Computational modeling of growth: systemic and pulmonary hypertension in the heart

We introduce a novel constitutive model for growing soft biological tissue and study its performance in two characteristic cases of mechanically induced wall thickening of the heart. We adopt the concept of an incompatible growth configuration introducing the multiplicative decomposition of the deformation gradient into an elastic and a growth part. The key feature of the model is the definition of the evolution equation for the growth tensor which we motivate by pressure-overload-induced sarcomerogenesis. In response to the deposition of sarcomere units on the molecular level, the individual heart muscle cells increase in diameter, and the wall of the heart becomes progressively thicker. We present the underlying constitutive equations and their algorithmic implementation within an implicit nonlinear finite element framework. To demonstrate the features of the proposed approach, we study two classical growth phenomena in the heart: left and right ventricular wall thickening in response to systemic and pulmonary hypertension

Impact of image segmentation on high-content screening data quality for SK-BR-3 cells

<p>Abstract</p> <p>Background</p> <p>High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells.</p> <p>Results</p> <p>Cases of poor cell body segmentation occurred frequently for the SK-BR-3 cell line. We trained classifiers to identify SK-BR-3 cells that were well segmented. On an independent test set created by human review of cell images, our optimal support-vector machine classifier identified well-segmented cells with 81% accuracy. The dose responses of morphological features were measurably different in well- and poorly-segmented populations. Elimination of the poorly-segmented cell population increased the purity of DNA content distributions, while appropriately retaining biological heterogeneity, and simultaneously increasing our ability to resolve specific morphological changes in perturbed cells.</p> <p>Conclusion</p> <p>Image segmentation has a measurable impact on HCS data. The application of a multivariate shape-based filter to identify well-segmented cells improved HCS data quality for an HCS-unfriendly cell line, and could be a valuable post-processing step for some HCS datasets.</p

Some Results on Cubic and Higher Order Extensions of the Poincar\'e Algebra

In these lectures we study some possible higher order (of degree greater than two) extensions of the Poincar\'e algebra. We first give some general properties of Lie superalgebras with some emphasis on the supersymmetric extension of the Poincar\'e algebra or Supersymmetry. Some general features on the so-called Wess-Zumino model (the simplest field theory invariant under Supersymmetry) are then given. We further introduce an additional algebraic structure called Lie algebras of order F, which naturally comprise the concepts of ordinary Lie algebras and superalgebras. This structure enables us to define various non-trivial extensions of the Poincar\'e algebra. These extensions are studied more precisely in two different contexts. The first algebra we are considering is shown to be an (infinite dimensional) higher order extension of the Poincar\'e algebra in $(1+2)-$dimensions and turns out to induce a symmetry which connects relativistic anyons. The second extension we are studying is related to a specific finite dimensional Lie algebra of order three, which is a cubic extension of the Poincar\'e algebra in $D-$space-time dimensions. Invariant Lagrangians are constructed.Comment: Mini course given at the Workshop higher symmetries in physics, Madrid, Spain, November 6-8, 200

Combining Structure and Sequence Information Allows Automated Prediction of Substrate Specificities within Enzyme Families

An important aspect of the functional annotation of enzymes is not only the type of reaction catalysed by an enzyme, but also the substrate specificity, which can vary widely within the same family. In many cases, prediction of family membership and even substrate specificity is possible from enzyme sequence alone, using a nearest neighbour classification rule. However, the combination of structural information and sequence information can improve the interpretability and accuracy of predictive models. The method presented here, Active Site Classification (ASC), automatically extracts the residues lining the active site from one representative three-dimensional structure and the corresponding residues from sequences of other members of the family. From a set of representatives with known substrate specificity, a Support Vector Machine (SVM) can then learn a model of substrate specificity. Applied to a sequence of unknown specificity, the SVM can then predict the most likely substrate. The models can also be analysed to reveal the underlying structural reasons determining substrate specificities and thus yield valuable insights into mechanisms of enzyme specificity. We illustrate the high prediction accuracy achieved on two benchmark data sets and the structural insights gained from ASC by a detailed analysis of the family of decarboxylating dehydrogenases. The ASC web service is available at http://asc.informatik.uni-tuebingen.de/

