Claus Metzner @ Biophysics

scope and vision

2015-05-06T14:21:00.063+02:00

Spreading of tumor cells

One of the central goals in our institute is a better understanding of the spreading of cancer. In this often deadly disease, individual cancer cells are migrating through the body to form secondary tumors in distant organs. For a better treatment of cancer, it is crucial to have a detailed model about how these cells are crawling across the dense bio-materials that comprise most of the body's connective tissue and how they find a passable path to their target organs.

Cell migration, even when ignoring the possibility of collective cell-cell interactions, is an extremely complex phenomenon. The connective tissue itself, such as collagen, is already a network of bio-polymers with a highly irregular structure and many nonlinear material properties. The cells are constantly changing their shape by forming and retreating protrusions that locally explore this surrounding material. At certain points the cells adhere to the external bio-polymer matrix via transmembrane receptors and exert forces onto the matrix by building contractile stress fibers between these focal adhesion points. By releasing adhesions at the rear part, the center of mass of the pre-stressed cell is moving forward. The stress fibers, mainly consisting of actin filaments crosslinked by myosin motor proteines, and the focal adhesions form together the most important part of the so-called cytoskeleton. This sub-structure of the cell is highly dynamic. Microscopic building blocks are constantly added to (and removed from) the cytoskeleton at different rates. Those biochemical remodelling processes, in turn, are regulated by a complex biochemical signaling network. Using signals transduced by the focal adhesion complexes, this reaction network is somehow processing information about the local environment and on this basis is guiding the cytoskeletal remodelling in a goal-directed way.

Selforganization by adaptive, explorative processes

How can a cell orchestrate the many microscopic structural changes of its cytoskeleton, from moment to moment, in such a way that there results on the macroscopic level a goal-directed migration towards its target site ? Or, on a somewhat shorter time-scale, how does the cell evaluate the local possibilites offered by the surrounding bio-polymer network and achieves at least a short move that brings it closer to the goal ?

One can generate some initial hypothesis for this unsolved problem by looking at similar selforganization processes in biology. A re-occuring pattern of adaptive selforganization is the method of blind trial and error, which also underlies evolution: Instead of executing a detailed, prescribed plan (that would fail anyway when encountering a novel situation) adaptive systems create many random alternatives and then select the ones which show the best signs of being useful in the present situation.

Applying this general mechanisms to our special case of cell migration, one might speculate, for example, that the cell is forming explorative protrusions into random directions. Only those protrusions that find a resilient fiber of the tissue matrix connect to this fiber via focal adhesions and are stabilized that way. All other protrusions are recycled. Similar explorative adaption processes may also occur on the more microscopic levels of cytoskeletal structure formation. Thus, a picture of the cell emerges as a multi-level hierarchy of adaptive processes. Each element of the system is autonomously trying out new ways to connect itself with the whole (variation). Elements that find a niche that is useful for the cell as a whole remain stable for a longer time (selection).

Complex Systems

In traditional physics, non-linear many-particle systems have been studied for a long time, and complex phenomena such as out-of-equilibrium phase transitions could be successfully explained in that context. More recently, the new field of Complex Systems (CS) has emerged as a natural extension of many-particle systems. The particles are now frequently replaced by "agents", for which reason some CS are also called multi-agent systems. In contrast to the particles of traditional physics, agents can be sub-systems of arbitrary complexity by themselves, such as bio-polymers, stress fibers, organelles, cells, humans, or firms. Inteactions between agents can be much more complicated than the simple force-laws between traditional point particles. However, in order to do theory for such systems, the behaviour of each agent and all interactions must be described in the respective models by clear rules (which may be stochastic). In this case, standard methods of theoretical physics can be applied to CS.

The theory of complex systems can explain the emergence of global patterns by a causal loop between the micro- and macro-level:

CS can also be used to model adaptive, explorative processes. In the language of physics, "variation" corresponds to the fluctuations of variables in a population of agents and selection corresponds to some performance-dependent growth rates of the different agents within this population.

