ERC FUNDED PROJECTS

The centriole is the largest evolutionary conserved macromolecular structure responsible for building centrosomes and cilia or flagella in many eukaryotes. Centrioles are critical for the proper execution of important biological processes ranging from cell division to cell signaling. Moreover, centriolar defects have been associated to several human pathologies including ciliopathies and cancer. This state of facts emphasizes the importance of understanding centriole biogenesis. The study of centriole formation is a deep-rooted question, however our current knowledge on its molecular organization at high resolution remains fragmented and limited. In particular, exquisite details of the overall molecular architecture of the human centriole and in particular of its central core region are lacking to understand the basis of centriole organization and function. Resolving this important question represents a challenge that needs to be undertaken and will undoubtedly lead to groundbreaking advances. Another important question to tackle next is to develop innovative methods to enable the nanometric molecular mapping of centriolar proteins within distinct architectural elements of the centriole. This missing information will be key to unravel the molecular mechanisms behind centriolar organization. This research proposal aims at building a cartography of the human centriole by elucidating its molecular composition and architecture. To this end, we will combine the use of innovative and multidisciplinary techniques encompassing spatial proteomics, cryo-electron tomography, state-of-the-art microscopy and in vitro assays and to achieve a comprehensive molecular and structural view of the human centriole. All together, we expect that these advances will help understand basic principles underlying centriole and cilia formation as well as might have further relevance for human health.

1 498 965 €

Start date: 2017-01-01, End date: 2021-12-31

Recurrent neural networks (RNNs) are general parallel-sequential computers. Some learn their programs or weights. Our supervised Long Short-Term Memory (LSTM) RNNs were the first to win pattern recognition contests, and recently enabled best known results in speech and handwriting recognition, machine translation, etc. They are now available to billions of users through the world's most valuable public companies including Google and Apple. Nevertheless, in lots of real-world tasks RNNs do not yet live up to their full potential. Although universal in theory, in practice they fail to learn important types of algorithms. This ERC project will go far beyond today's best RNNs through novel RNN-like systems that address some of the biggest open RNN problems and hottest RNN research topics: (1) How can RNNs learn to control (through internal spotlights of attention) separate large short-memory structures such as sub-networks with fast weights, to improve performance on many natural short-term memory-intensive tasks which are currently hard to learn by RNNs, such as answering detailed questions on recently observed videos? (2) How can such RNN-like systems metalearn entire learning algorithms that outperform the original learning algorithms? (3) How to achieve efficient transfer learning from one RNN-learned set of problem-solving programs to new RNN programs solving new tasks? In other words, how can one RNN-like system actively learn to exploit algorithmic information contained in the programs running on another? We will test our systems existing benchmarks, and create new, more challenging multi-task benchmarks. This will be supported by a rather cheap, GPU-based mini-brain for implementing large RNNs.

2 500 000 €

Start date: 2017-10-01, End date: 2022-09-30

Calcium-activated chloride channels (CaCCs) play key roles in a range of physiological processes such as the control of membrane excitability, photoreception and epithelial secretion. Although the importance of these channels has been recognized for more than 30 years their molecular identity remained obscure. The recent discovery of two protein families encoding for CaCCs, Anoctamins and Bestrophins, was a scientific breakthrough that has provided first insight into two novel ion channel architectures. Within this proposal we aim to determine the first high resolution structures of members of both families and study their functional behavior by an interdisciplinary approach combining biochemistry, X-ray crystallography and electrophysiology. The structural investigation of eukaryotic membrane proteins is extremely challenging and will require us to investigate large numbers of candidates to single out family members with superior biochemical properties. During the last year we have made large progress in this direction. By screening numerous eukaryotic Anoctamins and prokaryotic Bestrophins we have identified well-behaved proteins for both families, which were successfully scaled-up and purified. Additional family members will be identified within the course of the project. For these stable proteins we plan to grow crystals diffracting to high resolution and to proceed with structure determination. With first structural information in hand we will perform detailed functional studies using electrophysiology and complementary biophysical techniques to gain mechanistic insight into ion permeation and gating. As the pharmacology of both families is still in its infancy we will in later stages also engage in the identification and characterization of inhibitors and activators of Anoctamins and Bestrophins to open up a field that may ultimately lead to the discovery of novel therapeutic strategies targeting calcium-activated chloride channels.

