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

Many parts of Graph Theory have witnessed a huge growth over the last years, partly because of their relation to Theoretical Computer Science and Statistical Physics. These connections arise because graphs can be used to model many diverse structures. The focus of this proposal is on asymptotic results, i.e. the graphs under consideration are large. This often unveils patterns and connections which remain obscure when considering only small graphs. It also allows for the use of powerful techniques such as probabilistic arguments, which have led to spectacular new developments. In particular, my aim is to make decisive progress on central problems in the following 4 areas: (1) Factorizations: Factorizations of graphs can be viewed as partitions of the edges of a graph into simple regular structures. They have a rich history and arise in many different settings, such as edge-colouring problems, decomposition problems and in information theory. They also have applications to finding good tours for the famous Travelling salesman problem. (2) Hamilton cycles: A Hamilton cycle is a cycle which contains all the vertices of the graph. One of the most fundamental problems in Graph Theory/Theoretical Computer Science is to find conditions which guarantee the existence of a Hamilton cycle in a graph. (3) Embeddings of graphs: This is a natural (but difficult) continuation of the previous question where the aim is to embed more general structures than Hamilton cycles - there has been exciting progress here in recent years which has opened up new avenues. (4) Resilience of graphs: In many cases, it is important to know whether a graph `strongly’ possesses some property, i.e. one cannot destroy the property by changing a few edges. The systematic study of this notion is a new and rapidly growing area. I have developed new methods for deep and long-standing problems in these areas which will certainly lead to further applications elsewhere.

818 414 €

Start date: 2012-12-01, End date: 2018-11-30

According to string theory, coherent sheaves on three-dimensional Calabi-Yau spaces encode fundamental properties of the universe. On the other hand, they have a purely mathematical definition. We will develop and use the new field of categorified Donaldson-Thomas (DT) theory, which counts these objects. Via the powerful perspective of noncommutative algebraic geometry, this theory has found application in recent years in a wide variety of contexts, far from classical algebraic geometry. Categorification has proved tremendously powerful across mathematics, for example the entire subject of algebraic topology was started by the categorification of Betti numbers. The categorification of DT theory leads to the replacement of the numbers of DT theory by vector spaces, of which these numbers are the dimensions. In the area of categorified DT theory we have been able to prove fundamental conjectures upgrading the famous wall crossing formula and integrality conjecture in noncommutative algebraic geometry. The first three projects involve applications of the resulting new subject: 1. Complete the categorification of quantum cluster algebras, proving the strong positivity conjecture. 2. Use cohomological DT theory to prove the outstanding conjectures in the nonabelian Hodge theory of Riemann surfaces, and the subject of Higgs bundles. 3. Prove the comparison conjecture, realising the study of Yangian quantum groups and the geometric representation theory around them as a special case of DT theory. The final objective involves coming full circle, and applying our recent advances in noncommutative DT theory to the original theory that united string theory with algebraic geometry: 4. Develop a generalised theory of categorified DT theory extending our results in noncommutative DT theory, proving the integrality conjecture for categories of coherent sheaves on Calabi-Yau 3-folds.

1 239 435 €

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

During an organism’s development it must integrate internal and external information. An example in plants, whose development stretches across their lifetime, is the coordination between environmental stimuli and endogenous cues on regulating the key hormone gibberellin (GA). The present challenge is to understand how these diverse signals influence GA levels and how GA signalling leads to diverse GA responses. This challenge is deepened by a fundamental problem in hormone research: the specific responses directed by a given hormone often depend on the cell-type, timing, and amount of hormone accumulation, but hormone concentrations are most often assessed at the organism or tissue level. Our approach, based on a novel optogenetic biosensor, GA Perception Sensor 1 (GPS1), brings the goal of high-resolution quantification of GA in vivo within reach. In plants expressing GPS1, we observe gradients of GA in elongating root and shoot tissues. We now aim to understand how a series of independently tunable enzymatic and transport activities combine to articulate the GA gradients that we observe. We further aim to discover the mechanisms by which endogenous and environmental signals regulate these GA enzymes and transporters. Finally, we aim to understand how one of these signals, light, regulates GA patterns to influence dynamic cell growth and organ behavior. Our overarching goal is a systems level understanding of the signal integration upstream and growth programming downstream of GA. The groundbreaking aspect of this proposal is our focus at the cellular level, and we are uniquely positioned to carry out our multidisciplinary aims involving biosensor engineering, innovative imaging, and multiscale modelling. We anticipate that the discoveries stemming from this project will provide the detailed understanding necessary to make strategic interventions into GA dynamic patterning in crop plants for specific improvements in growth, development, and environmental responses.

1 499 616 €

Start date: 2018-01-01, End date: 2022-12-31