ERC FUNDED PROJECTS

The architecture of DNA in the cell nucleus is an emerging epigenetic key contributor to genome function. We recently developed 4C technology, a high-throughput technique that combines state-of-the-art 3C technology with tailored micro-arrays to uniquely allow for an unbiased genome-wide search for DNA loci that interact in the nuclear space. Based on 4C technology, we were the first to provide a comprehensive overview of long-range DNA contacts of selected loci. The data showed that active and inactive chromatin domains contact many distinct regions within and between chromosomes and genes switch long-range DNA contacts in relation to their expression status. 4C technology not only allows investigating the three-dimensional structure of DNA in the nucleus, it also accurately reconstructs at least 10 megabases of the one-dimensional chromosome sequence map around the target sequence. Changes in this physical map as a result of genomic rearrangements are therefore identified by 4C technology. We recently demonstrated that 4C detects deletions, balanced inversions and translocations in patient samples at a resolution (~7kb) that allowed immediate sequencing of the breakpoints. Excitingly, 4C technology therefore offers the first high-resolution genomic approach that can identify both balanced and unbalanced genomic rearrangements. 4C is expected to become an important tool in clinical diagnosis and prognosis. Key objectives of this proposal are: 1. Explore the functional significance of DNA folding in the nucleus by systematically applying 4C technology to differentially expressed gene loci. 2. Adapt 4C technology such that it allows for massive parallel analysis of DNA interactions between regulatory elements and gene promoters. This method would greatly facilitate the identification of functionally relevant DNA elements in the genome. 3. Develop 4C technology into a clinical diagnostic tool for the accurate detection of balanced and unbalanced rearrangements.

1 225 000 €

Start date: 2008-09-01, End date: 2013-08-31

Making accurate predictions is a crucial factor in many systems (such as in modelling energy consumption, power load forecasting, traffic networks, process industry, environmental modelling, biomedicine, brain-machine interfaces) for cost savings, efficiency, health, safety and organizational purposes. In this proposal we aim at realizing a new generation of more advanced black-box modelling techniques for estimating predictive models from measured data. We will study different optimization modelling frameworks in order to obtain improved black-box modelling approaches. This will be done by specifying models through constrained optimization problems by studying different candidate core models (parametric models, support vector machines and kernel methods) together with additional sets of constraints and regularization mechanisms. Different candidate mathematical frameworks will be considered with models that possess primal and (Lagrange) dual model representations, functional analysis in reproducing kernel Hilbert spaces, operator splitting and optimization in Banach spaces. Several aspects that are relevant to black-box models will be studied including incorporation of prior knowledge, structured dynamical systems, tensorial data representations, interpretability and sparsity, and general purpose optimization algorithms. The methods should be suitable for handling larger data sets and high dimensional input spaces. The final goal is also to realize a next generation software tool (including symbolic generation of models and handling different supervised and unsupervised learning tasks, static and dynamic systems) that can be generically applied to data from different application areas. The proposal A-DATADRIVE-B aims at getting end-users connected to the more advanced methods through a user-friendly data-driven black-box modelling tool. The methods and tool will be tested in connection to several real-life applications.

2 485 800 €

Start date: 2012-04-01, End date: 2017-03-31

I intend to investigate what cognitive mechanisms give us combinatorial speech. Combinatorial speech is the ability to make new words using pre-existing speech sounds. Humans are the only apes that can do this, yet we do not know how our brains do it, nor how exactly we differ from other apes. Using new experimental techniques to study human behavior and new computational techniques to model human cognition, I will find out how we deal with combinatorial speech. The experimental part will study individual and cultural learning. Experimental cultural learning is a new technique that simulates cultural evolution in the laboratory. Two types of cultural learning will be used: iterated learning, which simulates language transfer across generations, and social coordination, which simulates emergence of norms in a language community. Using the two types of cultural learning together with individual learning experiments will help to zero in, from three angles, on how humans deal with combinatorial speech. In addition it will make a methodological contribution by comparing the strengths and weaknesses of the three methods. The computer modeling part will formalize hypotheses about how our brains deal with combinatorial speech. Two models will be built: a high-level model that will establish the basic algorithms with which combinatorial speech is learned and reproduced, and a neural model that will establish in more detail how the algorithms are implemented in the brain. In addition, the models, through increasing understanding of how humans deal with speech, will help bridge the performance gap between human and computer speech recognition. The project will advance science in four ways: it will provide insight into how our unique ability for using combinatorial speech works, it will tell us how this is implemented in the brain, it will extend the novel methodology of experimental cultural learning and it will create new computer models for dealing with human speech.

1 276 620 €

Start date: 2012-02-01, End date: 2017-01-31

My proposal seeks to explain the complex interplay between genetic and environmental causes of individual variation in substance use and the risk for abuse. Substance use is common. Substances like nicotine and cannabis have well-known negative health consequences, while alcohol and caffeine use may be both beneficial and detrimental, depending on quantity and frequency of use. Twin studies (including my own) demonstrated that both heritable and environmental factors play a role. My proposal on substance use (nicotine, alcohol, cannabis and caffeine) is organized around several key objectives: 1. To unravel the complex contribution of genetic and environmental factors to substance use by using extended twin family designs; 2. To identify and confirm genes and gene networks involved in substance use by using DNA-variant data; 3. To explore gene expression patterns with RNA data in substance users versus non-users; 4. To investigate biomarkers in substance users versus non-users using blood or urine; 5. To unravel relation between substance use and health by linking twin-family data to national medical databases. To realize these aims I will use the extensive resources of the Netherlands Twin Register (NTR); including both the longitudinal phenotype database and the biological samples. I have been involved in data collection, coordination of data collection and analyzing NTR data since 1999. With my comprehensive experience in data collection, data analyses and my knowledge in the field of behavior genetics and addiction research I will be able to successfully lead this cutting-edge project. Additional data crucial for the project will be collected by my team. Large samples will be available for this study and state-of-the art methods will be used to analyze the data. All together, my project will offer powerful approaches to unravel the complex interaction between genetic and environmental causes of individual differences in substance use and the risk for abuse.

1 491 964 €

Start date: 2011-12-01, End date: 2017-05-31