Physics approach to reaction networks, cytoskeletal reorganization and cell migration

We try to describe the process of cell migration using the methods and concepts of Complex Systems Theory. While our dream goal would be the development of a true, integrative multi-level model of cell migration, the complexity of the problem demands to focus on the different levels and aspects of the process hierarchy one at a time:

Self-organization and signal processing in biochemical reaction networks

Spontaneous fluctuations of the cytoskeleton and long-time correlations

Cell migration as a random walk of adaptive agents in complex potential landscapes

We are also working on

Image processing methods for detecting the deformation of the bio-polymer networks caused by migrating cells.

reconstructing fiber networks from image stacks

2011-11-17T13:55:00.001+01:00

We have develloped a numerically efficient method to reconstruct a disordered network of thin biopolymers, such as collagen gels, from three-dimensional (3D) image stacks recorded with a confocal microscope. Our method is based on a template matching algorithm that simultaneously performs a binarization and skeletonization of the network. The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic. Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a 3x3 neighborhood. This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.

The method is described in an arXiv preprint.

C++ Implementation and sample data set (under construction)

teaching

2009-07-08T19:46:00.011+02:00

Early period: Semiconductor nanostructures:

Impurities in semiconductors

Concepts of modern semiconductor physics

Introduction to Physics (Mandatory course for non-physicists)

Coherent dynamics in semiconductor structures

Later period: Interdisciplinary, complex systems, biophysics:

Hot topics in modern science

Seminar on Physics, Computer Science, Neuro Science and Consciousness

Computational physics

Exercises in cell mechanics

Introduction to theoretical biophysics

In particular, I regularly teach a series of lectures on

Complex Systems Theory

The latter includes a wide spectrum of topics, such as (list not ordered):

Biochemical reaction networks, systems biology, emergence, artificial life, automata, fractals, stochastic processes, critical phenomena, complex networks, powerlaws, formal systems, L-systems, nonlinear dynamics, chaos, quantum chaos, dissipative systems, synchronization, traffic dynamics, cellular automata, neural networks, kohonen selforg. feature maps, (co-)evolutionary dynamics, game theory, autocatalytic networks, selfreplication, soft matter physics, granular matter physics, biophysics, econo-physics, socio-physics, cybernetics, strategies, selforganization, swarm dynamics, stigmergy, synergetics and information theory.

"recently" completed projects

2008-07-18T16:35:00.026+02:00

Recent Bachelor thesis:

Richard Gerum (Modellierung des Huddling-Verhaltens von Pinguinen mittels Multi-Agenten-Simulation)

Achim Schilling (Dynamik des Bindungsverhaltens des Adhäsionsapparates lebender Zellen)

Patrick Krauss (Rekonstruktion dreidimensionaler Fasernetzwerke aus konfokalen Mikroskopaufnahmen)

Janina Lange (Bestimmung der Porengrößenstatistik von Kollagengelen anhand konfokaler Mikroskopaufnahmen)

Mykhaylo Flipenko (Analytische Untersuchung von Zellmigrationsmodellen)

Alexander Heinz (Numerische Analyse der Zellmigration in Kollagengewebe)

Recent Diploma and PhD thesis:

Arthur Franz, "Evolution geregelter chemischer Netzwerke" (Diploma), Erlangen, Juli 2006

Carina Raupach, "On the spontaneous motion of cytoskeletally bound markers" [File Size 32MB !] (PhD), Juli 2008

Max Sajitz-Hermstein, "Stochastische Modelle fraktionaler Bewegungsvorgänge im lebenden Zytoskelett" (Diploma), Erlangen, Juli 2009

Franz Stadler, "Stochastische Modelle zur Beschreibung der anomalen Bewegungsstatistik migrierender Tumorzellen" (Diploma), Erlangen, November 2010

Recent short term projects

Michael Schmidberger (Statistical fluctuations in covalent modification cycles)

Anja Michl (Selfpropelled agents in random potential landscapes)

Sebastian Probst (Collective invasion of cells into complex tissue)

Andreas Kronwald (The cluster model of tumor cell invasion)

Sascha Maisel (Modelling of cytoskeletal dynamics)

Richard Gerum (Kraftausbreitung im Fasernetzwerk)

Mykhaylo Flipenko (Analytische Untersuchung von Zellmigrationsmodellen)

Alexander Heinz (Numerische Analyse der Zellmigration in Kollagengewebe)

Benedikt Krüger & Michael Schmidt (The shear response of random networks made of naturally bent fibers - a numerical investigation)

open projects

2008-05-06T21:22:00.024+02:00

The theory subgroup is offering many opportunities for collaborations of all kinds (short time projects, bachelor/master/diploma thesis). The following list shows some of the possible future directions of the group:

A. Reaction networks

So far, we have analyzed the probability distribution (PDF) of the temporal concentration fluctuations in a simple covalent modification cycle (CMC). The results lead to wide field of open questions, which could be addressed in future projects:

What are the concentration fluctuations in systems slightly more complex than a standard CMC, such as chains of CMCs, CMCs with feedback, and so on. These modifications are found frequently in the reaction networks of living cells, and they have fascinating properties even when approximated as deterministic systems (see, for example, the review article of Kholodenko).

