ERC FUNDED PROJECTS

Despite considerable advances in drug discovery, resistance to anticancer chemotherapy confounds the effective treatment of patients. Cancer cells can acquire broad cross-resistance to mechanistically and structurally unrelated drugs. P-glycoprotein (Pgp) actively extrudes many types of drugs from cancer cells, thereby conferring resistance to those agents. The central tenet of my work is that Pgp, a universally accepted biomarker of drug resistance, should in addition be considered as a molecular target of multidrug-resistant (MDR) cancer cells. Successful targeting of MDR cells would reduce the tumor burden and would also enable the elimination of ABC transporter-overexpressing cancer stem cells that are responsible for the replenishment of tumors. The proposed project is based on the following observations: - First, by using a pharmacogenomic approach, I have revealed the hidden vulnerability of MDRcells (Szakács et al. 2004, Cancer Cell 6, 129-37); - Second, I have identified a series of MDR-selective compounds with increased toxicity toPgp-expressing cells (Turk et al.,Cancer Res, 2009. 69(21)); - Third, I have shown that MDR-selective compounds can be used to prevent theemergence of MDR (Ludwig, Szakács et al. 2006, Cancer Res 66, 4808-15); - Fourth, we have generated initial pharmacophore models for cytotoxicity and MDR-selectivity (Hall et al. 2009, J Med Chem 52, 3191-3204). I propose a comprehensive series of studies that will address thefollowing critical questions: - First, what is the scope of MDR-selective compounds? - Second, what is their mechanism of action? - Third, what is the optimal therapeutic modality? Extensive biological, pharmacological and bioinformatic analyses will be utilized to address four major specific aims. These aims address basic questions concerning the physiology of MDR ABC transporters in determining the mechanism of action of MDR-selective compounds, setting the stage for a fresh therapeutic approach that may eventually translate into improved patient care.

1 499 640 €

Start date: 2012-01-01, End date: 2016-12-31

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

Vaccines and immunodiagnostics have been vital for public health and medicine, however a quantitative molecular understanding of vaccine-induced antibody responses is lacking. Antibody research is currently going through a big-data driven revolution, largely due to progress in next-generation sequencing (NGS) and bioinformatic analysis of antibody repertoires. A main advantage of high-throughput antibody repertoire analysis is that it provides a wealth of quantitative information not possible with other classical methods of antibody analysis (i.e., serum titers); this information includes: clonal distribution and diversity, somatic hypermutation patterns, and lineage tracing. In preliminary work my group has established standardized methods for antibody repertoire NGS, including an experimental-bioinformatic pipeline for error and bias correction that enables highly accurate repertoire sequencing and analysis. The overall goal of this proposal will be to apply high-throughput antibody repertoire analysis for quantitative vaccine profiling and discovery of next-generation immunodiagnostics. Using mouse subunit vaccination as our model system, we will answer for the first time, a fundamental biological question within the context of antibody responses - what is the link between genotype (antibody repertoire) and phenotype (serum antibodies)? We will expand upon this approach for improved rational vaccine design by quantitatively determining the impact of a comprehensive set of subunit vaccination parameters on complete antibody landscapes. Finally, we will develop advanced bioinformatic methods to discover immunodiagnostics based on antibody repertoire sequences. In summary, this proposal lays the foundation for fundamentally new approaches in the quantitative analysis of antibody responses, which long-term will promote the development of next-generation vaccines and immunodiagnostics.

1 492 586 €

Start date: 2016-06-01, End date: 2021-05-31

"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