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Relevant bibliographies by topics / Gillespie simulations

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Journal articles

Academic literature on the topic 'Gillespie simulations'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Gillespie simulations"

1

Rajput, Nikhil Kumar. "Gillespie algorithm and diffusion approximation based on Monte Carlo simulation for innovation diffusion: A comparative study." Monte Carlo Methods and Applications 25, no. 3 (2019): 209–15. http://dx.doi.org/10.1515/mcma-2019-2040.

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Abstract Monte Carlo simulations have been utilized to make a comparative study between diffusion approximation (DA) and the Gillespie algorithm and its dependence on population in the information diffusion model. Diffusion approximation is one of the widely used approximation methods which have been applied in queuing systems, biological systems and other fields. The Gillespie algorithm, on the other hand, is used for simulating stochastic systems. In this article, the validity of diffusion approximation has been studied in relation to the Gillespie algorithm for varying population sizes. It

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2

Kierzek, A. M. "STOCKS: STOChastic Kinetic Simulations of biochemical systems with Gillespie algorithm." Bioinformatics 18, no. 3 (2002): 470–81. http://dx.doi.org/10.1093/bioinformatics/18.3.470.

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3

Alfonso, L., G. B. Raga, and D. Baumgardner. "Monte Carlo simulations of two-component drop growth by stochastic coalescence." Atmospheric Chemistry and Physics Discussions 8, no. 2 (2008): 7289–313. http://dx.doi.org/10.5194/acpd-8-7289-2008.

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Abstract. The evolution of two-dimensional drop distributions is simulated in this study using a Monte Carlo method.~The stochastic algorithm of Gillespie (1976) for chemical reactions in the formulation proposed by Laurenzi et al. (2002) was used to simulate the kinetic behavior of the drop population. Within this framework species are defined as droplets of specific size and aerosol composition. The performance of the algorithm was checked by comparing the numerical with the analytical solutions found by Lushnikov (1975). Very good agreement was observed between the Monte Carlo simulations a

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4

Martinecz, Antal, Fabrizio Clarelli, Sören Abel, and Pia Abel zur Wiesch. "Reaction Kinetic Models of Antibiotic Heteroresistance." International Journal of Molecular Sciences 20, no. 16 (2019): 3965. http://dx.doi.org/10.3390/ijms20163965.

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Bacterial heteroresistance (i.e., the co-existence of several subpopulations with different antibiotic susceptibilities) can delay the clearance of bacteria even with long antibiotic exposure. Some proposed mechanisms have been successfully described with mathematical models of drug-target binding where the mechanism’s downstream of drug-target binding are not explicitly modeled and subsumed in an empirical function, connecting target occupancy to antibiotic action. However, with current approaches it is difficult to model mechanisms that involve multi-step reactions that lead to bacterial kil

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5

Chen-Charpentier, Benito. "Stochastic Modeling of Plant Virus Propagation with Biological Control." Mathematics 9, no. 5 (2021): 456. http://dx.doi.org/10.3390/math9050456.

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Plants are vital for man and many species. They are sources of food, medicine, fiber for clothes and materials for shelter. They are a fundamental part of a healthy environment. However, plants are subject to virus diseases. In plants most of the virus propagation is done by a vector. The traditional way of controlling the insects is to use insecticides that have a negative effect on the environment. A more environmentally friendly way to control the insects is to use predators that will prey on the vector, such as birds or bats. In this paper we modify a plant-virus propagation model with del

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6

Alfonso, L., G. B. Raga, and D. Baumgardner. "Monte Carlo simulations of two-component drop growth by stochastic coalescence." Atmospheric Chemistry and Physics 9, no. 4 (2009): 1241–51. http://dx.doi.org/10.5194/acp-9-1241-2009.

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Abstract. The evolution of two-dimensional drop distributions is simulated in this study using a Monte Carlo method. The stochastic algorithm of Gillespie (1976) for chemical reactions in the formulation proposed by Laurenzi et al. (2002) was used to simulate the kinetic behavior of the drop population. Within this framework, species are defined as droplets of specific size and aerosol composition. The performance of the algorithm was checked by a comparison with the analytical solutions found by Lushnikov (1975) and Golovin (1963) and with finite difference solutions of the two-component kine

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7

Chang, Qiang, Yang Lu, and Donghui Quan. "Accelerated Gillespie Algorithm for Gas–Grain Reaction Network Simulations Using Quasi-steady-state Assumption." Astrophysical Journal 851, no. 1 (2017): 68. http://dx.doi.org/10.3847/1538-4357/aa99d9.

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8

Jo, Yeji, Kyusik Mun, Yeonjoo Jeong, et al. "A Poisson Process Generator Based on Multiple Thermal Noise Amplifiers for Parallel Stochastic Simulation of Biochemical Reactions." Electronics 11, no. 7 (2022): 1039. http://dx.doi.org/10.3390/electronics11071039.

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In this paper, we propose a novel Poisson process generator that uses multiple thermal noise amplifiers (TNAs) as a source of randomness and controls its event rate via a frequency-locked loop (FLL). The increase in the number of TNAs extends the effective bandwidth of amplified thermal noise and hence enhances the maximum event rate the proposed architecture can generate. Verilog-A simulation of the proposed Poisson process generator shows that its maximum event rate can be increased by a factor of 26.5 when the number of TNAs increases from 1 to 10. In order to realize parallel stochastic si

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9

Iwuchukwu, Edward Uchechukwu, and Ardson dos Santos Junior Vianna. "Stochastic Modelling and Simulation of Free Radical Polymerization of Styrene in Microchannels using a Hybrid Gillespie Algorithm." Journal of Engineering and Exact Sciences 9, no. 1 (2023): 15327–01. http://dx.doi.org/10.18540/jcecvl9iss1pp15327-01e.

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Most recently, the production of polystyrene by Free Radical Polymerization (FRP) via microchannels has been a subject of core interest due to the efficiency of a micro-or milli-reactor brings. In addition, especially in pilot experimentations, a micro or milli-reactor has been known widely to be efficient in monitoring the microstructural end-use features or properties of the polymer as the chain propagates and ultimately terminates. However, the limitations posed by using micro or milli-reactors in process intensification such as clogging of pores can be a bottleneck when tracking the common

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10

Matzko, Richard Oliver, Laurentiu Mierla, and Savas Konur. "Novel Ground-Up 3D Multicellular Simulators for Synthetic Biology CAD Integrating Stochastic Gillespie Simulations Benchmarked with Topologically Variable SBML Models." Genes 14, no. 1 (2023): 154. http://dx.doi.org/10.3390/genes14010154.

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The elevation of Synthetic Biology from single cells to multicellular simulations would be a significant scale-up. The spatiotemporal behavior of cellular populations has the potential to be prototyped in silico for computer assisted design through ergonomic interfaces. Such a platform would have great practical potential across medicine, industry, research, education and accessible archiving in bioinformatics. Existing Synthetic Biology CAD systems are considered limited regarding population level behavior, and this work explored the in silico challenges posed from biological and computationa

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