ERC FUNDED PROJECTS

I propose to study how eukaryotic cells generate autophagosomes, organelles bounded by a double membrane. These are formed during autophagy and mediate the degradation of cytoplasmic substances within the lysosomal compartment. Autophagy thereby protects the organism from pathological conditions such as neurodegeneration, cancer and infections. Many core factors required for autophagosome formation have been identified but the order in which they act and their mode of action is still unclear. We will use a combination of biochemical and cell biological approaches to elucidate the choreography and mechanism of these core factors. In particular, we will focus on selective autophagy and determine how the autophagic machinery generates an autophagosome that selectively contains the cargo. To this end we will focus on the cytoplasm-to-vacuole-targeting pathway in S. cerevisiae that mediates the constitutive delivery of the prApe1 enzyme into the vacuole. We will use cargo mimetics or prApe1 complexes in combination with purified autophagy proteins and vesicles to reconstitute the process and so determine which factors are both necessary and sufficient for autophagosome formation, as well as elucidating their mechanism of action. In parallel we will study selective autophagosome formation in human cells. This will reveal common principles and special adaptations. In particular, we will use cell lysates from genome-edited cells in combination with purified autophagy proteins to reconstitute selective autophagosome formation around ubiquitin-positive cargo material. The insights and hypotheses obtained from these reconstituted systems will be validated using cell biological approaches. Taken together, our experiments will allow us to delineate the major steps of autophagosome formation during selective autophagy. Our results will yield detailed insights into how cells form and shape organelles in a de novo manner, which is major question in cell- and developmental biology.

1 999 640 €

Start date: 2016-03-01, End date: 2021-02-28

Computational simulations of natural phenomena are essential in science, engineering, product design, architecture, and computer graphics applications. However, despite progress in numerical algorithms and computational power, it is still unfeasible to compute detailed simulations at large scales. To make matters worse, important phenomena like turbulent splashing liquids and fracturing solids rely on delicate coupling between small-scale details and large-scale behavior. Brute-force computation of such phenomena is intractable, and current adaptive techniques are too fragile, too costly, or too crude to capture subtle instabilities at small scales. Increases in computational power and parallel algorithms will improve the situation, but progress will only be incremental until we address the problem at its source. I propose two main approaches to this problem of efficiently simulating large-scale liquid and solid dynamics. My first avenue of research combines numerics and shape: I will investigate a careful de-coupling of dynamics from geometry, allowing essential shape details to be preserved and retrieved without wasting computation. I will also develop methods for merging small-scale analytical solutions with large-scale numerical algorithms. (These ideas show particular promise for phenomena like splashing liquids and fracturing solids, whose small-scale behaviors are poorly captured by standard finite element methods.) My second main research direction is the manipulation of large-scale simulation data: Given the redundant and parallel nature of physics computation, we will drastically speed up computation with novel dimension reduction and data compression approaches. We can also minimize unnecessary computation by re-using existing simulation data. The novel approaches resulting from this work will undoubtedly synergize to enable the simulation and understanding of complicated natural and biological processes that are presently unfeasible to compute.

1 500 000 €

Start date: 2015-03-01, End date: 2020-02-29

Many questions in science, policy making and everyday life are of a causal nature: how would changing A influence B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. The ability to reliably make such causal predictions is of great value for practical applications in a variety of disciplines. Over the last two decades, remarkable progress has been made in the field. However, even though state-of-the-art causal inference algorithms work well on simulated data when all their assumptions are met, there is still a considerable gap between theory and practice. The goal of CAFES is to bridge that gap by developing theory and algorithms that will enable large-scale applications of causal inference in various challenging domains in science, industry and decision making. The key challenge that will be addressed is how to deal with cyclic causal relationships ("feedback loops"). Feedback loops are very common in many domains (e.g., biology, economy and climatology), but have mostly been ignored so far in the field. Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems. Extensions to stationary and non-stationary processes will be developed to advance the state-of-the-art in causal analysis of time-series data. In order to optimally use available resources, computationally efficient and statistically robust algorithms for causal inference from observational and interventional data in the context of confounders and feedback will be developed. The work will be done with a strong focus on applications in molecular biology, one of the most promising areas for automated causal inference from data.

1 405 652 €

Start date: 2015-09-01, End date: 2020-08-31

The basal ganglia consist of a set of neuroanatomical structures that participate in the representation and execution of action sequences. Dopamine neurotransmission in the striatum, the main input nucleus of the basal ganglia, is a fundamental mechanism involved in learning and regulation of such actions. The striatum has multiple functional units, where the limbic striatum is thought to mediate motivational aspects of actions (e.g., goal-directedness) and the sensorimotor striatum their automation (e.g., habit formation). A long-standing question in the field is how limbic and sensorimotor domains communicate with each other, and specifically if they do so during the automation of action sequences. It has been suggested that such coordination is implemented by reciprocal loop connections between striatal projection neurons and the dopaminergic midbrain. Although very influential in theory the effectiveness of this limbic-sensorimotor “bridging” principle has yet to be verified. I hypothesize that during the automation of behaviour regional dopamine signalling is governed by a striatal hierarchy and that dysregulation of this coordination leads to compulsive execution of automatic actions characteristic of several psychiatric disorders. To test this hypothesis, we will conduct electrochemical measurements with real-time resolution simultaneously in limbic and sensorimotor striatum to assess the regional coordination of dopamine release in behaving animals. We developed novel chronically implantable electrodes to enable monitoring of dopamine dynamics throughout the development of habitual behaviour and its compulsive execution in transgenic rats - a species suitable for our complex behavioural assays. Novel rabies virus-mediated gene delivery for in vivo optogenetics in these rats will give us the unique opportunity to test whether specific loop pathways govern striatal dopamine transmission and are causally involved in habit formation and compulsive behaviour.

1 500 000 €

Start date: 2015-05-01, End date: 2020-04-30