2 176 000 €

Start date: 2014-02-01, End date: 2020-01-31

"Recent technological evolutions, including the cloud, the multicore, the social and the mobiles ones, are turning computing ubiquitously distributed. Yet, building high-assurance distributed programs is notoriously challenging. One of the main reasons is that these systems usually seek to achieve several goals at the same time. In short, they need to be efficient, responding effectively in various average-case conditions, as well as reliable, behaving correctly in severe, worst-case conditions. As a consequence, they typically intermingle different strategies: each to cope with some specific condition, e.g., with or without node failures, message losses, time-outs, contention, cache misses, over-sizing, malicious attacks, etc. The resulting programs end up hard to design, prove, verify, implement, test and debug. Not surprisingly, there are anecdotal evidences of the fragility of the most celebrated distributed systems. The goal of this project is to contribute to building high-assurance distributed programs by introducing a new dimension for separating and isolating their concerns, as well as a new scheme for composing and reusing them in a modular manner. In short, the project will explore the inherent power and limitations of a novel paradigm, Adversary-Oriented Computing (AOC). Sub-programs, each implementing a specific strategy to cope with a given adversary, modelling a specific working condition, are designed, proved, verified, implemented, tested and debugged independently. They are then composed, possibly dynamically, as black-boxes within the same global program. The AOC project is ambitious and it seeks to fundamentally revisit the way distributed algorithms are designed and distributed systems are implemented. The gain expected in comparison with today's approaches is substantial, and I believe it will be proportional to the degree of difficulty of the distributed problem at hand."

2 147 012 €

Start date: 2014-06-01, End date: 2019-05-31

"The goal of the present proposal is to realize measurements of electronic dynamics in polyatomic molecules with attosecond temporal resolution (1 as = 10^-18s). We propose to study electronic rearrangements following photoexcitation, charge migration in a molecular chain induced by ionization and non-adiabatic multi-electron dynamics in an intense laser field. The grand question addressed by this research is the characterization of electron correlations which control the shape, properties and function of molecules. In all three proposed projects, a time-domain approach appears to be the most suitable since it reduces complex molecular dynamics to the purely electronic dynamics by exploiting the hierarchy of motional time scales. Experimentally, we propose to realize an innovative experimental setup. A few-cycle infrared (IR) pulse will be used to generate attosecond pulses in the extreme-ultraviolet (XUV) by high-harmonic generation. The IR pulse will be separated from the XUV by means of an innovative interferometer. Additionally, it will permit the introduction of a controlled attosecond delay between the two pulses. We propose to use the attosecond pulses as a tool to look inside individual IR- or UV-field cycles to better understand light-matter interactions. Time-resolved pump-probe experiments will be carried out on polyatomic molecules by detecting the energy and angular distribution of photoelectrons in a velocity-map imaging spectrometer. These experiments are expected to provide new insights into the dynamics of multi-electron systems along with new results for the validation and improvement of theoretical models. Multi-electron dynamics is indeed a very complex subject on its own and even more so in the presence of strong laser fields. The proposed experiments directly address theses challenges and are expected to provide new insights that will be beneficial to a wide range of scientific research areas."

1 999 992 €

Start date: 2012-09-01, End date: 2017-08-31

"Many problems in computer science can be cast as state-space search, where the objective is to find a path from an initial state to a goal state in a directed graph called a ""state space"". State-space search is challenging due to the state explosion problem a.k.a. ""curse of dimensionality"": interesting state spaces are often astronomically large, defying brute-force exploration. State-space search has been a core research problem in Artificial Intelligence since its early days and is alive as ever. Every year, a substantial fraction of research published at the ICAPS and SoCS conferences is concerned with state-space search, and the topic is very active at general AI conferences such as IJCAI and AAAI. Algorithms in the A* family, dating back to 1968, are still the go-to approach for state-space search. A* is a graph search algorithm whose only ""intelligence"" stems from a so-called ""heuristic function"", which estimates the distance from a state to the nearest goal state. The efficiency of A* depends on the accuracy of this estimate, and decades of research have pushed the envelope in devising increasingly accurate estimates. In this project, we question the ""A* + distance estimator"" paradigm and explore three new directions that go beyond the classical approach: 1. We propose a new paradigm of declarative heuristics, where heuristic information is not represented as distance estimates, but as properties of solutions amenable to introspection and general reasoning. 2. We suggest moving the burden of creativity away from the human expert by casting heuristic design as a meta-optimization problem that can be solved automatically. 3. We propose abandoning the idea of exploring sequential paths in state spaces, instead transforming state-space search into combinatorial optimization problems with no explicit sequencing aspect. We argue that the ""curse of sequentiality"" is as bad as the curse of dimensionality and must be addressed head-on."

