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Journal articles on the topic 'Gillespie simulations'

Author: Grafiati

Published: 6 September 2023

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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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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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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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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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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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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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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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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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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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ZAIKIN, A., A. K. MITRA, D. S. GOLDOBIN, and J. KURTHS. "INFLUENCE OF TRANSPORT RATES ON THE PROTEIN DEGRADATION BY PROTEASOMES." Biophysical Reviews and Letters 01, no. 04 (2006): 375–86. http://dx.doi.org/10.1142/s1793048006000355.

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We discuss how translocation properties of the 20S proteasome influence its length distribution, one of its most important feature for the normal functioning of the immune system. For this we consider a simple one-channel proteasome model and assume that the protein transport depends significantly on the length of a protein located inside the proteasome chamber. Using the master equation approach we show analytically that the length distribution with one dominating peak, observed in the experiments, can be achieved if the transport rate function is in a certain relation with cleavage probabili

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Albert, Jaroslav. "A Hybrid of the Chemical Master Equation and the Gillespie Algorithm for Efficient Stochastic Simulations of Sub-Networks." PLOS ONE 11, no. 3 (2016): e0149909. http://dx.doi.org/10.1371/journal.pone.0149909.

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WU, PEI-JUNG, CHOU-CHING K. LIN, and MING-SHAUNG JU. "AXIAL-SYMMETRIC MODELING AND KINEMATIC ANALYSIS OF SPREADING OF SPARSELY CULTURED FIBROBLASTS." Journal of Mechanics in Medicine and Biology 13, no. 04 (2013): 1350062. http://dx.doi.org/10.1142/s0219519413500620.

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Cell spreading plays an important role in the modulation of physiological functions such as inflammation and cancer metastasis. The Brownian ratchet model and Bell's model have been used to simulate actin dynamics and bond kinetics for focal adhesion dynamics, respectively. In the present study, these models were modified and two additional subcellular mechanisms, integrin and myosin kinetics, were incoporated. An integrin recruitment function was introduced to determine the size of a focal adhesion associated with the substrate stiffness. The relationship between myosin concentration and the

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Thakur, Bhumika, and Hildegard Meyer-Ortmanns. "Controlling the Mean Time to Extinction in Populations of Bacteria." Entropy 25, no. 5 (2023): 755. http://dx.doi.org/10.3390/e25050755.

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Populations of ecological systems generally have demographic fluctuations due to birth and death processes. At the same time, they are exposed to changing environments. We studied populations composed of two phenotypes of bacteria and analyzed the impact that both types of fluctuations have on the mean time to extinction of the entire population if extinction is the final fate. Our results are based on Gillespie simulations and on the WKB approach applied to classical stochastic systems, here in certain limiting cases. As a function of the frequency of environmental changes, we observe a non-m

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Slatkin, Montgomery. "Balancing Selection at Closely Linked, Overdominant Loci in a Finite Population." Genetics 154, no. 3 (2000): 1367–78. http://dx.doi.org/10.1093/genetics/154.3.1367.

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Abstract High levels of allelic diversity and strong linkage disequilibrium are found in the major histocompatibility (MHC) system in humans and other vertebrates. This article proposes several descriptive statistics that quantify the extent and pattern of strong linkage disequilibrium between pairs of highly polymorphic loci. It also develops an approximate analytic theory incorporating the effects of balancing selection, mutation, recombination, and genetic drift at two closely linked loci and compares the theoretical predictions with published surveys of the MHC class II loci, DQA1 and DQB1

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Marwa, Yohana Maiga, Isambi Sailon Mbalawata, and Samuel Mwalili. "Continuous Time Markov Chain Model for Cholera Epidemic Transmission Dynamics." International Journal of Statistics and Probability 8, no. 3 (2019): 32. http://dx.doi.org/10.5539/ijsp.v8n3p32.

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This paper is concern with modeling cholera epidemic. Despite the advances made in understanding this disease and its treatment, cholera continues to be a major public health problem in many countries. Deterministic and stochastic models emerged in modeling of cholera epidemic, in order to understand the mechanism by which cholera disease spread, conditions for cholera disease to have minor and large outbreaks. We formulate a continuous time Markov chain model for cholera epidemic transmission from the deterministic model. The basic reproduction number (R0) and the extinction thresholds of cor

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17

Wen, Chengyuan, Roy Odle, and Shengfeng Cheng. "Molecular Weight Distribution of Branched Polymers: Comparison between Monte Carlo Simulation and Flory-Stockmayer Theory." Polymers 15, no. 7 (2023): 1791. http://dx.doi.org/10.3390/polym15071791.

