ERC FUNDED PROJECTS

Our tissues are constantly renewed by stem cells. Over time, stem cells accumulate cellular damage that will compromise renewal and results in aging. As stem cells can divide asymmetrically, segregation of harmful factors to the differentiating daughter cell could be one possible mechanism for slowing damage accumulation in the stem cell. However, current evidence for such mechanisms comes mainly from analogous findings in yeast, and studies have concentrated only on few types of cellular damage. I hypothesize that the chronological age of a subcellular component is a proxy for all the damage it has sustained. In order to secure regeneration, mammalian stem cells may therefore specifically sort old cellular material asymmetrically. To study this, I have developed a novel strategy and tools to address the age-selective segregation of any protein in stem cell division. Using this approach, I have already discovered that stem-like cells of the human mammary epithelium indeed apportion chronologically old mitochondria asymmetrically in cell division, and enrich old mitochondria to the differentiating daughter cell. We will investigate the mechanisms underlying this novel phenomenon, and its relevance for mammalian aging. We will first identify how old and young mitochondria differ, and how stem cells recognize them to facilitate the asymmetric segregation. Next, we will analyze the extent of asymmetric age-selective segregation by targeting several other subcellular compartments in a stem cell division. Finally, we will determine whether the discovered age-selective segregation is a general property of stem cell in vivo, and it's functional relevance for maintenance of stem cells and tissue regeneration. Our discoveries may open new possibilities to target aging associated functional decline by induction of asymmetric age-selective organelle segregation.

1 500 000 €

Start date: 2016-05-01, End date: 2021-04-30

As technologies based on semiconductors and ferromagnets are reaching their limits in computational and memory-storage capabilities, new technologies based on spin are emerging as alternative. Magnetic molecules represent the ultimate small-scale magnetic unit that can be synthesized and processed into a device for spintronics and quantum computing applications but their use is confined to very low temperatures. The grand challenge of this proposal is to design magnetic molecules with long spin lifetime at ambient temperature by tuning the main microscopic interaction responsible for spin relaxation: the spin-phonon coupling. AI-DEMON will address this challenge by developing a novel first-principles and machine-learning computational framework able to cover all the essential aspects of the design of new coordination compounds with tailored properties. AI-DEMON has three main objectives, each one representing a major contribution to the field: i) I will unveil the mechanism of spin-phonon relaxation in magnetic molecules by developing a quantitative first-principles spin relaxation theory, ii) I will efficiently explore the chemical space of magnetic coordination compounds by developing a universal machine-learning model able to predict vibrational and magnetic properties, and iii) I will design molecular prototypes with tailored magnetic and vibrational properties by developing generative machine-learning methods. Preliminary results on spin relaxation theory and machine-learning applied to magnetic properties show great promise and set the cornerstone of the project. The use of novel methodologies, such as machine learning and first-principles spin dynamics, represent a strong disruption in the current approach to theoretical modelling and discovery of new magnetic molecules and will propel the field into a new and modern era. Significant impact beyond the field of molecular magnetism, e.g. bio-inorganic chemistry and solid-state qubits, can also be anticipated.

1 499 786 €

Start date: 2021-01-01, End date: 2025-12-31

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: 2024-01-31

What does the fossil record tell us about the evolution of colour in animals through deep time? Evidence of colour in fossils can inform on the visual signalling strategies used by ancient animals. Research to date often has a narrow focus, lacks a broad phylogenetic and temporal context, and rarely incorporates information on taphonomy. This proposal represents a bold new holistic approach to the study of fossil colour: it will couple powerful imaging- and chemical analytical techniques with a rigorous programme of fossilisation experiments simulating decay, burial, and transport, and analysis of fossils and their sedimentary context, to construct the first robust models for the evolution of colour in animals through deep time. The research will resolve the original integumentary colours of fossil higher vertebrates, and the original colours of fossil hair; the fossil record of non-melanin pigments in feathers and insects; the biological significance of monotonal patterning in fossil insects; and the evolutionary history of scales and 3D photonic crystals in insects. Critically, the research will test, for the first time, whether evidence of fossil colour can solve broader evolutionary questions, e.g. the true affinities of enigmatic Cambrian chordate-like metazoans, and feather-like integumentary filaments in dinosaurs. The proposal entails construction of a dedicated experimental maturation laboratory for simulating the impact of burial on tissues. This laboratory will form the core of the world’s first integrated ‘experimental fossilisation facility’, consolidating the PI’s team as the global hub for fossil colour research. The research team comprises the PI, three postdoctoral researchers, and three PhD students, and will form an extensive research network via collaborations with 13 researchers from Europe and beyond. The project will reach out to diverse scientists and will inspire a positive attitude to science among the general public and policymakers alike.

1 562 000 €

Start date: 2016-01-01, End date: 2021-04-30