@article{3790,
abstract = {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.},
author = {Diz Muñoz, Alba and Krieg, Michael and Bergert, Martin and Ibarlucea Benitez, Itziar and Müller, Daniel and Paluch, Ewa and Heisenberg, Carl-Philipp J},
journal = {PLoS Biology},
number = {11},
publisher = {Public Library of Science},
title = {{Control of directed cell migration in vivo by membrane-to-cortex attachment}},
doi = {10.1371/journal.pbio.1000544},
volume = {8},
year = {2010},
}
@inbook{3795,
abstract = {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.},
author = {Edelsbrunner, Herbert and Morozov, Dmitriy and Patel, Amit},
booktitle = {Topological Data Analysis and Visualization: Theory, Algorithms and Applications},
pages = {27 -- 42},
publisher = {Springer},
title = {{The stability of the apparent contour of an orientable 2-manifold}},
doi = {10.1007/978-3-642-15014-2_3},
year = {2010},
}
@article{3834,
abstract = {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.},
author = {Wolf, Verena and Goel, Rushil and Mateescu, Maria and Henzinger, Thomas A},
journal = {BMC Systems Biology},
number = {42},
pages = {1 -- 19},
publisher = {BioMed Central},
title = {{Solving the chemical master equation using sliding windows}},
doi = {10.1186/1752-0509-4-42},
volume = {4},
year = {2010},
}
@inproceedings{3839,
abstract = {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.},
author = {Henzinger, Thomas A and Hottelier, Thibaud and Kovács, Laura and Voronkov, Andrei},
location = {Madrid, Spain},
pages = {163 -- 179},
publisher = {Springer},
title = {{Invariant and type inference for matrices}},
doi = {10.1007/978-3-642-11319-2_14},
volume = {5944},
year = {2010},
}
@inproceedings{3853,
abstract = {Quantitative languages are an extension of boolean languages that assign to each word a real number. Mean-payoff automata are finite automata with numerical weights on transitions that assign to each infinite path the long-run average of the transition weights. When the mode of branching of the automaton is deterministic, nondeterministic, or alternating, the corresponding class of quantitative languages is not robust as it is not closed under the pointwise operations of max, min, sum, and numerical complement. Nondeterministic and alternating mean-payoff automata are not decidable either, as the quantitative generalization of the problems of universality and language inclusion is undecidable. We introduce a new class of quantitative languages, defined by mean-payoff automaton expressions, which is robust and decidable: it is closed under the four pointwise operations, and we show that all decision problems are decidable for this class. Mean-payoff automaton expressions subsume deterministic meanpayoff automata, and we show that they have expressive power incomparable to nondeterministic and alternating mean-payoff automata. We also present for the first time an algorithm to compute distance between two quantitative languages, and in our case the quantitative languages are given as mean-payoff automaton expressions.},
author = {Chatterjee, Krishnendu and Doyen, Laurent and Edelsbrunner, Herbert and Henzinger, Thomas A and Rannou, Philippe},
location = {Paris, France},
pages = {269 -- 283},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
title = {{Mean-payoff automaton expressions}},
doi = {10.1007/978-3-642-15375-4_19},
volume = {6269},
year = {2010},
}