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It is challenging to predict the molecular weight distribution (MWD) for a polymer with a branched architecture, though such information will significantly benefit the design and development of branched polymers with desired properties and functions. A Monte Carlo (MC) simulation method based on the Gillespie algorithm is developed to quickly compute the MWD of branched polymers formed through step-growth polymerization, with a branched polyetherimide from two backbone monomers (4,4′-bisphenol A dianhydride and m-phenylenediamine), a chain terminator (phthalic anhydride), and a branching agent

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18

Yates, Christian A., Matthew J. Ford, and Richard L. Mort. "A Multi-stage Representation of Cell Proliferation as a Markov Process." Bulletin of Mathematical Biology 79, no. 12 (2017): 2905–28. http://dx.doi.org/10.1007/s11538-017-0356-4.

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Abstract The stochastic simulation algorithm commonly known as Gillespie’s algorithm (originally derived for modelling well-mixed systems of chemical reactions) is now used ubiquitously in the modelling of biological processes in which stochastic effects play an important role. In well-mixed scenarios at the sub-cellular level it is often reasonable to assume that times between successive reaction/interaction events are exponentially distributed and can be appropriately modelled as a Markov process and hence simulated by the Gillespie algorithm. However, Gillespie’s algorithm is routinely appl

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Ssematimba, Amos, Sasidhar Malladi, Peter Bonney, et al. "African swine fever detection and transmission estimates using homogeneous versus heterogeneous model formulation in stochastic simulations within pig premises." Open Veterinary Journal 12, no. 6 (2022): 787. http://dx.doi.org/10.5455/ovj.2022.v12.i6.2.

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Background: African swine fever (ASF) is one of the most important foreign animal diseases to the U.S. swine industry. Stakeholders in the swine production sector are on high alert as they witness the devastation of ongoing outbreaks in some of its most important trade partner countries. Efforts to improve preparedness for ASF outbreak management are proceeding in earnest and mathematical modeling is an integral part of these efforts. Aim: This study aimed to assess the impact on within-herd transmission dynamics of African swine fever (ASF) when the models used to simulate transmission assume

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GIL, Jarosław, and Andrzej POLAŃSKI. "APPLICATION OF GILLESPIE ALGORITHM FOR SIMULATING EVOLUTION OF FITNESS OF MICROBIAL POPULATION." Applied Computer Science 18, no. 4 (2022): 5–15. http://dx.doi.org/10.35784/acs-2022-25.

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In this study we present simulation system based on Gillespie algorithm for generating evolutionary events in the evolution scenario of microbial population. We present Gillespie simulation system adjusted to reproducing experimental data obtained in barcoding studies – experimental techniques in microbiology allowing tracing microbial populations with very high resolution. Gillespie simulation engine is constructed by defining its state vector and rules for its modifications. In order to efficiently simulate barcoded experiment by using Gillespie algorithm we provide modification - binning ce

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Smyrnova-Trybulska, Eugenia. "EVOLUTION OF MEDIA COMPETENCES." OPEN EDUCATIONAL E-ENVIRONMENT OF MODERN UNIVERSITY, SPECIAL EDITION (2019): 77–92. http://dx.doi.org/10.28925/2414-0325.2019s7.

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This article presents issues related to the evolution of media and media competences, a review and analysis of selected historical, technological and educational conditions in the context of the development of digital technologies. A comparison is also made between digital, information and media competences, current development trends and future trends. The differences and requirements between qualified media users and qualified users of information technology are becoming less and less distinct. The 3 generations of Media education - 1.0, 2.0 and 3.0 were described. The main purpose of media

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22

Xing, Fei, Yi Ping Yao, Zhi Wen Jiang, and Bing Wang. "Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions." Advanced Materials Research 345 (September 2011): 104–12. http://dx.doi.org/10.4028/www.scientific.net/amr.345.104.

