@article{EigenBaumDeanetal., author = {Eigen, Lennart and Baum, Daniel and Dean, Mason N. and Werner, Daniel and W{\"o}lfer, Jan and Nyakatura, John A.}, title = {Ontogeny of a tessellated surface: carapace growth of the longhorn cowfish Lactoria cornuta}, series = {Journal of Anatomy}, volume = {241}, journal = {Journal of Anatomy}, number = {3}, publisher = {Wiley}, doi = {10.1111/joa.13692}, pages = {565 -- 580}, abstract = {Biological armors derive their mechanical integrity in part from their geometric architectures, often involving tessellations: individual structural elements tiled together to form surface shells. The carapace of boxfish, for example, is comprised of mineralized polygonal plates, called scutes, arranged in a complex geometric pattern and nearly completely encasing the body. In contrast to artificial armors, the boxfish exoskeleton grows with the fish; the relationship between the tessellation and the gross structure of the armor is therefore critical to sustained protection throughout growth. To clarify whether or how the boxfish tessellation is maintained or altered with age, we quantify architectural aspects of the tessellated carapace of the longhorn cowfish Lactoria cornuta through ontogeny (across nearly an order of magnitude in standard length) and in a high-throughput fashion, using high-resolution microCT data and segmentation algorithms to characterize the hundreds of scutes that cover each individual. We show that carapace growth is canalized with little variability across individuals: rather than continually adding scutes to enlarge the carapace surface, the number of scutes is surprisingly constant, with scutes increasing in volume, thickness, and especially width with age. As cowfish and their scutes grow, scutes become comparatively thinner, with the scutes at the edges (weak points in a boxy architecture) being some of the thickest and most reinforced in younger animals and thinning most slowly across ontogeny. In contrast, smaller scutes with more variable curvature were found in the limited areas of more complex topology (e.g. around fin insertions, mouth, and anus). Measurements of Gaussian and mean curvature illustrate that cowfish are essentially tessellated boxes throughout life: predominantly zero curvature surfaces comprised of mostly flat scutes, and with scutes with sharp bends used sparingly to form box edges. Since growth of a curved, tiled surface with a fixed number of tiles would require tile restructuring to accommodate the surface's changing radius of curvature, our results therefore illustrate a previously unappreciated advantage of the odd boxfish morphology: by having predominantly flat surfaces, it is the box-like body form that in fact permits a relatively straightforward growth system of this tessellated architecture (i.e. where material is added to scute edges). Our characterization of the ontogeny and maintenance of the carapace tessellation provides insights into the potentially conflicting mechanical, geometric and developmental constraints of this species, but also perspectives into natural strategies for constructing mutable tiled architectures.}, language = {en} } @article{KiewiszFabigConwayetal., author = {Kiewisz, Robert and Fabig, Gunar and Conway, William and Baum, Daniel and Needleman, Daniel and M{\"u}ller-Reichert, Thomas}, title = {Three-dimensional structure of kinetochore-fibers in human mitotic spindles}, series = {eLife}, volume = {11}, journal = {eLife}, doi = {10.7554/eLife.75459}, pages = {e75459}, abstract = {During cell division, kinetochore microtubules (KMTs) provide a physical linkage between the chromosomes and the rest of the spindle. KMTs in mammalian cells are organized into bundles, so-called kinetochore-fibers (k-fibers), but the ultrastructure of these fibers is currently not well characterized. Here we show by large-scale electron tomography that each k-fiber in HeLa cells in metaphase is composed of approximately nine KMTs, only half of which reach the spindle pole. Our comprehensive reconstructions allowed us to analyze the three-dimensional (3D) morphology of k-fibers and their surrounding MTs in detail. We found that k-fibers exhibit remarkable variation in circumference and KMT density along their length, with the pole-proximal side showing a broadening. Extending our structural analysis then to other MTs in the spindle, we further observed that the association of KMTs with non-KMTs predominantly occurs in the spindle pole regions. Our 3D reconstructions have implications for KMT growth and k-fiber self-organization models as covered in a parallel publication applying complementary live-cell imaging in combination with biophysical modeling (Conway et al., 2022). Finally, we also introduce a new visualization tool allowing an interactive display of our 3D spindle data that will serve as a resource for further structural studies on mitosis in human cells.}, language = {en} } @article{BoeltsHarthGaoetal., author = {Boelts, Jan and Harth, Philipp and Gao, Richard and Udvary, Daniel and Yanez, Felipe and Baum, Daniel and Hege, Hans-Christian and Oberlaender, Marcel and Macke, Jakob H.