How can the reaction networks control the cell activity despite the strong fluctuations ? Does the cell somehow fine-tune the reaction parameters of the networks in order to minimize fluctuations ? Do the parameters of known biochemical reaction networks confirm this hypothesis ? Could the fine-tuning be a self-regulated property ?

Or should one give up the view of biochemical reaction networks as networks of boolean switches (where each population of signalling molecules can only be in one of two states, "on" or "off") ? Should biochemical reaction networks be seen in a way similar to analog electronics circuits ?

If so, is it possible to realize with biochemical reaction networks functions analogous to "voltage controlled oscillators", "peak detektors", "sample and hold", or "filters" of various kinds ?

Or can the spontaneous concentration fluctuations in biochemical reaction networks be interpreted similar to the autonomous activity of neural networks, which is only modulated by the "data input" of the receptors (see recent article by Gros) ?

To which extent can the cell make use of the concentration fluctuations ? For such an investigation, a good starting point would be the bacterial chemotaxis network that contains a CMC to control the rotation direction of the flagella motor. Most relevant parameters of the CMC have been measured, and so it would be possible to apply our fluctuation theory to this system. It is known that the statistics of the motor rotation has some unusual power-law properties. Can those be explained by the biochemical statistics of the CMC ?

The fluctuation theory of biochemical reaction networks is also relevant to supermolecular assemblies in the cell, such as the focal adhesion complexes (FAC). Molecules are contantly added to and removed from the FAC. Using experimental techniques such as fluorescence correlation spectroscopy (which are available in our lab) the temporal statistics of such nonlinear, nonequilibrium and spatially inhomogeneous remodelling processes can be measured. It would therefore be possible to directly test the probability density functions and temporal correlation functions predicted by theoretical models of FAC remodelling.

B. Cytoskeletal fluctuations

Under construction.

C. Cell migration

Under construction.

D. Stress and strain field reconstruction

At present, we are seeking students for our image registration project. See also this flyer.

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All collaborators, while working on a research topic at the front of current science, will learn analytical methods as well as numerical techniques. Our institute has the advantage of a truely interdisciplinary environment and provides direct interactions between theorists and experimentalists. If you are interested in any of our current topics, or have own ideas for projects, please don't hesitate to contact me.

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stress and strain field reconstruction

2008-05-06T16:31:00.030+02:00

Whenever living cells are actively migrating through biological tissue, they are exerting forces onto the surrounding material. Depending on the local mechanical properties, the cellular traction forces lead to complex deformations of the material.

An important step towards a biophysical understanding of cancer cell migration is a quantitative measurement of these material deformations. For this purpose, the material is directly observed with optical microscopy and an image of the region around the cell is taken.

Next, we switch off the force generating apparatus of the cell by applying suitable chemicals. Once the cell relaxes, we take a second image, showing the deformation state of the material without the cellular forces.

By comparing the strained and relaxed images, it is possible to extract a quantitative deformation map, connecting each point of image 1 with the corresponding point of image 2 by a shift vector. This procedure of matching points between an original and a deformed image is known as "image registration".

The image registration procedure becomes easier if the material contains landmark objects of known shape. For this purpose we attach fluorescent markers to the material (spherical beads in random distribution). The picture below shows a pair of images containing such micro-beads:

Since each micro-bead is large enough to affect several neighboring pixels of the camera, a shift of the bead position can be detected with sub-pixel resolution, using techniques such as center-of-mass algorithms. In fact, we achieve a resolution in the nanometer range with our purely optical setup. Nevertheless, the registration procedure occasionally fails, for example in cases of clusters of close-by beads.