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To date, discrete event stochastic simulations of large scale biological reaction systems are extremely compute-intensive and time-consuming. Besides, it has been widely accepted that spatial factor plays a critical role in the dynamics of most biological reaction systems. The NSM (the Next Sub-Volume Method), a spatial variation of the Gillespie’s stochastic simulation algorithm (SSA), has been proposed for spatially stochastic simulation of those systems. While being able to explore high degree of parallelism in systems, NSM is inherently sequential, which still suffers from the problem of l

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Pietras, Bastian, Valentin Schmutz, and Tilo Schwalger. "Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity." PLOS Computational Biology 18, no. 12 (2022): e1010809. http://dx.doi.org/10.1371/journal.pcbi.1010809.

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Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity suggest metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model

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Dinh, Khanh, Roman Jaksik, Marek Kimmel, and Seth J. Corey. "Predicting Minimal Residual Disease in Acute Myeloid Leukemia through Stochastic Modeling of Clonality." Blood 134, Supplement_1 (2019): 1448. http://dx.doi.org/10.1182/blood-2019-127457.

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Background. Relapse affects about 50% of AML patients who achieved remission after treatment, and the prognosis of relapsed AML is poor. Current evidence has shown that in many patients, mutations giving rise to relapse are already present at diagnosis and remain in small numbers in remission, defined as the minimal residual disease (MRD) [1]. Chemoresistant clones contributing to relapse of the disease arise from minimal residual disease (MRD) rather than resulting from newly acquired mutations during or after chemotherapy. MRD is the presence of measurable leukemic cells using non-morphologi

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Klann, Michael, Arnab Ganguly, and Heinz Koeppl. "Hybrid spatial Gillespie and particle tracking simulation." Bioinformatics 28, no. 18 (2012): i549—i555. http://dx.doi.org/10.1093/bioinformatics/bts384.

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Chen, Xuyou, Ruomei Wang, Shujin Lin, Fei Wang, and Xiaonan Luo. "Thrombus Clotting Simulation Method Based on the Gillespie Method." Journal of Computer-Aided Design & Computer Graphics 31, no. 8 (2019): 1301. http://dx.doi.org/10.3724/sp.j.1089.2019.17570.

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ANDRECUT, M., and S. A. KAUFFMAN. "MEAN FIELD MODEL OF THE GENETIC TOGGLE SWITCH." International Journal of Modern Physics B 20, no. 29 (2006): 4947–63. http://dx.doi.org/10.1142/s021797920603576x.

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In this paper we discuss a noisy mean field model for the genetic toggle switch. We show that this model approximates very well the characteristics of the system, observed using the exact Gillespie stochastic simulation algorithm. Also, we show that the system can be made exponentially stable depending on reaction parameters.

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Kim, Young Jin, Jae Jun Lee, and Julian Lee. "Gillespie Simulation of Rare Events in a Genetic Regulatory Network." Journal of the Korean Physical Society 74, no. 9 (2019): 907–11. http://dx.doi.org/10.3938/jkps.74.907.

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29

Altıntan, Derya, Vi̇lda Purutçuoğlu, and Ömür Uğur. "Impulsive Expressions in Stochastic Simulation Algorithms." International Journal of Computational Methods 15, no. 01 (2017): 1750075. http://dx.doi.org/10.1142/s021987621750075x.

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Jumps can be seen in many natural processes. Classical deterministic modeling approach explains the dynamical behavior of such systems by using impulsive differential equations. This modeling strategy assumes that the dynamical behavior of the whole system is deterministic, continuous, and it adds jumps to the state vector at certain times. Although deterministic approach is satisfactory in many cases, it is a well-known fact that stochasticity or uncertainty has crucial importance for dynamical behavior of many others. In this study, we propose to include this abrupt change in the stochastic

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Tangherloni, Andrea, Marco S. Nobile, Paolo Cazzaniga, Daniela Besozzi, and Giancarlo Mauri. "Gillespie’s Stochastic Simulation Algorithm on MIC coprocessors." Journal of Supercomputing 73, no. 2 (2016): 676–86. http://dx.doi.org/10.1007/s11227-016-1778-8.

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Vestergaard, Christian L., and Mathieu Génois. "Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks." PLOS Computational Biology 11, no. 10 (2015): e1004579. http://dx.doi.org/10.1371/journal.pcbi.1004579.

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Ferlic, Jeremy, Jiantao Shi, Thomas O. McDonald, and Franziska Michor. "DIFFpop: a stochastic computational approach to simulate differentiation hierarchies with single cell barcoding." Bioinformatics 35, no. 19 (2019): 3849–51. http://dx.doi.org/10.1093/bioinformatics/btz074.

