into protein containers. However, current approaches for self-assembly design does not result in the assemblies with the required structural complexity to encode many of the sophisticated functions found in nature. To move forward, we have to learn how to engineer protein subunits with more than one designed interface that can assemble into tightly interacting complexes. In this proposal we propose a new protein design paradigm, shape directed protein design, in order to address shortcomings of the current methodology. The proposed method combines geometric shape matching and computational protein design. Using this approach we will de novo design assemblies with a wide variety of structural states, including protein complexes with cyclic and dihedral symmetry as well as icosahedral protein capsids built from novel protein building blocks. To enable these two design challenges we also develop a high-throughput assay to measure assembly stability in vivo that builds on a three-color fluorescent assay. This method will not only facilitate the screening of orders of magnitude more design constructs, but also enable the application of directed evolution to experimentally improve stable and assembly properties of designed containers as well as other designed assemblies.

2 325 292 €

Start date: 2018-06-01, End date: 2023-05-31

The spread of farming practices in various parts of the world had a marked influence on how humans live today and how we are distributed around the globe. Around 10,000 years ago, warmer conditions lead to population increases, coinciding with the invention of farming in several places around the world. Archaeological evidence attest to the spread of these practices to neighboring regions. In many cases this lead to whole continents being converted from hunter-gatherer to farming societies. It is however difficult to see from archaeological records if only the farming culture spread to other places or whether the farming people themselves migrated. Investigating patterns of genetic variation for farming populations and for remaining hunter-gatherer groups can help to resolve questions on population movements co-occurring with the spread of farming practices. It can further shed light on the routes of migration and dates when migrants arrived. The spread of farming to Europe has been thoroughly investigated in the fields of archaeology, linguistics and genetics, while on other continents these events have been less investigated. In Africa, mainly linguistic and archaeological studies have attempted to elucidate the spread of farming and herding practices. I propose to investigate the movement of farmer and pastoral groups in Africa, by typing densely spaced genome-wide variant positions in a large number of African populations. The data will be used to infer how farming and pastoralism was introduced to various regions, where the incoming people originated from and when these (potential) population movements occurred. Through this study, the Holocene history of Africa will be revealed and placed into a global context of migration, mobility and cultural transitions. Additionally the study will give due credence to one of the largest Neolithic expansion events, the Bantu-expansion, which caused a pronounced change in the demographic landscape of the African continent

1 500 000 €

Start date: 2017-11-01, End date: 2022-10-31

Real-world optimization problems pose major challenges to algorithmic research. For instance, (i) many important problems are believed to be intractable (i.e. NP-hard) and (ii) with the growth of data size, modern applications often require a decision making under {\em incomplete and dynamically changing input data}. After several decades of research, central problems in these domains have remained poorly understood (e.g. Is there an asymptotically most efficient binary search trees?) Existing algorithmic techniques either reach their limitation or are inherently tailored to special cases. This project attempts to untangle this gap in the state of the art and seeks new interplay across multiple areas of algorithms, such as approximation algorithms, online algorithms, fixed-parameter tractable (FPT) algorithms, exponential time algorithms, and data structures. We propose new directions from the {\em structural perspectives} that connect the aforementioned algorithmic problems to basic questions in combinatorics. Our approaches fall into one of the three broad schemes: (i) new structural theory, (ii) intermediate problems, and (iii) transfer of techniques. These directions partially build on the PI's successes in resolving more than ten classical problems in this context. Resolving the proposed problems will likely revolutionize our understanding about algorithms and data structures and potentially unify techniques in multiple algorithmic regimes. Any progress is, in fact, already a significant contribution to the algorithms community. We suggest concrete intermediate goals that are of independent interest and have lower risks, so they are suitable for Ph.D students.

1 411 258 €

Start date: 2018-02-01, End date: 2023-01-31