}, title = {Simulation-based inference for efficient identification of generative models in computational connectomics}, series = {PLOS Computational Biology}, volume = {19}, journal = {PLOS Computational Biology}, number = {9}, doi = {10.1371/journal.pcbi.1011406}, abstract = {Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the 'posterior distribution over parameters conditioned on the data') that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.}, language = {en} } @article{BoeltsHarthGaoetal., author = {Boelts, Jan and Harth, Philipp and Gao, Richard and Udvary, Daniel and Yanez, Felipe and Baum, Daniel and Hege, Hans-Christian and Oberlaender, Marcel and Macke, Jakob H}, title = {Simulation-based inference for efficient identification of generative models in connectomics}, series = {bioRxiv}, journal = {bioRxiv}, doi = {10.1101/2023.01.31.526269}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-89890}, abstract = {Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neural networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a single rule to fit the empirical data, SBI considers many parametrizations of a wiring rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rules and relies on machine learning methods to estimate a probability distribution (the `posterior distribution over rule parameters conditioned on the data') that characterizes all data-compatible rules. We demonstrate how to apply SBI in connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.}, language = {en} } @article{LindowBaumHege2014, author = {Lindow, Norbert and Baum, Daniel and Hege, Hans-Christian}, title = {Ligand Excluded Surface: A New Type of Molecular Surface}, series = {IEEE Transactions on Visualization and Computer Graphics}, volume = {20}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = {12}, doi = {10.1109/TVCG.2014.2346404}, pages = {2486 -- 2495}, year = {2014}, abstract = {The most popular molecular surface in molecular visualization is the solvent excluded surface (SES). It provides information about the accessibility of a biomolecule for a solvent molecule that is geometrically approximated by a sphere. During a period of almost four decades, the SES has served for many purposes - including visualization, analysis of molecular interactions and the study of cavities in molecular structures. However, if one is interested in the surface that is accessible to a molecule whose shape differs significantly from a sphere, a different concept is necessary. To address this problem, we generalize the definition of the SES by replacing the probe sphere with the full geometry of the ligand defined by the arrangement of its van der Waals spheres. We call the new surface ligand excluded surface (LES) and present an efficient, grid-based algorithm for its computation. Furthermore, we show that this algorithm can also be used to compute molecular cavities that could host the ligand molecule. We provide a detailed description of its implementation on CPU and GPU. Furthermore, we present a performance and convergence analysis and compare the LES for several molecules, using as ligands either water or small organic molecules.}, language = {en} } @article{RigortGuentherHegerletal.2012, author = {Rigort, Alexander and G{\"u}nther, David and Hegerl, Reiner and Baum, Daniel and Weber, Britta and Prohaska, Steffen and Medalia, Ohad and Baumeister, Wolfgang and Hege, Hans-Christian}, title = {Automated segmentation of electron tomograms for a quantitative description of actin filament networks}, series = {Journal of Structural Biology}, volume = {177}, journal = {Journal of Structural Biology}, doi = {10.1016/j.jsb.2011.08.012}, pages = {135 -- 144}, year = {2012}, language = {en} } @article{SchmidtEhrenbergBaumHege2001, author = {Schmidt-Ehrenberg, Johannes and Baum, Daniel and Hege, Hans-Christian}, title = {Visually stunning - Molecular conformations}, series = {The Biochemist}, volume = {23}, journal = {The Biochemist}, number = {5}, pages = {22 -- 26}, year = {2001}, language = {en} } @article{KratzBaumHotz2013, author = {Kratz, Andrea and Baum, Daniel and Hotz, Ingrid}, title = {Anisotropic Sampling of Planar and Two-Manifold Domains for Texture Generation and Glyph Distribution}, series = {Transactions on Visualization and Computer Graphics (TVCG)}, volume = {19}, journal = {Transactions on Visualization and Computer Graphics (TVCG)}, doi = {10.1109/TVCG.2013.83}, pages = {1782 -- 1794}, year = {2013}, language = {en} } @article{WeberGreenanProhaskaetal.2012, author = {Weber, Britta and Greenan, Garrett and Prohaska, Steffen and Baum, Daniel and Hege, Hans-Christian and M{\"u}ller-Reichert, Thomas and Hyman, Anthony and Verbavatz, Jean-Marc}, title = {Automated tracing of microtubules in electron tomograms of plastic embedded samples of Caenorhabditis elegans embryos}, series = {Journal of Structural Biology}, volume = {178}, journal = {Journal of Structural Biology}, number = {2}, doi = {10.1016/j.jsb.2011.12.004}, pages = {129 -- 138}, year = {2012}, language = {en} } @article{LindowBaumHege2011, author = {Lindow, Norbert and Baum, Daniel and Hege, Hans-Christian}, title = {Voronoi-Based Extraction and Visualization of Molecular Paths}, series = {IEEE Transactions on Visualization and Computer Graphics}, volume = {17}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = {12}, doi = {10.1109/TVCG.2011.259}, pages = {2025 -- 2034}, year = {2011}, language = {en} }