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Abstract Summary DIFFpop is an R package designed to simulate cellular differentiation hierarchies using either exponentially-expanding or fixed population sizes. The software includes functionalities to simulate clonal evolution due to the emergence of driver mutations under the infinite-allele assumption as well as options for simulation and analysis of single cell barcoding and labeling data. The software uses the Gillespie Stochastic Simulation Algorithm and a modification of expanding or fixed-size stochastic process models expanded to a large number of cell types and scenarios. Availabil

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Andrecut, M., and S. A. Kauffman. "Noise in Genetic Toggle Switch Models." Journal of Integrative Bioinformatics 3, no. 1 (2006): 63–77. http://dx.doi.org/10.1515/jib-2006-23.

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Summary In this paper we study the intrinsic noise effect on the switching behavior of a simple genetic circuit corresponding to the genetic toggle switch model. The numerical results obtained from a noisy mean-field model are compared to those obtained from the stochastic Gillespie simulation of the corresponding system of chemical reactions. Our results show that by using a two step reaction approach for modeling the transcription and translation processes one can make the system to lock in one of the steady states for exponentially long times.

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Lu, T., D. Volfson, L. Tsimring, and J. Hasty. "Cellular growth and division in the Gillespie algorithm." Systems Biology 1, no. 1 (2004): 121–28. http://dx.doi.org/10.1049/sb:20045016.

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Suderman, Ryan, Eshan D. Mitra, Yen Ting Lin, Keesha E. Erickson, Song Feng, and William S. Hlavacek. "Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations." Bulletin of Mathematical Biology 81, no. 8 (2018): 2822–48. http://dx.doi.org/10.1007/s11538-018-0418-2.

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Xie, Kewei, and Qian Wang. "Intracellular transport by motor proteins with the same directionality." JUSTC 53, no. 3 (2023): 0307. http://dx.doi.org/10.52396/justc-2022-0140.

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Active intracellular transport is mainly performed by a group of special nanomachines called motor proteins. During transport, cooperation between motor proteins significantly influences important transport features, such as distance and velocity. To understand this mechanism, we combine Gillespie simulation and analytical derivation to demonstrate how the mechanical properties of a single motor influence the cooperation between multiple motors, further regulating the transport distance. In addition, we build a deep learning model to help us quickly obtain the motor parameters. Our results she

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Vestergaard, Christian L., and Mathieu Génois. "Correction: Temporal Gillespie algorithm: Fast simulation of contagion processes on time-varying networks." PLOS Computational Biology 15, no. 7 (2019): e1007190. http://dx.doi.org/10.1371/journal.pcbi.1007190.

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Clote, Peter, and Amir H. Bayegan. "RNA folding kinetics using Monte Carlo and Gillespie algorithms." Journal of Mathematical Biology 76, no. 5 (2017): 1195–227. http://dx.doi.org/10.1007/s00285-017-1169-7.

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Fertig, Dávid, Eszter Mádai, Mónika Valiskó, and Dezső Boda. "Simulating Ion Transport with the NP+LEMC Method. Applications to Ion Channels and Nanopores." Hungarian Journal of Industry and Chemistry 45, no. 1 (2017): 73–84. http://dx.doi.org/10.1515/hjic-2017-0011.

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Abstract We describe a hybrid simulation technique that uses the Nernst-Planck (NP) transport equation to compute steady-state ionic flux in a non-equilibrium system and uses the Local Equilibrium Monte Carlo (LEMC) simulation technique to establish the statistical mechanical relation between the two crucial functions present in the NP equation: the concentration and the electrochemical potential profiles (Boda, D., Gillespie, D., J. Chem. Theor. Comput., 2012 8(3), 824–829). The LEMC method is an adaptation of the Grand Canonical Monte Carlo method to a non-equilibrium situation. We apply the

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De Maio, Nicola, William Boulton, Lukas Weilguny, et al. "phastSim: Efficient simulation of sequence evolution for pandemic-scale datasets." PLOS Computational Biology 18, no. 4 (2022): e1010056. http://dx.doi.org/10.1371/journal.pcbi.1010056.