In the future, we would like to become independent from artificial markers and instead use the natural features of the material itself as landmarks. This would require an image registration method which can deal with complex shapes, such as fibres:

Another ongoing project is the reconstruction of the cellular forces from the known deformation field of the material. This, of course, requires a model of the mechanical properties of the material. For this purpose, we are using two-dimensional essays, in which the cell is placed onto a surface of an artificial, linear elastic material with a well-defined Youngs modulus and Poisson ratio. However, in the case of a realistic biological medium, such as a three-dimensional random collagen fibre network, the mechanical properties are extremely complex.

It is an open question to which extent the linear elastic model of a homogeneous, isotropic medium is applicable to such fibre networks, even on length scales much larger than the typical mesh size.

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We are now working on the development of a robust, fast registration method, taylor-made for the above applications. It will be suitable for a multigrid solution and eventually is to be implemented on a graphics processing unit (GPU). We are seeking collaborators for this project.

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reaction networks

2008-05-06T16:30:00.028+02:00

On the lowest level, cells have to be described by the theory of nonlinear biochemical reaction networks (for an introductory overview, see this talk).

We suspect that the fundamental mechanisms of adaptive selforganization, driven by fluctuations and varying growth rates, already apply at this molecular level (see following talk). As a first step in this direction, we are analyzing the statistical properties of fluctuations in typical reaction networks.

Such networks usually involve reversible covalent modification of signaling molecules, such as protein phosphorylation. Under conditions of small molecule numbers, as is frequently the case in living cells, mass action theory fails to describe the dynamics of such systems. Instead, the biochemical reactions must be treated as stochastic processes that intrinsically generate concentration fluctuations of the chemicals.

In a recent study we have investigated the stochastic reaction kinetics of covalent modification cycles (CMCs) by analytical modeling and numerically exact Monte-Carlo simulation of the temporally fluctuating concentration.

Depending on the parameter regime, we have found for the probability density of the concentration qualitatively distinct classes of distribution functions, including power law distributions with a fractional and tunable exponent.

Beside extremely broad probability distributions, the intrinsic concentration fluctuations in biochemical reaction networks may also give rise to long-time correlations. The fractional powerlaw correlations observed in the spoantaneous cytoskeletal fluctuations might then be explained by the underlying biochemical reaction processes. We have explored some of these possibilities in a recent diploma thesis (German). Parts of it have been publihed as a poster.

cytoskeletal fluctuations

2008-05-06T16:29:00.055+02:00

The CSK as a dynamic network of passive (blue) and active (red) acto-myosin stress fibers. A microbead (gray) becomes a node in the network and allows to to measure the response to externally applied forces (upper right inset), or to observe spontaneous fluctuations (lower right inset). The tractions of the active fibers create a force field (black arrows) in the surrounding matrix.

When microbeads are attached to the CSK of living cells, the beads start to move spontaneously in a random fashion. The amplitude of the bead's velocity fluctuations are on a much higher level than in the case of Brownian motion, indicating that they are not driven by thermal forces, but result from ATP-powered remodelling processes of the CSK.

Some measured example trajectories of CSK-bound beads (2-5) . For comparison, case 1 demonstrates the amount of thermal and camera noise of an immobilized bead. [Data from C. Raupach].

A detailed statistical analysis of many recorded bead trajectories reveals that the diffusion process is anomaleous in several respects: As a function of lag-time, one finds at around 1 sec a transition from sub- to superdiffusive transport, accompanied by characteristic changes of the turning angle distributions. On longer time scales, the mean squared displacement grows with a fractional powerlaw exponent. The step width distributions are non-gaussian with positive kurtosis.

The measured mean squared displacement (MSD) of 3 different diffusing, CSK-bound beads (2-4). Case 1 corresponds to an immobilized bead. The turning angle distributions (TAD) indicate antipersistence for lag times smaller than 1 sec and persistent transport for longer lags. [Data from C. Raupach]

Viewed as an abstract stochastic process, the bead motion can be described as a specific kind of persistent random walk on a white noise floor. This model can reproduce all the experimental distributions and successfully predicts new relations between certain observables. However, it cannot explain the origin of the long time correlations in the bead's transport directions.

For this purpose, we also develop more concrete, biophysical models.

The bead is described as a node in a simple network of developing stress fibers that act as springs. The biophysical remodelling of the network, i.e. the temporal growth and degradation of the fibers, corresponds to a temporal change of the spring parameters, like their rest lengths and stiffness. In this way, the long time memory of the bead's transport direction can be traced back to correlations in the fiber remodelling processes.

