检索结果-内蒙古大学图书馆

gillespie eco-evolutionary models (GEMs) reveal the role of heritable trait variation in eco-evolutionary dynamics

ECOLOGY AND EVOLUTION 2016年第4期6卷 935-945页

作者： DeLong, John P. Gibert, Jean P. Univ Nebraska Sch Biol Sci Lincoln NE 68588 USA

Heritable trait variation is a central and necessary ingredient of evolution. Trait variation also directly affects ecological processes, generating a clear link between evolutionary and ecological dynamics. Despite the changes in variation that occur through selection, drift, mutation, and recombination, current eco-evolutionary models usually fail to track how variation changes through time. Moreover, eco-evolutionary models assume fitness functions for each trait and each ecological context, which often do not have empirical validation. We introduce a new type of model, gillespie eco-evolutionary models (GEMs), that resolves these concerns by tracking distributions of traits through time as eco-evolutionary dynamics progress. This is done by allowing change to be driven by the direct fitness consequences of model parameters within the context of the underlying ecological model, without having to assume a particular fitness function. GEMs work by adding a trait distribution component to the standard gillespie algorithm - an approach that models stochastic systems in nature that are typically approximated through ordinary differential equations. We illustrate GEMs with the Rosenzweig-MacArthur consumer-resource model. We show not only how heritable trait variation fuels trait evolution and influences eco-evolutionary dynamics, but also how the erosion of variation through time may hinder eco-evolutionary dynamics in the long run. GEMs can be developed for any parameter in any ordinary differential equationmodel and, furthermore, can enable modeling of multiple interacting traits at the same time. We expect GEMs will open the door to a new direction in eco-evolutionary and evolutionary modeling by removing long-standing modeling barriers, simplifying the link between traits, fitness, and dynamics, and expanding eco-evolutionary treatment of a greater diversity of ecological interactions. These factors make GEMs much more than a modeling advance, but an important con

关键词： Body size consumer-resource dynamics Eco-evolutionary dynamics ecological pleiotropy gillespie algorithm trait variation

来源：评论

学校读者我要写书评

暂无评论

A gillespie-based Computational Model for Integrating Event-driven and Multi-Agent Based Simulation 14

A Gillespie-based Computational Model for Integrating Event-...

引用

14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Montagna, Sara Omicini, Andrea Pianini, Danilo Univ Bologna Alma Mater Studiorum Via Sacchi 3 I-47521 Cesena Italy

ISBN: (纸本)9781450334136

Based on two intuitions - (i) event-driven systems and multi-agent systems are two computational paradigms that are amenable of a coherent interpretation within a unique conceptual framework;(ii) integrating the two simulation approaches can lead to a more expressive and powerful simulation framework - we propose a computational model integrating Discrete-Event Simulation (DES) and Multi-Agent Based Simulation (MABS), based on an extension of the gillespie's stochastic simulation algorithm.

关键词： Multi-agent based simulation event-driven simulation stochastic simulation gillespie algorithm

来源：评论

学校读者我要写书评

暂无评论

Lattice Microbes: High-Performance Stochastic Simulation Method for the Reaction-Diffusion Master Equation

引用

JOURNAL OF COMPUTATIONAL CHEMISTRY 2013年第3期34卷 245-255页

作者： Roberts, Elijah Stone, John E. Luthey-Schulten, Zaida Univ Illinois Dept Chem Urbana IL 61801 USA Univ Illinois Ctr Phys Living Cells Urbana IL 61801 USA Univ Illinois Beckman Inst Urbana IL 61801 USA

Spatial stochastic simulation is a valuable technique for studying reactions in biological systems. With the availability of high-performance computing (HPC), the method is poised to allow integration of data from structural, single-molecule and biochemical studies into coherent computational models of cells. Here, we introduce the Lattice Microbes software package for simulating such cell models on HPC systems. The software performs either well-stirred or spatially resolved stochastic simulations with approximated cytoplasmic crowding in a fast and efficient manner. Our new algorithm efficiently samples the reaction-diffusion master equation using NVIDIA graphics processing units and is shown to be two orders of magnitude faster than exact sampling for large systems while maintaining an accuracy of similar to 0.1%. Display of cell models and animation of reaction trajectories involving millions of molecules is facilitated using a plug-in to the popular VMD visualization platform. The Lattice Microbes software is open source and available for download at http://***/schulten/lm (C) 2012 Wiley Periodicals, Inc. DOI: 10.1002/jcc.23130

关键词： chemical master equation reaction-diffusion master equation gillespie algorithm stochastic simulation graphics processing unit computing

来源：评论

学校读者我要写书评

暂无评论

Stochastic simulation of quorum sensing in Vibrio fischeri based on P System

引用

EVOLVING SYSTEMS 2019年第2期10卷 167-177页

作者： Ben Ali, Yamina Mohamed Tazir, Khaira Univ Badji Mokhtar Dept Comp Sci BP 12 Annaba 23000 Algeria