1 997 510 €

Start date: 2019-02-01, End date: 2024-01-31

The goal of this proposal is to fundamentally change the way we build and reason about software. We aim to develop new kinds of statistical programming systems that provide probabilistically likely solutions to tasks that are difficult or impossible to solve with traditional approaches. These statistical programming systems will be based on probabilistic models of massive codebases (also known as ``Big Code'') built via a combination of advanced programming languages and powerful machine learning and natural language processing techniques. To solve a particular challenge, a statistical programming system will query a probabilistic model, compute the most likely predictions, and present those to the developer. Based on probabilistic models of ``Big Code'', we propose to investigate new statistical techniques in the context of three fundamental research directions: i) statistical program synthesis where we develop techniques that automatically synthesize and predict new programs, ii) statistical prediction of program properties where we develop new techniques that can predict important facts (e.g., types) about programs, and iii) statistical translation of programs where we investigate new techniques for statistical translation of programs (e.g., from one programming language to another, or to a natural language). We believe the research direction outlined in this interdisciplinary proposal opens a new and exciting area of computer science. This area will combine sophisticated statistical learning and advanced programming language techniques for building the next-generation statistical programming systems. We expect the results of this proposal to have an immediate impact upon millions of developers worldwide, triggering a paradigm shift in the way tomorrow's software is built, as well as a long-lasting impact on scientific fields such as machine learning, natural language processing, programming languages and software engineering.

1 500 000 €

Start date: 2016-04-01, End date: 2021-03-31

Discovering the causal mechanisms of a complex system of interacting components is necessary in order to control it. Computational Causal Discovery (CD) is a field that offers the potential to discover causal relations under certain conditions from observational data alone or with a limited number of interventions/manipulations. An important, challenging biological problem that may take decades of experimental work is the induction of biological cellular pathways; pathways are informal causal models indispensable in biological research and drug design. Recent exciting advances in flow/mass cytometry biotechnology allow the generation of large-sample datasets containing measurements on single cells, thus setting the problem of pathway learning suitable for CD methods. CAUSALPATH builds upon and further advances recent breakthrough developments in CD methods to enable the induction of biological pathways from cytometry and other omics data. As a testbed problem we focus on the differentiation of human T-cells; these are involved in autoimmune and inflammatory diseases, as well as cancer and thus, are targets of new drug development for a range of chronic diseases. The biological problem acts as our campus for general novel formalisms, practical algorithms, and useful tools development, pointing to fundamental CD problems: presence of feedback cycles, presence of latent confounding variables, CD from time-course data, Integrative Causal Analysis (INCA) of heterogeneous datasets and others. Three features complement CAUSALPATH’s approach: (A) methods development will co-evolve with biological wet-lab experiments periodically testing the algorithmic postulates, (B) Open-source tools will be developed for the non-expert, and (C) Commercial exploitation of the results will be sought out. CAUSALPATH brings together an interdisciplinary team, committed to this vision. It builds upon the PI’s group recent important results on INCA algorithms.

1 724 000 €

Start date: 2015-01-01, End date: 2019-12-31

Striking morphological transformations are a hallmark of any cell division cycle. During nuclear division chromatin is compacted into distinctive rod-shaped chromatids in preparation of chromosome segregation by the spindle apparatus. Multi-subunit SMC protein complexes and a large number of regulatory factors are at the heart of this elementary process. SMC complexes also play key roles during other aspects of genome function such as the control of gene expression and the repair of damaged DNA. They are thought to act as chromatin linkers with exquisite specificity for certain pairs of DNA fibres. However, the underlying molecular mechanisms are not understood. Active extrusion of DNA loops by the SMC complex has been proposed to be the mechanistic basis for the establishment of long-range, intra-chromatid DNA bridges. Here, I put forward a multi-pronged research programme that aims to elucidate fundamentally conserved features of SMC protein function and action using the prokaryotic SMC condensin complex in Bacillus subtilis as a tractable model system. We will conduct a combined structural, biochemical and cell biology approach (including crystallography, electron paramagnetic resonance, ChIP-Seq and ‘native’ HiC) to uncover how the SMC complex acts at the higher levels of organization of the bacterial chromosome to promote the efficient individualization of sister DNA molecules. We will reveal the molecular and structural bases for the association between the SMC complex and the bacterial chromosome at different stages of the loading reaction – each representing a crucial intermediate in a sophisticated chromosome organization process. For the first time, we will be able to map the paths of chromosomal DNA through an SMC complex. Our in-depth mechanistic insights will likely have implications for the understanding of various pathological conditions and have the potential to contribute to the development of novel antibacterial compounds.

1 999 599 €

Start date: 2017-06-01, End date: 2022-05-31