Since the remodelling processes are regulated by a biochemical reaction network, the correlations must be ultimately explained by a theory of fluctuations in nonlinear chemical reaction systems.

First results have been published in Metzner-07, Raupach-07 and Raupach-08 (thesis).

On a more macroscopic level, the cytoskeletal network may be described by a continuum material with complex rheological properties. The motion of any point in such a "medium" can then be explained by the interplay of the time-dependent forces acting on that point and the rheological response function of the medium. Some preliminary ideas along those lines have been presented in a talk.

cell migration

2008-05-06T16:29:00.051+02:00

One of our current projects is a coarse-grained description of migrating cells as self-propelled, adaptive agents in a complex potential landscape.

Typical questions arising in that context are as follows:

Assume a given network of bio-polymer fibers with known elastic response. Imagine embedded into this network a passive, spherical model cell of given elastic properties. What is the resulting effective 3D potential energy landscape ? Is it unique ? How can the viscosity or the active shape changes of the cell be included in a coarse-grained way ?

Can the complex internal reorganizations of the cytoskeleton, the time-dependent point forces onto the extracellular matrix and the resulting matrix deformation be translated into an effective total force acting on the cell center ?

What are the statistical properties of this effective migration force and what are the properties of the resulting random walk in the complex potential landscape (mean squared displacement, step with distribution) ?

Can adaption of the cell to the local environment be incorporated into that model as a functional dependence of the statistical force parameters on the local shape of the potential landscape ? If yes, how must this adaptive modulation of force statistics be optimized in order to achieve efficient (fast and energy saving) migration ?

Some initial ideas have been posted in a research blog.

At present, we also follow a data-driven approach in order to understand cell migration: Living tumor cells are brought on the surface of a collagen gel with controlled mechanical properties. The invasion of the tumor cells into the collagen gel is then observed with a light microscope, resulting in trajectories of the individual cells, or more conveniently, distribution functions.

We now try to model the time-dependent invasion profiles as a stochastic process, described by Fokker-Planck equations with suitable diffusion and drift terms.

For first ideas and results, see the research blog.

recent publications (biophysics)

2008-05-06T14:59:00.029+02:00

* C. Metzner, C. Raupach, C.T. Mierke, and B. Fabry,

Fluctuations of cytoskeleton-bound microbeads—

the effect of bead–receptor binding dynamics,

J. Phys.: Condens. Matter 22 194105 (2010) . PDF

* C. Metzner, M. Sajitz-Hermstein, M. Schmidberger, and B. Fabry,
Noise and critical phenomena in biochemical signaling cycles
at small molecule numbers,
Phys. Review E 80, 021915 (2009). PDF

* C. Raupach, D. Zitterbart, C. Mierke, C. Metzner, F. Müller, and Ben Fabry,
Stress fluctuations and motion of cytoskeletal-bound markers,
Phys. Rev. E 76, 011918 (2007). PDF

* C. Metzner, C. Raupach, D. Zitterbart, and B. Fabry,
Simple model of cytoskeletal fluctuations,
Phys. Rev. E 76, 021925 (2007). PDF

older publications (semiconductor nanostructures)

2008-05-06T14:53:00.006+02:00

* H.J. Beyer, C. Metzner, J. Heitzer and G.H. Döhler,
Temperature dependence of the tunable luminescence, absorption and gain spectra of n-i-p-i doping superlattices - Theory and comparison with experiment,
Superlattices and Microstructures 6, 351-356 (1989).

* C. Metzner, H.J. Beyer and G.H. Döhler,
Theory of donor-acceptor-pair luminescence in d-doping n-i-p-i superlattices,
Phys. Rev. B 46, 4128-4138 (1992). PDF

* C. Metzner, S.G. Müller, T. Schmidt and G.H. Döhler,
Classical theory of impurity bands in excited d-doping n-i-p-i superlattices,
Superlattices and Microstructures 12, 101-106 (1992).