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Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, and are an essential component of some inference methods. The ongoing surge in available sequence data is however testing the limits of our bioinformatics software. One example is the large number of SARS-CoV-2 genomes available, which are beyond the processing power of many methods, and simulating such large datasets is also proving difficult. Here, we present a new algorithm and software for efficiently simulating sequence evolution along extremely large trees (e.g. >

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Komarov, Ivan, and Roshan M. D'Souza. "Accelerating the Gillespie Exact Stochastic Simulation Algorithm Using Hybrid Parallel Execution on Graphics Processing Units." PLoS ONE 7, no. 11 (2012): e46693. http://dx.doi.org/10.1371/journal.pone.0046693.

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Cota, Wesley, and Silvio C. Ferreira. "Optimized Gillespie algorithms for the simulation of Markovian epidemic processes on large and heterogeneous networks." Computer Physics Communications 219 (October 2017): 303–12. http://dx.doi.org/10.1016/j.cpc.2017.06.007.

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Abel, John H., Brian Drawert, Andreas Hellander, and Linda R. Petzold. "GillesPy: A Python Package for Stochastic Model Building and Simulation." IEEE Life Sciences Letters 2, no. 3 (2016): 35–38. http://dx.doi.org/10.1109/lls.2017.2652448.

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Muniyandi. "Experimenting the Simulation Strategy of Membrane Computing with Gillespie Algorithm by Using Two Biological Case Studies." Journal of Computer Science 6, no. 5 (2010): 525–35. http://dx.doi.org/10.3844/jcssp.2010.525.535.

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Ashwin, Peter, B. S. V. Patnaik, and C. David Wright. "Fast simulation of phase-change processes in chalcogenide alloys using a Gillespie-type cellular automata approach." Journal of Applied Physics 104, no. 8 (2008): 084901. http://dx.doi.org/10.1063/1.2978334.

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Bittig, Arne T., and Adelinde M. Uhrmacher. "ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology." IEEE/ACM Transactions on Computational Biology and Bioinformatics 14, no. 6 (2017): 1339–49. http://dx.doi.org/10.1109/tcbb.2016.2598162.

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Amar, Patrick. "Pandæsim: An Epidemic Spreading Stochastic Simulator." Biology 9, no. 9 (2020): 299. http://dx.doi.org/10.3390/biology9090299.

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Many methods have been used to model epidemic spreading. They include ordinary differential equation systems for globally homogeneous environments and partial differential equation systems to take into account spatial localisation and inhomogeneity. Stochastic differential equations systems have been used to model the inherent stochasticity of epidemic spreading processes. In our case study, we wanted to model the numbers of individuals in different states of the disease, and their locations in the country. Among the many existing methods we used our own variant of the well known Gillespie sto

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Sheldon, Matthew, and Giuliano Casale. "TauSSA." ACM SIGMETRICS Performance Evaluation Review 49, no. 4 (2022): 70–75. http://dx.doi.org/10.1145/3543146.3543162.

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In this paper, we present TauSSA, a discrete-event simulation tool for stochastic queueing networks integrated in the LINE solver. TauSSA combines Gillespie's stochastic simulation algorithm with tau leaping, a methodology for optimistic simulation acceleration. Although tau leaping is frequently used in chemical reaction network simulation, it has so far found limited application in queueing theory. TauSSA offers one of the very first attempts to make this method broadly applicable to analyze extended queueing network models, which include class switching, fork-join, and non-exponential servi

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Cao, Yang, Petzold Linda, and Effrosyni Seitaridou. "Stochastic Simulation of Biochemical Systems: In Memory of Dan T. Gillespie’s contributions." Bulletin of Mathematical Biology 81, no. 8 (2019): 2819–21. http://dx.doi.org/10.1007/s11538-019-00633-w.

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Mura, Ivan. "On modeling approaches for the predictive simulation of living systms dymamics." Revista Ontare 1, no. 2 (2015): 101. http://dx.doi.org/10.21158/23823399.v1.n2.2013.1225.

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ONTARE. REVISTA DE INVESTIGACIÓN DE LA FACULTAD DE INGENIERÍAEste artículo se propone presentar dos de los principales enfoques que están disponibles hoy en día para modelar y simular la evolución dinámica de sistemas vivos: el modelo determinístico continuo, que es dictado por los sistemas de ecuaciones diferenciales ordinarias, y el estocástico discreto, que encuentra su base en el algoritmo de simulación estocástica propuesto por Gillespie en 1976. El objetivo de esta comparación es proporcionar la información necesaria para apoyar la selección de un enfoque de modelaje, basado en un conjun

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