TY - JOUR
AB - Cell shape and motility are primarily controlled by cellular mechanics. The attachment of the plasma membrane to the underlying actomyosin cortex has been proposed to be important for cellular processes involving membrane deformation. However, little is known about the actual function of membrane-to-cortex attachment (MCA) in cell protrusion formation and migration, in particular in the context of the developing embryo. Here, we use a multidisciplinary approach to study MCA in zebrafish mesoderm and endoderm (mesendoderm) germ layer progenitor cells, which migrate using a combination of different protrusion types, namely, lamellipodia, filopodia, and blebs, during zebrafish gastrulation. By interfering with the activity of molecules linking the cortex to the membrane and measuring resulting changes in MCA by atomic force microscopy, we show that reducing MCA in mesendoderm progenitors increases the proportion of cellular blebs and reduces the directionality of cell migration. We propose that MCA is a key parameter controlling the relative proportions of different cell protrusion types in mesendoderm progenitors, and thus is key in controlling directed migration during gastrulation.
AU - Diz Muñoz, Alba
AU - Krieg, Michael
AU - Bergert, Martin
AU - Ibarlucea Benitez, Itziar
AU - Müller, Daniel
AU - Paluch, Ewa
AU - Heisenberg, Carl-Philipp J
ID - 3790
IS - 11
JF - PLoS Biology
TI - Control of directed cell migration in vivo by membrane-to-cortex attachment
VL - 8
ER -
TY - CONF
AB - Recent progress in per-pixel object class labeling of natural images can be attributed to the use of multiple types of image features and sound statistical learning approaches. Within the latter, Conditional Random Fields (CRF) are prominently used for their ability to represent interactions between random variables. Despite their popularity in computer vision, parameter learning for CRFs has remained difficult, popular approaches being cross-validation and piecewise training.
In this work, we propose a simple yet expressive tree-structured CRF based on a recent hierarchical image segmentation method. Our model combines and weights multiple image features within a hierarchical representation and allows simple and efficient globally-optimal learning of ≈ 105 parameters. The tractability of our model allows us to pose and answer some of the open questions regarding parameter learning applying to CRF-based approaches. The key findings for learning CRF models are, from the obvious to the surprising, i) multiple image features always help, ii) the limiting dimension with respect to current models is the amount of training data, iii) piecewise training is competitive, iv) current methods for max-margin training fail for models with many parameters.
AU - Nowozin, Sebastian
AU - Gehler, Peter
AU - Lampert, Christoph
ID - 3793
TI - On parameter learning in CRF-based approaches to object class image segmentation
VL - 6316
ER -
TY - CONF
AB - We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time, and not all data modalities may be present at application time. Maximum covariance analysis, as a generalization of PCA, has many desirable properties, but its application to practical problems is limited by its need for perfectly paired data. We overcome this limitation by a latent variable approach that allows working with weakly paired data and is still able to efficiently process large datasets using standard numerical routines. The resulting weakly paired maximum covariance analysis often finds better representations than alternative methods, as we show in two exemplary tasks: texture discrimination and transfer learning.
AU - Lampert, Christoph
AU - Krömer, Oliver
ID - 3794
TI - Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning
VL - 6312
ER -
TY - CHAP
AB - The (apparent) contour of a smooth mapping from a 2-manifold to the plane, f: M → R2 , is the set of critical values, that is, the image of the points at which the gradients of the two component functions are linearly dependent. Assuming M is compact and orientable and measuring difference with the erosion distance, we prove that the contour is stable.
AU - Edelsbrunner, Herbert
AU - Morozov, Dmitriy
AU - Patel, Amit
ID - 3795
T2 - Topological Data Analysis and Visualization: Theory, Algorithms and Applications
TI - The stability of the apparent contour of an orientable 2-manifold
ER -
TY - JOUR
AB - We investigated temporal changes in hybridization and introgression between native red deer (Cervus elaphus) and invasive Japanese sika (Cervus nippon) on the Kintyre Peninsula, Scotland, over 15 years, through analysis of 1513 samples of deer at 20 microsatellite loci and a mtDNA marker. We found no evidence that either the proportion of recent hybrids, or the levels of introgression had changed over the study period. Nevertheless, in one population where the two species have been in contact since ∼1970, 44% of individuals sampled during the study were hybrids. This suggests that hybridization between these species can proceed fairly rapidly. By analysing the number of alleles that have introgressed from polymorphic red deer into the genetically homogenous sika population, we reconstructed the haplotypes of red deer alleles introduced by backcrossing. Five separate hybridization events could account for all the recently hybridized sika-like individuals found across a large section of the Peninsula. Although we demonstrate that low rates of F1 hybridization can lead to substantial introgression, the progress of hybridization and introgression appears to be unpredictable over the short timescales.
