Meta-stochastic simulation of biochemical models for systems and synthetic biology.
Meta-stochastic simulation of biochemical models for systems and synthetic biology.
复制标题
系统和合成生物学生化模型的元随机模拟。
DOI：
10.1021/sb5001406
复制
发表时间：
2015
影响因子：
4.7
通讯作者：
Sanassy D
中科院分区：
文献类型：
--
作者：
Sanassy D
随机模拟算法 (SSA) 用于追踪低物种浓度下生化系统的真实轨迹。随着生物系统建模的复杂性增加，选择性能最佳的 SSA 非常重要。已经引入了许多对 SSA 的改进，但它们都只适用于某一类模型。这使得系统或合成生物学家在面对需要模拟的新模型时很难决定采用哪种算法。在本文中，我们证明可以确定哪种算法最适合模拟特定模型，并且可以在算法执行之前进行预测。我们提出了一个基于网络的预测工具，允许科学家上传生化模型并获得最佳性能 SSA 的预测。此外，ssapredict 使用户可以选择下载我们预先配置的高性能模拟器，以使用预测的最快算法作为模拟引擎来执行查询的生化模型的模拟。 ThessapredictWeb 应用程序可从 http://ssapredict.ico2s.org 获取。它是免费软件，其源代码根据 GNU Affero 通用公共许可证的条款分发。
Stochastic simulation algorithms (SSAs) are used to trace realistic trajectories of biochemical systems at low species concentrations. As the complexity of modeled biosystems increases, it is important to select the best performing SSA. Numerous improvements to SSAs have been introduced but they each only tend to apply to a certain class of models. This makes it difficult for a systems or synthetic biologist to decide which algorithm to employ when confronted with a new model that requires simulation. In this paper, we demonstrate that it is possible to determine which algorithm is best suited to simulate a particular model and that this can be predicteda priorito algorithm execution. We present a Web based toolssapredictthat allows scientists to upload a biochemical model and obtain a prediction of the best performing SSA. Furthermore,ssapredictgives the user the option to download our high performance simulatorngsspreconfigured to perform the simulation of the queried biochemical model with the predicted fastest algorithm as the simulation engine. ThessapredictWeb application is available at http://ssapredict.ico2s.org. It is free software and its source code is distributed under the terms of the GNU Affero General Public License.
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影响因子：
5.8
作者：
Hucka, M;Finney, A;Wang, J
通讯作者：
Wang, J
影响因子：
2.7
作者：
Wu, Xindong;Kumar, Vipin;Steinberg, Dan
通讯作者：
Steinberg, Dan
DOI：
10.1093/bioinformatics/btr571
发表时间：
2011-12-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Blakes J;Twycross J;Romero-Campero FJ;Krasnogor N
通讯作者：
Krasnogor N
用于科学应用的 web2py
影响因子：
2.1
作者：
M. D. di Pierro
通讯作者：
M. D. di Pierro