* K. Schrüfer, S. Eckl, C.Metzner, H.J. Beyer and G.H. Döhler,
Thomas-Fermi theory of n-i-p-i doping superlattices,
J. Appl. Phys. 72 (10), 4992-4994 (1992). PDF

* T. Schmidt, C. Metzner, S.G. Müller and G.H. Döhler,
Classical theory of impurity bands in d-doping superlattices,
Phys. Rev. B 47, 10633-10647 (1993). PDF

* M. Renn, C. Metzner and G.H. Döhler,
Effect of random impurity distribution on the luminescence of doping superlattices,
Phys. Rev. B 48, 11220-11227 (1993). PDF

* K. Schrüfer, C. Metzner, U. Wieser, M. Kneissl and G.H. Döhler,
Quantum effects of potential fluctuations in GaAs d-doping superlattices,
Superlattices and Microstructures 15 (4), 413-420 (1994).

* C. Metzner, K. Schrüfer, U. Wieser, M. Luber, M. Kneissl and G.H. Döhler,
Disorder effects on luminescence in d-doped n-i-p-i superlattices,
Phys. Rev. B. 51, 5106-5115 (1995). PDF

* T. Schmidt, St.G. Müller, K.H. Gulden, C. Metzner and G.H. Döhler,
In-plane transport properties of heavily d-doped GaAs n-i-p-i superlattices: Metal-insulator transition, weak and strong localization,
Phys. Rev. B 54, 13980-13995 (1996). PDF

* J. Schönhut, C. Metzner, S. Müller, T. Schmidt, G.H. Döhler, A. Förster and H. Lüth,
Optical investigations of impurity bands in a d-doped n-layer,
sol. state electron. 40, 701-705 (1996). PDF

* K. Schrüfer, C. Metzner, M. Hofmann and G.H. Döhler,
Nonlinear impurity screening and metal insulator transition in strongly depleted 2D electron gases,
Superlattices and Microstructures 21, 223-230 (1997). PDF

* C. Metzner, G.H. Döhler and H. Sakaki,
Localization of Quantum Well Excitons by Lateral Disorder. A Numerical Study,
phys. stat. sol. (a) 164, 471-476 (1997). PDF

* S. Tsujino, C. Metzner, T. Noda and H. Sakaki,
Saturation of Intersubband Absorption by Real-Space Transfer in Modulation Doped Single GaAs-AlAs Quantum Well,
phys. stat. sol. (b) 204, 162-165 (1997). PDF

* C. Metzner, M. Hofmann and G.H. Döhler,
Intersubband transitions of a quasi-two-dimensional electron gas in the strong disorder regime,
Phys. Rev. B 58, 7188-7196 (1998). PDF

* S. Rott, K. Schrüfer, C. Metzner et. al.,
Screening in a d-doped semiconductor,
Superlattices and Microstructures 23, 315-321 (1998). PDF

* C. Metzner, M. Hofmann and G.H. Döhler,
Effects of extreme disorder on electronic properties in d-doped semiconductor microstructures - a realistic computer simulation,
Superlattices and Microstructures 25, 239-246 (1999). PDF

* C. Metzner, G. Yusa and H. Sakaki,
Modelling of inter-dot Coulomb interaction effects in field-effect transistors with embedded quantum dot layer,
Superlattices and Microstructures 25, 537-549 (1999). PDF

* C. Metzner and G.H. Döhler,
Collective optical excitation of interacting localized electrons,
Phys. Rev. B 60, 11005-11013 (1999). PDF

* C. Metzner, C. Steen, M. Hofmann, M. Hackenberg and G.H. Döhler,
Interplay of disorder and tunneling in coupled quantum well structures - Tuning the intersubband lineshape by an electric field,
Physica E 7, 722-725 (2000). PDF

* C. Metzner and G.H. Döhler,
Role of dynamic depolarization for the intersubband resonance in the localization regime,
Physica E 7, 718-721 (2000). PDF

* C. Metzner, C. Steen, R. Winkler, M. Hofmann, M. Hackenberg and G.H. Döhler,
Intersubband transitions in coupled wells with disorder,
Physica E 6, 606 (2000). PDF

* C. Steen, C. Metzner, M. Hofmann and G.H. Döhler,
Absorption of in-plane polarized light in quasi-2D systems enabled by strong potential fluctuations,
Physica E 7, 220 (2000). PDF

* M. Hackenberg, C. Metzner, M. Hofmann and G.H. Döhler,
Subband selective disorder in a quasi-2D system and its effect on the intersubband spectrum,
Physica E 7, 216 (2000). PDF

