1,720,992 research outputs found
Investigating Local Evolutions in Dynamical Probabilistic P Systems
No full textWe present a simulation tool to predict the behavior of single regions in
dynamical probabilistic P systems with reduced size, that is, membrane systems
with probability values associated to rules that dynamically change during the
evolution, where the number of objects whose evolution is analyzed is not
greater than 2. The tool is based on the construction of a grid over the phase
space of a region, which is then used to evaluate the mean displacement of each
multiset in the grid and to build the vector field of that region. As a
consequence, we can predict the local evolutions (i.e., the behavior of the
system inside each membrane) for all possible choices of initial multisets. We
show some applications of this method to investigate the dynamics of two simple
abstract toy-systems and of the Lotka-Volterra model
Modelling metapopulations with stochastic membrane systems
No full textMetapopulations, or multi-patch systems, are models describing the interactions and the behavior of populations living in fragmented habitats. Dispersal, persistence and extinction are some of the characteristics of interest in ecological studies of metapopulations. In this paper, we propose a novel method to analyze metapopulations, which is based on a discrete and stochastic modelling framework in the area of Membrane Computing. New structural features of membrane systems, necessary to appropriately describe a multi-patch system, are introduced, such as the reduction of the maximal parallel consumption of objects, the spatial arrangement of membranes and the stochastic creation of objects. The role of the additional features, their meaning for a metapopulation model and the emergence of relevant behaviors are then investigated by means of stochastic simulations. Conclusive remarks and ideas for future research are finally presented
Effects of stochastic fluctuations on the coordination of flagella in bacterial chemotaxis
No full textChemotaxis allows bacteria to respond and
adapt to the environment, by tuning tumbling and running motions due to the rotation of their flagella. We defined a model of chemotaxis and performed stochastic simulations of the dynamics of a pivotal protein, CheYp. These results allowed
to compare the mean time of running, tumbling and adaptation with respect to different numbers of flagella. Our results
suggest that the interplay between stochastic fluctuations of CheYp and the synchronization of flagella might represent a relevant component for the proper functionality of chemotaxi
BioSimWare : a simulation environment for stochastic modeling of complex biological systems
No full text(No abstract is available for this article.
Computing with energy and chemical reactions
No full textTaking inspiration from some laws of Nature — energy transformation and chemical reactions — we consider two different paradigms of computation in the framework of Membrane Computing. We first study the computational power of energy-based P systems, a model of membrane systems where a fixed amount of energy is associated with each object and the rules transform objects by manipulating their energy. We show that if we assign local priorities to the rules, then energy-based P systems are as powerful as Turing machines; otherwise, they can be simulated by vector addition systems, and hence are not universal. Then, we consider stochastic membrane systems where computations are performed through chemical networks. We show how molecular species and chemical reactions can be used to describe and simulate the functioning of Fredkin gates and circuits. We conclude the paper with some research topics related to computing with energy-based P systems and with chemical reactions
Seasonal variance in P system models for metapopulations
No full textMetapopulations are ecological models describing the interactions and the behavior of populations living in fragmented
habitats. In this paper, metapopulations are modelled by means of dynamical probabilistic P systems, where additional structural
features have been defined (e.g., a weighted graph associated with the membrane structure and the reduction of maximal parallelism).
In particular, we investigate the influence of stochastic and periodic resource feeding processes, owing to seasonal variance, on emergent metapopulation dynamics
Dynamical probabilistic P systems
No full textDynamical probabilistic P systems are discrete, stochastic, and parallel devices, where the probability values associated with the rules change during the evolution of the system. These systems are proposed as a novel approach to the analysis and simulation of the behavior of complex systems. We introduce all necessary definitions of these systems and of their dynamical aspects, we describe the functioning of the parallel and stochastic algorithm used in computer simulation, and evaluate its time complexity. Finally, we show some applications of dynamical probabilistic P systems for the investigation of the dynamics of the Lotka-Volterra system and of metapopulation systems
An in silico investigation of different regulation mechanisms of the bacterial second messenger c-di-GMP
No full textNo abstract available
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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