| Venue: | Institute of Neurosciences and Biophysics 1, Cellular Biophysics Seminar-Room 249, 1st Floor, Building: 15.1, E-2 |
| Start: | Wednesday, November 14th, 2007 |
| Time: | 13.30 - 15.00 (1st lecture) 15.30 - 17.00 (2nd lecture) |
This lecture serves as an introduction to Monte Carlo and molecular dynamics simulations of classical systems. In the lecture I will discuss some more general concepts used in these type of computer simulations (ensembles, choice of interactions, periodic boundary conditions, minimum image convention) as well as the method specific concepts and algorithms of both techniques (detailed balance, Metropolis algorithm, integrators,....). The techniques will be illustrated by some 'soft-matter' examples.
Polyelectrolytes, such as DNA, are composed of charged groups, which interacted with each other and other charges in a system via the long-range Coulomb interaction. This long-range interaction requires special attention in computer simulations of periodic systems. In the lecture, a brief description of the implementation of the Coulomb interaction by Ewald summation will be provided. In addition, various examples will be outlined of the particular effects of charge-charge interactions such as counterion condensation and polyelectrolyte aggregation.
State of the art NMR experiments consist of multiple building blocks, including excitation, polarization transfer and evolution periods, during which the spin system is evolving under the influence of various interactions, as the external magnetic field, chemical shielding by the surrounding electrons, direct and indirect coupling with other nuclei, and the external radiofrequency field. In NMR-spectroscopy of solid samples, chemical shielding and direct couplings are orientation-dependent, and often averaged by MAS (magic angle spinning), which renders these interactions time-dependent. In this lecture I will outline the quantum mechanical fundamentals underlyng the spin evolution and give some illustrative examples of their use in computer simulations for prediction and analysis of NMR spectra and polarization transfer characterization.
Two major forces by which populations adapt are gain and loss of genes, events that can be inferred from genomic comparisons of close relatives. In order to understand the evolutionary significance of these processes, it is necessary to analyse them in a systemic context. We chose E. coli as our model system, as it is one of the best studied cellular systems, and because of the availability of experimentally verified simulation methods for its metabolic network. How and why did E. coli's metabolic network change in the course of evolution? What forces affected the fixation and integration of new genes? Can we use our knowledge of E. coli to understand the metabolic evolution of its endosymbiotic relatives? How did interactions among proteins affect their potential to be horizontally transferred between species? How were new genes integrated into the regulatory network of E. coli? These are the questions that I will address in my talk.
Colloidal dispersions, polymeric solutions or vesicles in flow are examples of complex systems. In these systems the relevant length scales that describe the dynamics of the solute and the surrounding solvent are generally separated by several orders of magnitude. This has motivated the development of several coarse grained descriptions of the solvent in which irrelevant degrees of freedom are not taken into account. In this lecture, I will introduce the basic idea of some of these simulation techniques, like lattice Boltzmann, dissipative particle dynamics and multiparticle collision dynamics.
Colloidal suspensions consist of mesoscopically small particles immersed in a low-molecular-weight solvent like water. The mesoparticles perform an erratic Brownian motion driven by random kicks caused by the solvent molecules, and by interactions with other surrounding particles. In this lecture, I will explain the essentials of the Brownian Dynamics simulation technique for interacting colloidal particles, a technique widely used to compute transport properties of dense suspensions. In particular, I will address the unusual dynamics of colloidal particles, a dynamics shared by microorganisms like bacteria. Simulation examples that will be discussed are particle mean-squared displacements, diffusion functions and the technologically important phenomena of colloidal shear-thinning and thickening.
BioSoft