* S. Tsujino, M. Rüfenacht, H. Nakajima, T. Noda, C. Metzner and H. Sakaki,
Peak position of the intersubband absorption spectrum of quantum wells with controlled electron concentration,
Phys. Rev. B 62, 1560 (2000). PDF

* C. Metzner,
Intersubband Excitations in Disordered Systems
Habilitation Thesis (2000). PDF

* W.V. Schoenfeld, C. Metzner, E. Letts and P.M. Petroff,
Spectroscopy of strain-induced quantum dots in GaAs/AlGaAs quantum well structures,
Phys. Rev. B 63, 205319 (2001). PDF

* R. Grill, C. Metzner and G.H. Döhler,
Unrestricted Hartree-Fock cluster calculation of donor-acceptor-pair luminescence in d-doped n-i-p-i superlattices,
Phys. Rev. B 63, 235316 (2001). PDF

* M. Beck, D. Streb, M. Vitzethum, P. Kiesel, S. Malzer, C. Metzner, and G.H. Döhler,
Ambipolar drift of spatially separated electrons and holes,
Phys. Rev. B 64, 085307 (2001). PDF

* I. Shtrichman, C. Metzner, E. Ehrenfreund, D. Gershoni, K.D. Maranowski, and A.C. Gossard,
Depolarization Shift of the Intersubband Resonance in a Quantum Well with Electron-Hole Plasma,
Phys. Rev. B 65, 035310 (2001). PDF

* I. Shtrichman, C. Metzner, B.D. Geradot, W.V. Schoenfeld, and P.M. Petroff,
Photoluminescence of a single InAs quantum dot molecule under applied electric field,
Phys. Rev. B (Rapid Com.) 65, 081303(R) (2002). PDF

* N. Riemann, C. Metzner, and G.H. Döhler,
Density dependent intersubband absorption in strongly disordered systems,
Phys. Rev. B 65, 115304 (2002). PDF

* C. Metzner,
Controlling a cluster of interacting quantum dot molecules by laser pulses – a computer experiment,
phys. stat. sol. (c) 4, 1360 (2003). PDF

* C. Metzner, D. Stehr,
Mesoscopic dots as collective terahertz oscillators,
Phys. Rev. B 70, 195433 (2004). PDF

* D. Stehr, C. Metzner, M. Helm, T. Roch , and G. Strasser,
Resonant Impurity Bands in Semiconductor Superlattices,
Phys. Rev. Lett. 95, 257401 (2005). PDF

* M. Beck, C. Metzner, S. Malzer, and G.H. Döhler,
Spin lifetimes and strain-controlled spin precession of drifting electrons in GaAs,
Europhysics Letters 75 (4), 597-603 (2006).

* R. Schmidt, U. Scholz, M. Vitzethum, R. Fix, C. Metzner, et al.
Fabrication of genuine single-quantum-dot light-emitting diodes,
Appl. Phys. Letters 88, Rev. B 70, 121115 (2006). PDF

* D. Stehr and M. Helm, C. Metzner, M.C. D. Wanke,
Microscopic theory of impurity states in coupled quantum wells and superlattices,
Phys. Rev. B 74, 085311 (2006). PDF

contact

2008-05-06T14:36:00.010+02:00

Delocalized:
Email: claus.metzner@gmx.net
Mobile: 0151 206 12304

University:
ZMPT, Room 02.078
Henkestr. 91
91052 Erlangen
Germany
Phone: +49 (0)9131 852 5615
Fax: +49 (0)9131 852 5601

Private:
Schleifmühlstr. 6
91054 Erlangen
Phone: +49 (0) 9131 973037

curriculum vitae

2008-05-06T13:50:00.019+02:00

Preparations:

5 billion years of evolutionary optimization
Birth (08.Feb, 1964)

Theory of Semiconductor Nanostructures:

Diploma (89)
Civil Service (89-90)
PhD (90-94)
Postdoc, University of Tokyo (95-97)
Habilitation (97-01)
Postdoc, University of California, Santa Barbara (00-01)

Theory of Complex Systems:

Privatdozent, University of Erlangen (since 01)

Theoretical Biophysics:

Collaboration with Prof. Ben Fabry (05-06))
Member of Fabry Biophysics Group (since 06)

Long CV as PDF

List of Journal Publications as PDF