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Biomedical subjects

Christian Panse

Publications and source records attributed to Christian Panse.

5 recordsLinked to original sources
PubMed2006

Qualitative and quantitative analyses of protein phosphorylation in naive and stimulated mouse synaptosomal preparations.

Activity-dependent protein phosphorylation is a highly dynamic yet tightly regulated process essential for cellular signaling. Although recognized as critical for neuronal functions, the extent and stoichiometry of phosphorylation in brain cells remain undetermined. In this study, we resolved activity-dependent changes in phosphorylation stoichiometry at specific sites in distinct subcellular compartments of brain cells. Following highly sensitive phosphopeptide enrichment using immobilized metal affinity chromatography and mass spectrometry, we isolated and identified 974 unique phosphorylation sites on 499 proteins, many of which are novel. To further explore the significance of specific phosphorylation sites, we used isobaric peptide labels and determined the absolute quantity of both phosphorylated and non-phosphorylated peptides of candidate phosphoproteins and estimated phosphorylation stoichiometry. The analyses of phosphorylation dynamics using differentially stimulated synaptic terminal preparations revealed activity-dependent changes in phosphorylation stoichiometry of target proteins. Using this method, we were able to differentiate between distinct isoforms of Ca2+/calmodulin-dependent protein kinase (CaMKII) and identify a novel activity-regulated phosphorylation site on the glutamate receptor subunit GluR1. Together these data illustrate that mass spectrometry-based methods can be used to determine activity-dependent changes in phosphorylation stoichiometry on candidate phosphopeptides following large scale phosphoproteome analysis of brain tissue.

Amino Acid Sequence↗
PubMedDate not supplied

CartoDraw: a fast algorithm for generating contiguous cartograms.

Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. In this study, we formally define a family of cartogram drawing problems. We show that even simple variants are unsolvable in the general case. Because the feasible variants are NP-complete, heuristics are needed to solve the problem. Previously proposed solutions suffer from problems with the quality of the generated drawings. For a cartogram to be recognizable, it is important to preserve the global shape or outline of the input map, a requirement that has been overlooked in the past. To address this, our objective function for cartogram drawing includes both global and local shape preservation. To measure the degree of shape preservation, we propose a shape similarity function, which is based on a Fourier transformation of the polygons' curvatures. Also, our application is visualization of dynamic data, for which we need an algorithm that recalculates a cartogram in a few seconds. None of the previous algorithms provides adequate performance with an acceptable level of quality for this application. In this paper, we therefore propose an efficient iterative scanline algorithm to reposition edges while preserving local and global shapes. Scanlines may be generated automatically or entered interactively to guide the optimization process more closely. We apply our algorithm to several example data sets and provide a detailed comparison of the two variants of our algorithm and previous approaches.

Algorithms↗
PubMedDate not supplied

Visualization of geo-spatial point sets via global shape transformation and local pixel placement.

In many applications, data is collected and indexed by geo-spatial location. Discovering interesting patterns through visualization is an important way of gaining insight about such data. A previously proposed approach is to apply local placement functions such as PixelMaps that transform the input data set into a solution set that preserves certain constraints while making interesting patterns more obvious and avoid data loss from overplotting. In experience, this family of spatial transformations can reveal fine structures in large point sets, but it is sometimes difficult to relate those structures to basic geographic features such as cities and regional boundaries. Recent information visualization research has addressed other types of transformation functions that make spatially-transformed maps with recognizable shapes. These types of spatial-transformation are called global shape functions. In particular, cartogram-based map distortion has been studied. On the other hand, cartogram-based distortion does not handle point sets readily. In this study, we present a framework that allows the user to specify a global shape function and a local placement function. We combine cartogram-based layout (global shape) with PixelMaps (local placement), obtaining some of the benefits of each toward improved exploration of dense geo-spatial data sets.

Journal Article↗