Inspiration from nature and especially from biology is up today a multidisciplinary challenge where biologists aim to exploit competences' of a computer to understand phenomena of life through simulations and where artificial intelligence aims to develop new computational approaches inherent from natural processes. Situated in the border of evolutionary systems and bioinformatics, the computational model of P system is an evolved algorithm based upon the structure and evolution rules of biological living cells to outperform some conventional systems. Thus, this paper presents a version of multi-compartmental gillespie algorithm used to simulate the parallelism of chemical reactions in the quorum sensing phenomena of a bacteria colony. This model is used to distribute the computation over the membranes. The aim is to achieve scalability and get mapping the communication between bacteria and their environment. The obtained results prove that this process adds an important value to the multi-compartmental gillespie algorithm, which uses the organizational aspect of the membrane structure. They also prove that the independent parallel evolution of the membranes is basic to improve legitimacy of the strategy which leads to a reliable simulation and gives a more realistic representation of the system's evolution.

关键词： Cell communication Quorum sensing P systems gillespie algorithm

来源：评论

学校读者我要写书评

暂无评论

Solving the problem of negative populations in approximate accelerated stochastic simulations using the representative reaction approach

引用

JOURNAL OF COMPUTATIONAL CHEMISTRY 2013年第5期34卷 394-404页

作者： Kadam, Shantanu Vanka, Kumar Natl Chem Lab Div Phys Chem Pune 411008 Maharashtra India

Methods based on the stochastic formulation of chemical kinetics have the potential to accurately reproduce the dynamical behavior of various biochemical systems of interest. However, the computational expense makes them impractical for the study of real systems. Attempts to render these methods practical have led to the development of accelerated methods, where the reaction numbers are modeled by Poisson random numbers. However, for certain systems, such methods give rise to physically unrealistic negative numbers for species populations. The methods which make use of binomial variables, in place of Poisson random numbers, have since become popular, and have been partially successful in addressing this problem. In this manuscript, the development of two new computational methods, based on the representative reaction approach (RRA), has been discussed. The new methods endeavor to solve the problem of negative numbers, by making use of tools like the stochastic simulation algorithm and the binomial method, in conjunction with the RRA. It is found that these newly developed methods perform better than other binomial methods used for stochastic simulations, in resolving the problem of negative populations. (C) 2012 Wiley Periodicals, Inc.

关键词： Stochastic simulations Kinetic Monte Carlo gillespie algorithm representative reaction approach approximate methods binomial distributions Poisson distributions

来源：评论

学校读者我要写书评

暂无评论

Simulation-free estimation of reaction propensities in cellular reactions and gene signaling networks

引用

COMPUTERS & CHEMICAL ENGINEERING 2016年 87卷 154-163页

作者： Yenkie, K. M. Diwekar, U. M. Linninger, A. A. Univ Illinois Dept Bioengn Chicago IL USA Vishwamitra Res Inst CUSTOM Crystal Lake IL USA

Classical reaction kinetics based on deterministic rates laws are not valid for the description of cellular events in which the small number of molecules introduces stochasticity with discrete instead of continuous state transitions. Stochastic models are suitable for simulating transcriptional and translational events inside biological cells, but are impractical for solving inverse problems, which aim to estimate unknown reaction propensities from experimental observations. We introduce a new mathematical framework of Ito stochastic differential equations for the modeling of discrete cellular events and the robust and consistent parameter estimation of cellular dynamics where classical reaction kinetics is invalid. The results supported by case studies on gene expression in B. subtilis cells and viral gene transcription and translation inside non-lytic viral cells demonstrate that the proposed methodology performs as reliable as the gold standard gillespie algorithm for simulating cellular events. More importantly, the new Ito process framework is ideal for estimating unknown reaction propensities from data as readily as in deterministic parameter estimation by using the novel 'SPE - simulation free parameter estimation' approach. Also, the computation time for the stochastic differential equation models is significantly low when compared to discrete event simulations. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Ito process gillespie algorithm Stochastic modeling Inversion

来源：评论

学校读者我要写书评

暂无评论

Modeling framework for membrane computing in biological systems: Evaluation with a case study

引用

JOURNAL OF COMPUTATIONAL SCIENCE 2014年第2期5卷 137-143页

作者： Muniyandi, Ravie Chandren Zin, Abdullah Mohd Univ Kebangsaan Malaysia Fac Technol & Informat Sci Res Ctr Software Technol & Management SOFTAM Bangi 43600 Selangor Malaysia

Membrane computing can represent the structures and behaviors of biological systems while considering their characteristics. This paper proposes a modeling framework for membrane computing in biological systems to provide guidelines when using and experimenting with membrane computing. The framework processes include the biological requirements and property specifications, membrane computing model, membrane computing simulation strategy, and model checking approach. A biological system that comprised the ligand receptor networks of TGF-beta protein was used as a case study to evaluate the framework. The evaluation of the framework demonstrated that membrane computing performed better than conventional ordinary differential equations when capturing the structure and behavior of biological systems. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Biological systems gillespie algorithm Membrane computing Model checking Modeling framework

