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SELANSI:用于随机基因调控网络模拟的工具箱。

SELANSI: a toolbox for simulation of stochastic gene regulatory networks.

机构信息

BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, 36208 Vigo, Spain.

Department of Mathematics, University of A Coruña Campus Elviña s/n, 15071 A Coruña, Spain.

出版信息

Bioinformatics. 2018 Mar 1;34(5):893-895. doi: 10.1093/bioinformatics/btx645.

DOI:10.1093/bioinformatics/btx645
PMID:29040384
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6030881/
Abstract

MOTIVATION

Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort.

RESULTS

This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a software toolbox for the simulation of stochastic multidimensional gene regulatory networks. SELANSI exploits intrinsic structural properties of gene regulatory networks to accurately approximate the corresponding Chemical Master Equation with a partial integral differential equation that is solved by a semi-lagrangian method with high efficiency. Networks under consideration might involve multiple genes with self and cross regulations, in which genes can be regulated by different transcription factors. Moreover, the validity of the method is not restricted to a particular type of kinetics. The tool offers total flexibility regarding network topology, kinetics and parameterization, as well as simulation options.

AVAILABILITY AND IMPLEMENTATION

SELANSI runs under the MATLAB environment, and is available under GPLv3 license at https://sites.google.com/view/selansi.

CONTACT

antonio@iim.csic.es.

摘要

动机

基因调控本质上是随机的。在许多涉及系统和合成生物学的应用中,如遗传电路的反向工程和从头设计,由于随机模拟的计算成本很高,因此经常忽略随机效应(尽管可能很关键)。随着这些领域的发展,人们越来越需要能够以较低的计算成本提供基因调控网络 (GRN) 随机动力学的准确近似的工具。

结果

这项工作提出了 SELANSI(基于半拉格朗日的 GRN 仿真),这是一个用于模拟随机多维基因调控网络的软件工具箱。SELANSI 利用基因调控网络的内在结构特性,通过半拉格朗日方法以高效率求解偏积分微分方程,从而准确地近似相应的化学主方程。所考虑的网络可能涉及具有自我和交叉调节的多个基因,其中基因可以由不同的转录因子调节。此外,该方法的有效性不受特定动力学类型的限制。该工具在网络拓扑、动力学和参数化以及仿真选项方面提供了完全的灵活性。

可用性和实现

SELANSI 在 MATLAB 环境下运行,并在 https://sites.google.com/view/selansi 下以 GPLv3 许可证提供。

联系人

antonio@iim.csic.es。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f34/6030881/572f77574972/btx645f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f34/6030881/572f77574972/btx645f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f34/6030881/572f77574972/btx645f1.jpg

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