Mathematical modeling of the Drosophila neuromuscular junction

Poster presentation: An important challenge in neuroscience is understanding how networks of neurons go about processing information. Synapses are thought to play an essential role in cellular information processing however quantitative and mathematical models of the underlying physiologic processes that occur at synaptic active zones are lacking. We are generating mathematical models of synaptic vesicle dynamics at a well-characterized model synapse, the Drosophila larval neuromuscular junction. This synapse's simplicity, accessibility to various electrophysiological recording and imaging techniques, and the genetic malleability intrinsic to Drosophila system make it ideal for computational and mathematical studies. We have employed a reductionist approach and started by modeling single presynaptic boutons. Synaptic vesicles can be divided into different pools; however, a quantitative understanding of their dynamics at the Drosophila neuromuscular junction is lacking [4]. We performed biologically realistic simulations of high and low release probability boutons [3] using partial differential equations (PDE) taking into account not only the evolution in time but also the spatial structure in two dimensions (the extension to three dimensions will be implemented soon). PDEs are solved using UG, a program library for the calculation of multi-dimensional PDEs solved using a finite volume approach and implicit time stepping methods leading to extended linear equation systems be solvedwith multi-grid methods [3,4]. Numerical calculations are done on multi-processor computers for fast calculations using different parameters in order to asses the biological feasibility of different models. In preliminary simulations, we modeled vesicle dynamics as a diffusion process describing exocytosis as Neumann streams at synaptic active zones. The initial results obtained with these models are consistent with experimental data. However, this should be regarded as a work in progress. Further refinements will be implemented, including simulations using morphologically realistic geometries which were generated from confocal scans of the neuromuscular junction using NeuRA (a Neuron Reconstruction Algorithm). Other parameters such as glutamate diffusion and reuptake dynamics, as well as postsynaptic receptor kinetics will be incorporated as well

Synaptic boutons sizes are tuned to best fit their physiological performances

To truly appreciate the myriad of events which relate synaptic function and vesicle dynamics, simulations should be done in a spatially realistic environment. This holds true in particular in order to explain as well the rather astonishing motor patterns which we observed within in vivo recordings which underlie peristaltic contractionsas well as the shape of the EPSPs at different forms of long-term stimulation, presented both here, at a well characterized synapse, the neuromuscular junction (NMJ) of the Drosophila larva (c.f. Figure 1). To this end, we have employed a reductionist approach and generated three dimensional models of single presynaptic boutons at the Drosophila larval NMJ. Vesicle dynamics are described by diffusion-like partial differential equations which are solved numerically on unstructured grids using the uG platform. In our model we varied parameters such as bouton-size, vesicle output probability (Po), stimulation frequency and number of synapses, to observe how altering these parameters effected bouton function. Hence we demonstrate that the morphologic and physiologic specialization maybe a convergent evolutionary adaptation to regulate the trade off between sustained, low output, and short term, high output, synaptic signals. There seems to be a biologically meaningful explanation for the co-existence of the two different bouton types as previously observed at the NMJ (characterized especially by the relation between size and Po), the assigning of two different tasks with respect to short- and long-time behaviour could allow for an optimized interplay of different synapse types. We can present astonishing similar results of experimental and simulation data which could be gained in particular without any data fitting, however based only on biophysical values which could be taken from different experimental results. As a side product, we demonstrate how advanced methods from numerical mathematics could help in future to resolve also other difficult experimental neurobiological issues

Synaptic bouton sizes are tuned to best fit their physiological performances : poster presentation from Twentieth Annual Computational Neuroscience Meeting: CNS*2011, Stockholm, Sweden, 23 - 28 July 2011

Poster presentation from Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011. To truly appreciate the myriad of events which relate synaptic function and vesicle dynamics, simulations should be done in a spatially realistic environment. This holds true in particular in order to explain the rather astonishing motor patterns presented here which we observed within in vivo recordings which underlie peristaltic contractions at a well characterized synapse, the neuromuscular junction (NMJ) of the Drosophila larva. To this end, we have employed a reductionist approach and generated three dimensional models of single presynaptic boutons at the Drosophila larval NMJ. Vesicle dynamics are described by diffusion-like partial differential equations which are solved numerically on unstructured grids using the uG platform. In our model we varied parameters such as bouton-size, vesicle output probability (Po), stimulation frequency and number of synapses, to observe how altering these parameters effected bouton function. Hence we demonstrate that the morphologic and physiologic specialization maybe a convergent evolutionary adaptation to regulate the trade off between sustained, low output, and short term, high output, synaptic signals. There seems to be a biologically meaningful explanation for the co-existence of the two different bouton types as previously observed at the NMJ (characterized especially by the relation between size and Po),the assigning of two different tasks with respect to short- and long-time behaviour could allow for an optimized interplay of different synapse types. As a side product, we demonstrate how advanced methods from numerical mathematics could help in future to resolve also other difficult experimental neurobiological issues