来源：评论

学校读者我要写书评

暂无评论

Queueing Theory-Based Perspective of the Kinetics of "Channeled" Enzyme Cascade Reactions

引用

ACS CATALYSIS 2018年第11期8卷 10721-10731页

作者： Tsitkov, Stanislav Pesenti, Theo Palacci, Henri Blanchet, Jose Hess, Henry Columbia Univ Dept Biomed Engn New York NY 10027 USA Ecole Super Phys & Chim Ind Ville Paris F-75231 Paris 05 France Stanford Univ Management Sci & Engn Palo Alto CA 94305 USA

Queueing approaches can capture the stochastic dynamics of chemical reactions and provide a more accurate picture of the reaction kinetics than coupled differential equations in situations where the number of molecules is small. A striking example of such a situation is an enzyme cascade with substrate channeling, where reaction intermediates are directly passed from one enzyme to the next via tunnels or surface paths with limited capacity. In order to better understand the contribution of the stochastic dynamics to the observed enhancement in cascade throughput as a result from substrate channeling, we compare the results of a model using differential equations to describe concentration changes with a queueing model. The continuum model and the queueing model yield identical results, except when the maximum rate of reaction of the enzymes are similar. In two enzyme cascades, the queueing model predicts at most a 50% smaller throughput than the continuum model even if the waiting room size (the maximum number of molecules that can fit in the tunnel or surface path between enzymes) is limited to only one molecule and the enzymes are perfectly matched in their kinetic rates. In longer cascades, the discrepancy increases, reaching a 5-fold difference for a 10 enzyme cascade. In line with theoretical results from queueing theory, stochastic effects are found to always reduce cascade throughput, which means they cannot contribute to the experimentally observed enhancement in throughput due to channeling.

关键词： enzyme cascade queueing theory gillespie algorithm Michaelis-Menten model stochasticity

来源：评论

学校读者我要写书评

暂无评论

AESS: Accelerated Exact Stochastic Simulation

引用

COMPUTER PHYSICS COMMUNICATIONS 2011年第12期182卷 2580-2586页

作者： Jenkins, David D. Peterson, Gregory D. Univ Tennessee Knoxville TN 37996 USA

The Stochastic Simulation algorithm (SSA) developed by gillespie provides a powerful mechanism for exploring the behavior of chemical systems with small species populations or with important noise contributions. Gene circuit simulations for systems biology commonly employ the SSA method, as do ecological applications. This algorithm tends to be computationally expensive, so researchers seek an efficient implementation of SSA. In this program package, the Accelerated Exact Stochastic Simulation algorithm (AESS) contains optimized implementations of gillespie's SSA that improve the performance of individual simulation runs or ensembles of simulations used for sweeping parameters or to provide statistically significant results. Program summary Program title: AESS Catalogue identifier: AEJW_v1_0 Program summary URL:http://***/summaries/AEJW_v1_*** Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: University of Tennessee copyright agreement No. of lines in distributed program, including test data, etc.: 10861 No. of bytes in distributed program, including test data, etc.: 394 631 Distribution format: *** Programming language: C for processors, CUDA for NVIDIA GPUs Computer: Developed and tested on various x86 computers and NVIDIA C1060 Tesla and GTX 480 Fermi GPUs. The system targets x86 workstations, optionally with multicore processors or NVIDIA GPUs as accelerators. Operating system: Tested under Ubuntu Linux OS and CentOS 5.5 Linux OS Classification: 3, 16.12 Nature of problem: Simulation of chemical systems, particularly with low species populations, can be accurately performed using gillespie's method of stochastic simulation. Numerous variations on the original stochastic simulation algorithm have been developed, including approaches that produce results with statistics that exactly match the chemical master equation (CME) as well as other approaches that approximate the CME. Solution met

关键词： Stochastic simulation gillespie algorithm Systems biology Monte Carlo Computational ecology

来源：评论

学校读者我要写书评

暂无评论

Coupling sample paths to the thermodynamic limit in Monte Carlo estimators with applications to gene expression

引用

JOURNAL OF COMPUTATIONAL PHYSICS 2017年 346卷 1-13页

作者： Levien, Ethan Bressloff, Paul C. Univ Utah Dept Math Salt Lake City UT 84112 USA

Many biochemical systems appearing in applications have a multiscale structure so that they converge to piecewise deterministic Markov processes in a thermodynamic limit. The statistics of the piecewise deterministic process can be obtained much more efficiently than those of the exact process. We explore the possibility of coupling sample paths of the exact model to the piecewise deterministic process in order to reduce the variance of their difference. We then apply this coupling to reduce the computational complexity of a Monte Carlo estimator. Motivated by the rigorous results in [1], we show how this method can be applied to realistic biological models with nontrivial scalings. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Chemical reaction networks Monte Carlo Variance reduction Piecewise deterministic Markov process gillespie algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：