ERC FUNDED PROJECTS

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

4D reconstruction, the camera-based dense dynamic scene reconstruction, is a grand challenge in computer graphics and computer vision. Despite great progress, 4D capturing the complex, diverse real world outside a studio is still far from feasible. 4DRepLy builds a new generation of high-fidelity 4D reconstruction (4DRecon) methods. They will be the first to efficiently capture all types of deformable objects (humans and other types) in crowded real world scenes with a single color or depth camera. They capture space-time coherent deforming geometry, motion, high-frequency reflectance and illumination at unprecedented detail, and will be the first to handle difficult occlusions, topology changes and large groups of interacting objects. They automatically adapt to new scene types, yet deliver models with meaningful, interpretable parameters. This requires far reaching contributions: First, we develop groundbreaking new plasticity-enhanced model-based 4D reconstruction methods that automatically adapt to new scenes. Second, we develop radically new machine learning-based dense 4D reconstruction methods. Third, these model- and learning-based methods are combined in two revolutionary new classes of 4DRecon methods: 1) advanced fusion-based methods and 2) methods with deep architectural integration. Both, 1) and 2), are automatically designed in the 4D Real World Reconstruction Loop, a revolutionary new design paradigm in which 4DRecon methods refine and adapt themselves while continuously processing unlabeled real world input. This overcomes the previously unbreakable scalability barrier to real world scene diversity, complexity and generality. This paradigm shift opens up a new research direction in graphics and vision and has far reaching relevance across many scientific fields. It enables new applications of profound social pervasion and significant economic impact, e.g., for visual media and virtual/augmented reality, and for future autonomous and robotic systems.

1 977 000 €

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

One of the most exciting research questions within archaeology is that of the peopling of Australasia by at least c.50,000 years ago. This represents some of the earliest evidence of modern human colonization outside Africa, yet, even at the greatest sea-level lowstand, this migration would have involved seafaring. It is the maritime nature of this dispersal which makes it so important to questions of technological, cognitive and social human development. These issues have traditionally been the preserve of archaeologists, but with a multidisciplinary approach that embraces cutting-edge marine geophysical, hydrodynamic and archaeogenetic analyses, we now have the opportunity to examine the When, Where, Who and How of the earliest seafaring in world history. The voyage from Sunda (South East Asia) to Sahul (Australasia) provides evidence for the earliest ‘open water’ crossing in the world. A combination of the sparse number of early archaeological finds and the significant changes in the palaeolandscape and submergence of the broad north western Australian continental shelf, mean that little is known about the routes taken and what these crossings may have entailed. This project will combine research of the submerged palaeolandscape of the continental shelf to refine our knowledge of the onshore/offshore environment, identify potential submerged prehistoric sites and enhance our understanding of the palaeoshoreline and tidal regime. This will be combined with archaeogenetic research targeting mtDNA and Y-chromosome data to resolve questions of demography and dating. For the first time this project takes a truly multidisciplinary approach to address the colonization of Sahul, providing an unique opportunity to tackle some of the most important questions about human origins, the relationship between humans and the changing environment, population dynamics and migration, seafaring technology, social organisation and cognition.

1 134 928 €

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

In an era of rapid climate change there is a pressing need to understand whether and how organisms are able to adapt to novel environments. Such understanding is hampered by a major divide in the life sciences. Disciplines like systems biology or neurobiology make rapid progress in unravelling the mechanisms underlying the responses of organisms to their environment, but this knowledge is insufficiently integrated in eco-evolutionary theory. Current eco-evolutionary models focus on the response patterns themselves, largely neglecting the structures and mechanisms producing these patterns. Here I propose a new, mechanism-oriented framework that views the architecture of adaptation, rather than the resulting responses, as the primary target of natural selection. I am convinced that this change in perspective will yield fundamentally new insights, necessitating the re-evaluation of many seemingly well-established eco-evolutionary principles. My aim is to develop a comprehensive theory of the eco-evolutionary causes and consequences of the architecture underlying adaptive responses. In three parallel lines of investigation, I will study how architecture is shaped by selection, how evolved response strategies reflect the underlying architecture, and how these responses affect the eco-evolutionary dynamics and the capacity to adapt to novel conditions. All three lines have the potential of making ground-breaking contributions to eco-evolutionary theory, including: the specification of evolutionary tipping points; resolving the puzzle that real organisms evolve much faster than predicted by current theory; a new and general explanation for the evolutionary emergence of individual variation; and a framework for studying the evolution of learning and other general-purpose mechanisms. By making use of concepts from information theory and artificial intelligence, the project will also introduce various methodological innovations.

2 500 000 €

Start date: 2018-12-01, End date: 2023-11-30