53,439 research outputs found
A Formal Methods Approach to Pattern Synthesis in Reaction Diffusion Systems
We propose a technique to detect and generate patterns in a network of
locally interacting dynamical systems. Central to our approach is a novel
spatial superposition logic, whose semantics is defined over the quad-tree of a
partitioned image. We show that formulas in this logic can be efficiently
learned from positive and negative examples of several types of patterns. We
also demonstrate that pattern detection, which is implemented as a model
checking algorithm, performs very well for test data sets different from the
learning sets. We define a quantitative semantics for the logic and integrate
the model checking algorithm with particle swarm optimization in a
computational framework for synthesis of parameters leading to desired patterns
in reaction-diffusion systems
Turing instabilities in general systems
We present necessary and sufficient conditions on the stability matrix of a general n(S2)-dimensional reaction-diffusion system which guarantee that its uniform steady state can undergo a Turing bifurcation. The necessary (kinetic) condition, requiring that the system be composed of an unstable (or activator) and a stable (or inhibitor) subsystem, and the sufficient condition of sufficiently rapid inhibitor diffusion relative to the activator subsystem are established in three theorems which form the core of our results. Given the possibility that the unstable (activator) subsystem involves several species (dimensions), we present a classification of the analytically deduced Turing bifurcations into p (1 h p h (n m 1)) different classes. For n = 3 dimensions we illustrate numerically that two types of steady Turing pattern arise in one spatial dimension in a generic reaction-diffusion system. The results confirm the validity of an earlier conjecture [12] and they also characterise the class of so-called strongly stable matrices for which only necessary conditions have been known before [23, 24]. One of the main consequences of the present work is that biological morphogens, which have so far been expected to be single chemical species [1-9], may instead be composed of two or more interacting species forming an unstable subsystem
The Kinetic Basis of Self-Organized Pattern Formation
In his seminal paper on morphogenesis (1952), Alan Turing demonstrated that
different spatio-temporal patterns can arise due to instability of the
homogeneous state in reaction-diffusion systems, but at least two species are
necessary to produce even the simplest stationary patterns. This paper is aimed
to propose a novel model of the analog (continuous state) kinetic automaton and
to show that stationary and dynamic patterns can arise in one-component
networks of kinetic automata. Possible applicability of kinetic networks to
modeling of real-world phenomena is also discussed.Comment: 8 pages, submitted to the 14th International Conference on the
Synthesis and Simulation of Living Systems (Alife 14) on 23.03.2014, accepted
09.05.201
Agent-Based Modeling of Intracellular Transport
We develop an agent-based model of the motion and pattern formation of
vesicles. These intracellular particles can be found in four different modes of
(undirected and directed) motion and can fuse with other vesicles. While the
size of vesicles follows a log-normal distribution that changes over time due
to fusion processes, their spatial distribution gives rise to distinct
patterns. Their occurrence depends on the concentration of proteins which are
synthesized based on the transcriptional activities of some genes. Hence,
differences in these spatio-temporal vesicle patterns allow indirect
conclusions about the (unknown) impact of these genes.
By means of agent-based computer simulations we are able to reproduce such
patterns on real temporal and spatial scales. Our modeling approach is based on
Brownian agents with an internal degree of freedom, , that represents
the different modes of motion. Conditions inside the cell are modeled by an
effective potential that differs for agents dependent on their value .
Agent's motion in this effective potential is modeled by an overdampted
Langevin equation, changes of are modeled as stochastic transitions
with values obtained from experiments, and fusion events are modeled as
space-dependent stochastic transitions. Our results for the spatio-temporal
vesicle patterns can be used for a statistical comparison with experiments. We
also derive hypotheses of how the silencing of some genes may affect the
intracellular transport, and point to generalizations of the model
Easy and green route towards nanostructured ZnO as active sensing material with unexpected H2S dosimeter-type behaviour
Nanostructured ZnO particles were prepared through a straightforward, quick and low\u2010temperature synthesis route involving coprecipitation of the metal precursor salts with oxalic acid, followed by hydrothermal treatment at 135 or 160 \ub0C. The synthesised nanostructured powders were thoroughly characterised by a wide array of analytical techniques from the morphological (Scanning Electron Microscopy \u2013SEM\u2010, Transmission Electron Microscopy \u2010TEM\u2010, Energy\u2010dispersive X\u2010ray Spectroscopy \u2010EDXS\u2010), structural (Powder X\u2010Ray Diffraction \u2010PXRD\u2010, Selected Area Electron Diffraction \u2010SAED\u2010), compositional (X\u2010ray Photoelectron Spectroscopy \u2010XPS\u2010) and physical (thermal stability) point of view. As far as functional applications are concerned, the powders were tested as gas sensor materials for H2S detection. Thereby these ZnO particles show unexpected gas dosimeter behaviour at 150 \ub0C. Based on these observations and on a comparison with literature a new model for the interaction of ZnO nanostructures with H2S is proposed
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