ERC FUNDED PROJECTS

My objective is to understand the physiologic and medical importance of the posttranslational processing of CAAX proteins (e.g., K-RAS and prelamin A) and to define the suitability of the CAAX protein processing enzymes as therapeutic targets for the treatment of cancer and progeria. CAAX proteins undergo three posttranslational processing steps at a carboxyl-terminal CAAX motif. These processing steps, which are mediated by four different enzymes (FTase, GGTase-I, RCE1, and ICMT), increase the hydrophobicity of the carboxyl terminus of the protein and thereby facilitate interactions with membrane surfaces. Somatic mutations in K-RAS deregulate cell growth and are etiologically involved in the pathogenesis of many forms of cancer. A mutation in prelamin A causes Hutchinson-Gilford progeria syndrome—a pediatric progeroid syndrome associated with misshaped cell nuclei and a host of aging-like disease phenotypes. One strategy to render the mutant K-RAS and prelamin A less harmful is to interfere with their ability to bind to membrane surfaces (e.g., the plasma membrane and the nuclear envelope). This could be accomplished by inhibiting the enzymes that modify the CAAX motif. My Specific Aims are: (1) To define the suitability of the CAAX processing enzymes as therapeutic targets in the treatment of K-RAS-induced lung cancer and leukemia; and (2) To test the hypothesis that inactivation of FTase or ICMT will ameliorate disease phenotypes of progeria. I have developed genetic strategies to produce lung cancer or leukemia in mice by activating an oncogenic K-RAS and simultaneously inactivating different CAAX processing enzymes. I will also inactivate several CAAX processing enzymes in mice with progeria—both before the emergence of phenotypes and after the development of advanced disease phenotypes. These experiments should reveal whether the absence of the different CAAX processing enzymes affects the onset, progression, or regression of cancer and progeria.

1 689 600 €

Start date: 2008-06-01, End date: 2013-05-31

The long-term goal of our research is to advance the state-of-the-art in molecular simulation algorithms by 4-5 orders of magnitude, particularly in the context of the GROMACS software we are developing. This is an immense challenge, but with huge potential rewards: it will be an amazing virtual microscope for basic chemistry, polymer and material science research; it could help us understand the molecular basis of diseases such as Creutzfeldt-Jacob, and it would enable rational design rather than random screening for future drugs. To realize it, we will focus on four critical topics: • ALGORITHMS FOR SIMULATION ON GRAPHICS AND OTHER STREAMING PROCESSORS: Graphics cards and the test Intel 80-core chip are not only the most powerful processors available, but this type of streaming architectures will power many supercomputers in 3-5 years, and it is thus critical that we design new “streamable” MD algorithms. • MULTISCALE MODELING: We will develop virtual-site-based methods to bridge atomic and mesoscopic dynamics, QM/MM, and mixed explicit/implicit solvent models with water layers around macromolecules. • MULTI-LEVEL PARALLEL & DISTRIBUTED SIMULATION: Distributed computing provides virtually infinite computer power, but has been limited to small systems. We will address this by combining SMP parallelization and Markov State Models that partition phase space into transition/local dynamics to enable distributed simulation of arbitrary systems. • EFFICIENT FREE ENERGY CALCULATIONS: We will design algorithms for multi-conformational parallel sampling, implement Bennett Acceptance Ratios in Gromacs, correction terms for PME lattice sums, and combine standard force fields with polarization/multipoles, e.g. Amoeba. We have a very strong track record of converting methodological advances into applications, and the results will have impact on a wide range of fields from biomolecules and polymer science through material simulations and nanotechnology.

992 413 €

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

Computer vision concerns itself with understanding the real world through the analysis of images. Typical problems are object recognition, medical image segmentation, geometric reconstruction problems and navigation of autonomous vehicles. Such problems often lead to complicated optimization problems with a mixture of discrete and continuous variables, or even infinite dimensional variables in terms of curves and surfaces. Today, state-of-the-art in solving these problems generally relies on heuristic methods that generate only local optima of various qualities. During the last few years, work by the applicant, co-workers, and others has opened new possibilities. This research project builds on this. We will in this project focus on developing new global optimization methods for computing high-quality solutions for a broad class of problems. A guiding principle will be to relax the original, complicated problem to an approximate, simpler one to which globally optimal solutions can more easily be computed. Technically, this relaxed problem often is convex. A crucial point in this approach is to estimate the quality of the exact solution of the approximate problem compared to the (unknown) global optimum of the original problem. Preliminary results have been well received by the research community and we now wish to extend this work to more difficult and more general problem settings, resulting in thorough re-examination of algorithms used widely in different and trans-disciplinary fields. This project is to be considered as a basic research project with relevance to industry. The expected outcome is new knowledge spread to a wide community through scientific papers published at international journals and conferences as well as publicly available software.

1 440 000 €

Start date: 2008-07-01, End date: 2013-06-30

The objective is to develop a robust multifunctional framework/probe for capturing the evolution of deformation and failure in a variety of processes at the micro-nano transition regime. An interdisciplinary approach will be pursued based on fundamental theory and experiment, in conjunction with multiscale simulations for micro/nanotechnology applications. The approach is unconventional as it ventures to extend continuum mechanics down to the micro/nano regime and verify this through nanoindentation and atomic force microscopy techniques. It is also unique as the new phenomenology introduced for establishing this extension (higher order gradients accounting for microscopic processes and interfacial energy terms accounting for nanoscopic phenomena) will be substantiated through hybrid (ab initio-atomistic-defect-finite element) simulations. The framework will be employed to consider fracture and size effects in a number of micro-nano scale transition configurations ranging from nanograined aggregates and nanolayered structures to nanotubes and micropillars, and from Li-ion battery electrodes to bioactive interfaces. Other micro/nano objects such as quantum dots, nanowires and NEMS/MEMS devices, as well as biomolecular microcrystalline membranes leading to living cell division will be considered. In a sense this “scale” transition theory is reminiscent in scope to Landau’s “phase” transition theory where a variety of different physical phenomena can be treated within a common framework. This optimism stems from the PI’s previous success with this approach, as well as Smalley’s remark that the “laws of continuum mechanics are amazingly robust for treating even intrinsically discrete objects only a few atoms in diameter”. A good mix of young researchers and mature scholars will be employed, thus connecting people and ideas through joint publications and scholarly activities in a critical area of fundamental and applied research.

1 128 400 €

Start date: 2008-10-01, End date: 2013-09-30