1D-3D hybrid modeling—from multi-compartment models to full resolution models in space and time

Investigation of cellular and network dynamics in the brain by means of modeling & simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling & simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in level of detail to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing spatial aspects of the cells. For single cell or small-world networks, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the 3D morphology of cells and organelles into 3D space and time-dependent simulations. Every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. We present a hybrid simulation approach, that makes use of reduced 1D-models using e.g. the NEURON which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed 3D-morphology of neurons and organelles. To couple 1D- & 3D-simulations, we present a geometry and membrane potential mapping framework, with which graph-based morphologies, e.g. in swc-/hoc-format, are mapped to full surface and volume representations of the neuron; membrane potential data from 1D-simulations are used as boundary conditions for full 3D simulations. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved highly detailed 3D-modeling approaches. The new framework is applied to investigate electrically active neurons and their intracellular spatio-temporal Calcium Dynamics

Emergent Horizons in the Laboratory

The concept of a horizon known from general relativity describes the loss of causal connection and can be applied to non-gravitational scenarios such as out-of-equilibrium condensed-matter systems in the laboratory. This analogy facilitates the identification and theoretical study (e.g., regarding the trans-Planckian problem) and possibly the experimental verification of "exotic" effects known from gravity and cosmology, such as Hawking radiation. Furthermore, it yields a unified description and better understanding of non-equilibrium phenomena in condensed matter systems and their universal features. By means of several examples including general fluid flows, expanding Bose-Einstein condensates, and dynamical quantum phase transitions, the concepts of event, particle, and apparent horizons will be discussed together with the resulting quantum effects.Comment: 7 pages, 4 figure

Signatures of Planck-scale interactions in the cosmic microwave background?

Based on a rather general low-energy effective action (interacting quantum fields in classical curved space-times), we calculate potential signatures of new physics (such as quantum gravity) at ultra-high energies (presumably the Planck scale) in the anisotropies of the cosmic microwave background. These Planck-scale interactions create non-Gaussian contributions, where special emphasis is laid on the three-point function as the most promising observable, which also allows the discrimination between models violating and those obeying Lorentz invariance. PACS: 98.80.Cq, 04.62.+v, 98.70.Vc, 98.80.Qc.Comment: 4 page

Geo-Biological Investigations on Azooxanthellate Cold-Water Coral Reefs on the Carbonate Mounds Along the Celtic Continental Slope

Northeast Atlantic 2004 Cruise No. 61, Leg 1 April 19 to May 4, 2004, Lisbon – Cor

Probing the potential landscape inside a two-dimensional electron-gas

We report direct observations of the scattering potentials in a two-dimensional electron-gas using electron-beam diffaction-experiments. The diffracting objects are local density-fluctuations caused by the spatial and charge-state distribution of the donors in the GaAs-(Al,Ga)As heterostructures. The scatterers can be manipulated externally by sample illumination, or by cooling the sample down under depleted conditions.Comment: 4 pages, 4 figure

Nonexponetial relaxation of photoinduced conductance in organic field effect transistor

We report detailed studies of the slow relaxation of the photoinduced excess charge carriers in organic metal-insulator-semiconductor field effect transistors consisting of poly(3-hexylthiophene) as the active layer. The relaxation process cannot be physically explained by processes, which lead to a simple or a stretched-exponential decay behavior. Models based on serial relaxation dynamics due to a hierarchy of systems with increasing spatial separation of the photo-generated negative and positive charges are used to explain the results. In order to explain the observed trend, the model is further modified by introducing a gate voltage dependent coulombic distribution manifested by the trapped negative charge carriers.Comment: 17 pages, 3 Figure

Impact of Numerical Methods in Thermal Modeling of Li-Ion Batteries on Temperature Distribution and Computation Time

Thermal battery modeling is important for further battery development and optimization. The temperature strongly influences the performance and aging behavior. In the cell stack, electrochemical processes take place resulting in a large amount of heat release, which, in turn, affects the temperature distribution. Therefore, the main focus is on the cell stack, the most complex structure inside the cell. In particular, the discontinuous and anisotropic material properties represent a major challenge for simulations due to the layering. This work proposes self-developed methods, based on the Finite Volume Method and the Finite Element Method, taking on these challenges. First, for both methods the functionality is verified and numerical convergence is validated. These, and also classical methods, are compared based on test problems with a known analytical solution in view of numerical errors as well as computing time. It if found that their accuracy and efficiency depends strongly on the specific problem, which makes their numerical investigation necessary and inevitable. Second, the methods are evaluated on a specific battery problem. Their results are plausible and correspond to the physical phenomena