# Seminare 2011

**Seminar 1: Nucleation in small systems: analyzing mechanism and kinetics of phase transitions with transition path sampling**

Prof. C. Dellago, Computational Physics, University of Vienna (A)

Under suitable conditions, first order phase transitions such as the freezing of a liquid or the structural transformation of a solid occur via a nucleation and growth mechanism, in which a nucleus of the stable phase forms in the metastable phase. For systems with sizes in the nanometer regime, the nucleation mechanism and its kinetics are strongly affected by finite size effects. I will address this issue using the Wurtzite-to-rocksalt transition in CdSe nanocrystals as illustrative example. In this system, studied experimentally by Alivisatos and coworkers, the activation enthalpy determined from the temperature dependence of the transition rate constant scales linearly with the size of the crystal. Based on the results of transition path sampling simulations, used here to overcome the problem of widely disparate time scales, we provide an explanation for this observation and relate it to the particular structure of the critical nuclei. The role of the reaction coordinate in the investigations of the transition mechanism is discussed.

Web: http://comp-phys.univie.ac.at/Dellago

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**Seminar 2: Wavelet Boundary Element Methods for the Polarizable Continuum Model**

Prof. H. Harbrecht, Mathematical Institute, University of Basel

The present talk is concerned with the rapid solution of boundary integral equations which arise from solvation continuum models. We apply a fully discrete wavelet Galerkin scheme for the computation of the apparent surface charge on solvent accessible and solvent excluded surfaces. This scheme requires parametric surfaces. We therefore developed a mesh generator which automatically constructs a parametrization of the molecular surface by four-sided patches. Numerical results are presented which demonstrate the feasibility and scope of our approach.

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**Seminar 3: Going to greater lengths: large-scale quantum simulations with**

linear-scaling density-functional theory

linear-scaling density-functional theory

Prof. P. Haynes, Imperial College London, UK

First-principles simulations attempt to describe the behaviour of materials by solving the quantum-mechanical Schroedinger equation from scratch. Since no prior assumptions are made, it is now possible to predict the properties of materials under extreme conditions, or to design new materials to order. One approach that has been remarkably successful is density-functional theory (DFT), which balances relatively low computational cost with a sufficiently accurate treatement of electronic correlation for many purposes. However even with DFT the computational cost scales as the cube of the number of atoms in the simulation, and limits routine calculations to a few hundred atoms. This talk will introduce a new linear-scaling approach that extends the scope and scale of DFT calculations to tens of thousands of atoms. The talk will highlight recent work in two areas: application to the simulation of entire polar semiconducting nanorods to identify the factors that determine the charge distribution relevant to optoelectronic properties and self-assembly, and the development of linear-scaling methods for the calculation of optical spectra.

Web: http://www.cmth.ph.ic.ac.uk/people/p.haynes/

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**Seminar 4: A computational study of electro-mechanical interactions in the infarct injured heart**** **

Prof. J. Sundnes, Assistant Director Research at Simula Research Laboratory AS, Norway

The rhythmic contraction of the heart is regulated by a close coupling of electrical and mechanical processes. The most obvious coupling is known as excitation coontraction coupling (ECC), which describes the electrical signal triggering contraction of the muscle cells. Less obvious, but also of fundamental importance, is the fact that mechanical state of the cells and tissue will affect their electrophysiology, an effect commonly referred to as mechano-electric feedback (MEF). Although the role of MEF is not fully understood, it is believed to play an important regulatory function in the healthy heart, but also to amplify electrical heterogeneities in the infarct injured heart. The latter is a potential source of fatal arrhythmias in chronic infarction patients.

Computer simulations stand out as a promising path towards increased understanding of electro-mechanical interactions in the heart, and in particular to fully uncover the role of MEF in post infarct arrhythmias. However, the simulations are challenging to perform because of the complexity and strong non-linearity of the relevant mathematical models. In this talk we will address these computational issues, and propose a set of solution methods based on operator splitting techniques. The derived computational methods will then be applied to a model of infarct injured sheep hearts, to study the effect of MEF in the border zone surrounding the infarcted region.

Web: http://www.mn.uio.no/ifi/english/people/aca/sundnes/index.html

Web: http://simula.no/

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**Seminar 5: Revealing the role of T cells for antibody optimisation by multi-photon imaging and mathematical modelling**

Prof. M. Meyer-Hermann, Head of Department Systems Immunology, Helmholtz Centre for Infection Research (HZI), Braunschweig (D)

This seminar was cancelled on short note.

Web: http://www.systems-immunology.de

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**Seminar 6: 50 Years of General Problem Solving: Are We Getting There?**

Prof. M. Helmert, Computer Science Dept., University of Basel

Automated action planning is one of the classic research areas in artificial intelligence, dating back to the work by Newell and Simon on the General Problem Solver in 1959. Since then, and especially since the emergence of efficient planning systems based on heuristic search around the turn of the millenium, the state of the art for action planning has improved tremendously. Planning systems have achieved a level of maturity that makes them useful for practical applications, and the field continues to progress rapidly both in terms of scalable algorithms and in terms of theoretically understanding the possibilities and limitations of automated planning systems.

In my talk, I will introduce the problem of automated planning, describe state-of-the-art approaches for solving it, and outline some research challenges that our group at the University of Basel is hoping to address over the next five years.

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**Seminar 7: CellOrganizer: Image-derived Models of Subcellular Organization over Time and Space**

Prof. R. F. Murphy, Carnegie Mellon University (USA); and Freiburg Institute for Advanced Studies, Albert Ludwig University, Freiburg (D)

The CellOrganizer project provides tools for learning generative models of cell organization directly from images, storing and retrieving those models in XML files, and synthesizing cell images (or other representations) from one or more models. Model learning captures variation among cells in a collection of images. Images used for model learning and instances synthesized from models can be two- or three dimensional static images or movies. Current components of CellOrganizer can learn models of cell shape, nuclear shape, chromatin texture, vesicular organelle size, shape and position, and microtubule distribution. These models can be conditional upon each other: for example, for a given synthesized cell instance, organelle position is dependent upon the cell and nuclear shape of that instance. CellOrganizer has been applied to include human HeLa cells, mouse NIH 3T3 cells, and Arabidopsis protoplasts. Major advantages of the generative model approach are that models learned from separate experiments can be combined into one synthetic cell instance, and that results from different microscope systems and different experimental conditions can be compared through the framework of the generative model parameters that describe them. This will be especially important for integrating results from diverse high content analysis and screening systems. The generative model framework, combined with active machine learning methods, provides a framework for exploring a very large perturbagen space to find potential therapeutics with high desired activity on a specific target while minimizing activity on other targets.