Surface-Enhanced Nitrate Photolysis on Ice

Heterogeneous nitrates photolysis is the trigger for many chemical processes occurring in the polar boundary layer and is widely believed to occur in a quasi-liquid layer (QLL) at the surface of ice. The dipole forbidden character of the electronic transition relevant to boundary layer atmospheric chemistry and the small photolysis/photoproducts quantum yields in ice (and in water) may confer a significant enhancement and interfacial specificity to this important photochemical reaction at the surface of ice. Using amorphous solid water films at cryogenic temperatures as models for the disordered interstitial air/ice interface within the snowpack suppresses the diffusive uptake kinetics thereby prolonging the residence time of nitrate anions at the surface of ice. This approach allows their slow heterogeneous photolysis kinetics to be studied providing the first direct evidence that nitrates adsorbed onto the first molecular layer at the surface of ice are photolyzed more effectively than those dissolved within the bulk. Vibrational spectroscopy allows the ~3-fold enhancement in photolysis rates to be correlated with the nitrates’ distorted intramolecular geometry thereby hinting at the role played by the greater chemical heterogeneity in their solvation environment at the surface of ice than in the bulk. A simple 1D kinetic model suggests 1-that a 3(6)-fold enhancement in photolysis rate for nitrates adsorbed onto the ice surface could increase the photochemical NO[subscript 2] emissions from a 5(8) nm thick photochemically active interfacial layer by 30%(60)%, and 2-that 25%(40%) of the NO[subscript 2] photochemical emissions to the snowpack interstitial air are released from the top-most molecularly thin surface layer on ice. These findings may provide a new paradigm for heterogeneous (photo)chemistry at temperatures below those required for a QLL to form at the ice surface

Les Houches 2011: Physics at TeV Colliders New Physics Working Group Report

We present the activities of the "New Physics" working group for the "Physics at TeV Colliders" workshop (Les Houches, France, 30 May-17 June, 2011). Our report includes new agreements on formats for interfaces between computational tools, new tool developments, important signatures for searches at the LHC, recommendations for presentation of LHC search results, as well as additional phenomenological studies.Comment: 243 pages, report of the Les Houches 2011 New Physics Group; fix three figure

Long-read sequencing of diagnosis and post-therapy medulloblastoma reveals complex rearrangement patterns and epigenetic signatures

Cancer genomes harbor a broad spectrum of structural variants (SVs) driving tumorigenesis, a relevant subset of which escape discovery using short-read sequencing. We employed Oxford Nanopore Technologies (ONT) long-read sequencing in a paired diagnostic and post-therapy medulloblastoma to unravel the haplotype-resolved somatic genetic and epigenetic landscape. We assembled complex rearrangements, including a 1.55-Mbp chromothripsis event, and we uncover a complex SV pattern termed templated insertion (TI) thread, characterized by short (mostly <1 kb) insertions showing prevalent self-concatenation into highly amplified structures of up to 50 kbp in size. TI threads occur in 3% of cancers, with a prevalence up to 74% in liposarcoma, and frequent colocalization with chromothripsis. We also perform long-read-based methylome profiling and discover allele-specific methylation (ASM) effects, complex rearrangements exhibiting differential methylation, and differential promoter methylation in cancer-driver genes. Our study shows the advantage of long-read sequencing in the discovery and characterization of complex somatic rearrangements

Functional analysis of structural variants in single cells using Strand-seq

Somatic structural variants (SVs) are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We present a computational method, scNOVA, which uses Strand-seq to perform haplotype-aware integration of SV discovery and molecular phenotyping in single cells by using nucleosome occupancy to infer gene expression as a readout. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation, and consequences of SVs on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T cell acute lymphoblastic leukemia, which revealed c-Myb activation, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture, using a Notch inhibitor. By directly linking SVs to their functional effects, scNOVA enables systematic single-cell multiomic studies of structural variation in heterogeneous cell populations