ERC FUNDED PROJECTS

Spider silk is Nature’s high performance material that has the potential to revolutionize the materials industry. However, production and spinning of artificial spider silk fibers are challenging, and current methods to produce silk fibers include denaturing conditions which prevent the silk proteins from assembling into fibers in the same complex way as native silk proteins do. In order to fulfill the potential of spider silk we need to increase our understanding of the silk formation process and decipher how protein folding and interactions relate to mechanical properties of the resulting silk fiber. Recent insights into the physiology and molecular mechanisms of the spinning process has made it possible to develop a biomimetic artificial spider silk spinning device (see our publications Andersson et al. Nat Chem Biol. 2017; Otikovs et al. Angew Chemie Int Engl Ed. 2017). We are, for the first time, able to spin artificial silk fibers in which the proteins adopt correct secondary, tertiary and quaternary structures. The overall objective of ARTSILK is to build on these recent technical leaps and use state-of-the-art technologies to generate artificial silk fibers that are equal or superior to native spider silk in terms of toughness and tensile strength. To reach the overall objective we will use the recently mapped spider genome, protein engineering and single cell RNA (ScRNA) sequencing to design novel silk proteins for fiber production. We will also study the relationship between protein secondary structure formation and fiber mechanical properties in order to decipher the ques that determine mechanical properties of the fiber. This knowledge will be important also for the basic understanding of how soluble proteins covert into b-sheet rich fibrils in, e.g., Alzheimer’s disease. Finally, we will use microfluidic chips to engineer the next generation spinning device and 3D-printing techniques to make reproducible three-dimensional structures of spider silk.

2 000 000 €

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

Internal models are fundamental to our understanding of how the mind constructs percepts, makes decisions, controls movements, and interacts with others. Yet, we lack principled quantitative methods to systematically estimate internal models from observable behaviour, and current approaches for discovering their mental representations remain heuristic and piecemeal. I propose to develop a set of novel 'doubly Bayesian' data analytical methods, using state-of-the-art Bayesian statistical and machine learning techniques to infer humans' internal models formalised as prior distributions in Bayesian models of cognition. This approach, cognitive tomography, takes a series of behavioural observations, each of which in itself may have very limited information content, and accumulates a detailed reconstruction of the internal model based on these observations. I also propose a set of stringent, quantifiable criteria which will be systematically applied at each step of the proposed work to rigorously assess the success of our approach. These methodological advances will allow us to track how the structured, task-general internal models that are so fundamental to humans' superior cognitive abilities, change over time as a result of decay, interference, and learning. We will apply cognitive tomography to a variety of experimental data sets, collected by our collaborators, in paradigms ranging from perceptual learning, through visual and motor structure learning, to social and concept learning. These analyses will allow us to conclusively and quantitatively test our central hypothesis that, rather than simply changing along a single 'memory strength' dimension, internal models typically change via complex and consistent patterns of transformations along multiple dimensions simultaneously. To facilitate the widespread use of our methods, we will release and support off-the-shelf usable implementations of our algorithms together with synthetic and real test data sets.

1 179 462 €

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

Computing is changing from living on our desktops and in dedicated devices to being everywhere. In phones, sensors, appliances, and robots – computers (from now on devices) are everywhere and affecting all aspects of our lives. The techniques to make them safe and reliable are investigated and are starting to emerge and consolidate. However, these techniques enable devices to work in isolation or co-exist. We currently do not have techniques that enable development of real autonomous collaboration between devices. Such techniques will revolutionize all usage of devices and, as consequence, our lives. Manufacturing, supply chain, transportation, infrastructures, and earth- and space exploration would all transform using techniques that enable development of collaborating devices. When considering isolated (and co-existing) devices, reactive synthesis – automatic production of plans from high level specification – is emerging as a viable tool for the development of robots and reactive software. This is especially important in the context of safety-critical systems, where assurances are required and systems need to have guarantees on performance. The techniques that are developed today to support robust, assured, reliable, and adaptive devices rely on a major change in focus of reactive synthesis. The revolution of correct-by-construction systems from specifications is occurring and is being pushed forward. However, to take this approach forward to work also for real collaboration between devices the theoretical frameworks that will enable distributed synthesis are required. Such foundations will enable the correct-by-construction revolution to unleash its potential and allow a multiplicative increase of utility by cooperative computation. d-SynMA will take distributed synthesis to this new frontier by considering novel interaction and communication concepts that would create an adaptable framework of correct-by-construction application of collaborating devices.

1 871 272 €

Start date: 2018-05-01, End date: 2023-04-30

Mitochondria are required to convert food into usable energy forms and every cell contains thousands of them. Unlike most other cellular compartments, mitochondria have their own genomes (mtDNA) that encode for 13 of the about 90 proteins present in the respiratory chain. All proteins necessary for mtDNA replication, as well as transcription and translation of mtDNA-encoded genes, are encoded in the nucleus. Mutations in nuclear-encoded proteins required for mtDNA maintenance is an important cause of neurodegeneration and muscle diseases. The common result of these defects is either mtDNA depletion or accumulation of multiple deletions of mtDNA in postmitotic tissues. The long-term goal (or vision) of research in my laboratory is to understand in molecular detail how mtDNA is replicated and how this process is regulated in mammalian cells. To this end we use a protein biochemistry approach, which we combine with in vivo verification in cell lines. My group was in 2004 the first to reconstitute mtDNA replication in vitro and we have continued to develop even more elaborate system ever since. In the current application, the major focus is studies of the mitochondrial D-loop region, a triple-stranded structure in the mitochondrial genome. The D-loop functions as a regulatory hub and we will determine how initiation and termination of mtDNA replication is controlled from this region. We will also determine the physical organization of the mtDNA replication machinery at the replication fork and establish how mtDNA deletions, a classical hallmark of human ageing, are formed.

1 999 985 €

Start date: 2016-11-01, End date: 2021-10-31