AU - Senn, Helen
AU - Goodman, Simon
AU - Swanson, Graeme
AU - Barton, Nicholas H
AU - Pemberton, Josephine
ID - 3604
IS - 5
JF - Molecular Ecology
TI - Investigating temporal changes in hybridisation and introgression between invasive sika (Cervus nippon) and native red deer (Cervus elaphus) on the Kintyre Peninsula, Scotland
VL - 19
ER -
TY - JOUR
AB - A recent paper by von Engelhardt et al. identifies a novel auxiliary subunit of native AMPARs, termedCKAMP44. Unlike other auxiliary subunits, CKAMP44 accelerates desensitization and prolongs recovery from desensitization. CKAMP44 is highly expressed in hippocampal dentate gyrus granule cells and decreases the paired-pulse ratio at perforant path input synapses. Thus, both principal and auxiliary AMPAR subunits control the time course of signaling at glutamatergic synapses.
AU - Guzmán, José
AU - Jonas, Peter M
ID - 3832
IS - 1
JF - Neuron
TI - Beyond TARPs: The growing list of auxiliary AMPAR subunits
VL - 66
ER -
TY - JOUR
AU - Jonas, Peter M
AU - Hefft, Stefan
ID - 3833
IS - 7
JF - The European Journal of Neuroscience
TI - GABA release at terminals of CCK-interneurons: synchrony, asynchrony and modulation by cannabinoid receptors (commentary on Ali & Todorova)
VL - 31
ER -
TY - JOUR
AB - Background
The chemical master equation (CME) is a system of ordinary differential equations that describes the evolution of a network of chemical reactions as a stochastic process. Its solution yields the probability density vector of the system at each point in time. Solving the CME numerically is in many cases computationally expensive or even infeasible as the number of reachable states can be very large or infinite. We introduce the sliding window method, which computes an approximate solution of the CME by performing a sequence of local analysis steps. In each step, only a manageable subset of states is considered, representing a "window" into the state space. In subsequent steps, the window follows the direction in which the probability mass moves, until the time period of interest has elapsed. We construct the window based on a deterministic approximation of the future behavior of the system by estimating upper and lower bounds on the populations of the chemical species.
Results
In order to show the effectiveness of our approach, we apply it to several examples previously described in the literature. The experimental results show that the proposed method speeds up the analysis considerably, compared to a global analysis, while still providing high accuracy.
Conclusions
The sliding window method is a novel approach to address the performance problems of numerical algorithms for the solution of the chemical master equation. The method efficiently approximates the probability distributions at the time points of interest for a variety of chemically reacting systems, including systems for which no upper bound on the population sizes of the chemical species is known a priori.
AU - Wolf, Verena
AU - Goel, Rushil
AU - Mateescu, Maria
AU - Henzinger, Thomas A
ID - 3834
IS - 42
JF - BMC Systems Biology
TI - Solving the chemical master equation using sliding windows
VL - 4
ER -
TY - CONF
AB - We present a numerical approximation technique for the analysis of continuous-time Markov chains that describe net- works of biochemical reactions and play an important role in the stochastic modeling of biological systems. Our approach is based on the construction of a stochastic hybrid model in which certain discrete random variables of the original Markov chain are approximated by continuous deterministic variables. We compute the solution of the stochastic hybrid model using a numerical algorithm that discretizes time and in each step performs a mutual update of the transient prob- ability distribution of the discrete stochastic variables and the values of the continuous deterministic variables. We im- plemented the algorithm and we demonstrate its usefulness and efficiency on several case studies from systems biology.
AU - Henzinger, Thomas A
AU - Mateescu, Maria
AU - Mikeev, Linar
AU - Wolf, Verena
ID - 3838
TI - Hybrid numerical solution of the chemical master equation
ER -
TY - CONF
AB - We present a loop property generation method for loops iterating over multi-dimensional arrays. When used on matrices, our method is able to infer their shapes (also called types), such as upper-triangular, diagonal, etc. To gen- erate loop properties, we first transform a nested loop iterating over a multi- dimensional array into an equivalent collection of unnested loops. Then, we in- fer quantified loop invariants for each unnested loop using a generalization of a recurrence-based invariant generation technique. These loop invariants give us conditions on matrices from which we can derive matrix types automatically us- ing theorem provers. Invariant generation is implemented in the software package Aligator and types are derived by theorem provers and SMT solvers, including Vampire and Z3. When run on the Java matrix package JAMA, our tool was able to infer automatically all matrix types describing the matrix shapes guaranteed by JAMA’s API.
AU - Henzinger, Thomas A
AU - Hottelier, Thibaud
AU - Kovács, Laura
AU - Voronkov, Andrei
ID - 3839
TI - Invariant and type inference for matrices
VL - 5944
ER -