Plenarvorträge - DPG-Tagungen

Dynamik und Statistische Physik Dienstag
Coarse grained models are utilized for studying the effect of electrostatics
and hydrophobicity on the aggregation behavior of these molecules.
A phase diagram is constructed for these molecules, which clearly shows
that the interplay of electrostatics and hydrophobicity leads to finite size
aggregates.
DY 23.3 Di 12:15 H3
Fractional diffusion model of ion channel gating — •Igor Goychuk
and Peter Hänggi — Institut für Physik, Universität Augsburg,
Germany
We have put forward a fractional diffusion model of ion channel gating
which is capable to explain the origin of non-exponential distributions of
the residence time intervals as they are observed in several types of ion
channels. The model presents a generalization of the discrete diffusion
model by Millhauser, Salpeter and Oswald [Proc. Natl. Acad. Sci. USA
85, 1503 (1988)] to the case of continuous, anomalously slow conformational
diffusion which is described within the mathematical framework
of the fractional diffusion equation approach. Our model contains three
parameters only: the mean residence time, the conformational diffusion
time and the index of fractional diffusion 0 < α ≤ 1. A tractable analytical
expression for the characteristic function of the residence time
distribution is derived which in the normal diffusion case, α = 1, reduces
to our earlier result in [1]. Our new result captures a description of the
residence time distributions that do exhibit a decaying power law in time
with an (negative) exponent that differs from the normal diffusive behavior;
i.e. a value for the exponent given by 3/2. It is shown that depending
on the parameters of the studied model the residence time distribution
DY 24 Dynamic Instabilities in Biophysics
may exhibit up to three characteristic time-regimes: initially a stretched
exponential and then two different power laws.
[1] I. Goychuk and P. Hänggi, Proc. Natl. Acad. Sci. USA 99, 3552 (2002);
Physica A 325, 9 (2003).
Hauptvortrag DY 23.4 Di 12:30 H3
Statistical Physics of RNA Secondary Structures — •Ralf
Bundschuh — Department of Physics, The Ohio State University. 174
West 18th Avenue, Columbus, Ohio 43210-1106, U.S.A.
In addition to its importance for the biological function of RNA
molecules RNA secondary structure formation is an interesting system
from the statistical physics point of view. The ensemble of secondary
structures of random RNA sequences shows a rich phase diagram with
distinct native, denatured, molten, and glassy phases separated by thermodynamical
phase transitions. These phase transitions are driven by
the competition between thermal fluctuations, the disorder frozen into
the specific sequence of a given RNA molecule, and the evolutionary bias
towards the formation of some biologically relevant structure. Yet, in
contrast to the protein folding problem which is driven by very similar
principles and shows a similar phase diagram RNA secondary structure
formation can be represented by a simple diagrammatic language which
allows the application of various analytical and numerical methods. This
makes RNA secondary structure formation an ideal model system for
heteropolymer folding. In the talk, I will characterize and explain the
complex behaviour of RNA folding using several simple models and discuss
possible implications to biological processes.
Zeit: Dienstag 14:30–16:45 Raum: H2
Hauptvortrag DY 24.1 Di 14:30 H2
Physical Aspects of Cell Division — •Karsten Kruse — Max
Planck Institut für Physik komplexer Systeme, Nöthnitzer Str. 38, 01187
Dresden
Cell division is one of the truly fundamental processes in biology and
consists of a highly controlled sequence of dynamic events. However, essential
features of some of these events can be understood as emerging
from dynamic instabilities. Two systems will be presented to illustrate
this point. In the bacterium Escherichia coli, division occurs at the cell’s
center. Selection of the division site relies on pole-to-pole oscillations of
the proteins MinC, MinD, and MinE. In animal cells, division often occurs
off the center. Asymmetric division is achieved by displacing the
mitotic spindle, a bipolar structure of filamentous proteins. This displacement
is accompanied by oscillations of the spindle poles. For both
systems, the oscillations will be shown to result from dynamical instabilities.
These examples suggest, that self-organization is an essential
principle underlying cell division.
DY 24.2 Di 15:00 H2
Barrier crossing of semiflexible polymers — •Pavel Kraikivski,
Jan Kierfeld, and Reinhard Lipowsky — MPI für Kolloid- und
Grenzflächenforschung, 14424 Potsdam
We study the motion of semiflexible polymers in double-well potentials.
We calculate shape, energy, and effective diffusion constant of kink
excitations, and in particular their dependence on the bending rigidity
of the semiflexible polymer. For symmetric potentials, the kink motion is
purely diffusive whereas kink motion becomes directed in the presence of
a driving force on the polymer. We determine the average velocity of the
semiflexible polymer based on the kink dynamics. The Kramers escape
over the potential barriers proceeds by nucleation and diffusive motion of
kink-antikink pairs, the relaxation to the straight configuration by annihilation
of kink-antikink pairs. Our results apply to the activated motion
of biopolymers such as DNA and actin filaments or synthetic polyelectrolytes
on structured substrates.
DY 24.3 Di 15:15 H2
A Continuum Model for Bacterial Ripple Pattern Formation —
•Uwe Börner and Markus Bär — Max-Planck-Institut für Physik
komplexer Systeme, Nöthnitzer Str. 38, 01187 Dresden
We study pattern forming instabilities in a continuum model of coupled
hypberbolic partial differential equations that describe active motion and
chemical interaction between starving myxobacteria. The decisive param-
eter is a refractory time during which bacteria are unable to respond to
signals by other bacteria. It is shown that if the refractory time crosses
a threshold value, an ensemble of bacteria experiences a density instability
that is in line with the characteristic experimental rippling patterns.
Moreover, we show that this instability is robust against the incorporation
of diffusion and compares well to earlier simulation and mean-field
results in the analogous discrete medium.
DY 24.4 Di 15:30 H2
Dynamics of Neurons with Residual Post-Spike Response to
Synaptic Input Currents — •Christoph Kirst, Marc Timme,
and Theo Geisel — Max-Planck-Institut für Strömungsforschung
and Fakultät für Physik, Universität Göttingen, Bunsenstr. 10, 37073
Göttingen
Since the precise timing of spikes is believed to play an important role
for information processing in the brain, many recent studies have been
devoted to the spiking dynamics of neural networks. The response of a
biological neuron to incoming post-synaptic currents strongly depends
on whether or not it has just emitted an action potential (spike). This
effect, however, has so far been neglected in most theoretical studies of
driven single neurons as well as of neural networks, see e.g. [1,2].
Here we investigate the influence of a reduced post-spike neuronal response
in spike-driven neural oscillators. Intriguingly, we find that the
dynamics of a single neuron changes qualitatively, even if an arbitrarily
small fraction of the post-synaptic current is lost due to spike emission.
Implications for the dynamics of networks of neurons are discussed.
[1] K. Pakdaman, Phys. Rev. E 63:041907 (2001).
[2] P.C. Bressloff and S. Coombes, Neural Comput. 12:91 (2000).
DY 24.5 Di 15:45 H2
What Determines the Speed of Neural Processing? — •Björn
Naundorf, Theo Geisel, and Fred Wolf — Max-Planck Institut
für Strömungsforschung and Fakultät für Physik, Universität Göttingen,
37073 Göttingen, Germany
In a generic neuron model, we present the linear response theory for the
firing rate in response to both time dependent input currents and noise
amplitudes. In both cases the signal transmission is strongly attenuated
for frequencies above the stationary firing rate. For high frequencies both
the mean input and the noise transmission function decay as ω −2 , independent
of model details. Moreover we study a sparse two-population
network consisting of inhibitory and excitatory neurons focusing on the