• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

RMBNToolbox：生化网络的随机模型

RMBNToolbox: random models for biochemical networks.

作者信息

Aho Tommi, Smolander Olli-Pekka, Niemi Jari, Yli-Harja Olli

机构信息

Department of Information Technology, Institute of Signal Processing, Tampere University of Technology, Tampere, Finland.

出版信息

BMC Syst Biol. 2007 May 24;1:22. doi: 10.1186/1752-0509-1-22.

DOI:10.1186/1752-0509-1-22
PMID:17524136
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1896132/
Abstract

BACKGROUND

There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models.

RESULTS

We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language.

CONCLUSION

While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

摘要

背景

对生化和细胞生物学网络进行建模以及对这些模型进行计算分析的兴趣日益浓厚。该领域分析方法和相关软件的发展迅速。然而，可用模型的数量仍然相对较少，且模型规模仍然有限。动力学信息的缺乏通常是构建详细模拟模型的限制因素。

结果

我们展示了一个用于生成模拟真实生化网络的随机生化网络模型的计算工具箱。该工具箱名为“生化网络随机模型”。它在Matlab环境中运行，能够生成各种网络结构、化学计量学、反应动力学定律及其参数。生成过程可以基于统计规则和分布，并且在已知真实生化网络更详细信息的情况下可以使用这些信息。该工具箱易于扩展。生成的网络模型可以以系统生物学标记语言的格式导出。

结论

虽然关于生化网络的信息越来越多，但随机网络可作为更好理解它们的中间步骤。随机网络能够研究各种网络特征对网络整体行为的影响。此外，人工网络模型的构建为参数估计和数据分析领域中各种计算方法的验证提供了所需的基础真实数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/e83b1ab05ab9/1752-0509-1-22-8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/04fe6f8548c6/1752-0509-1-22-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/9fbcf6f039e8/1752-0509-1-22-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/ead63dd73437/1752-0509-1-22-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/b652d99b9f07/1752-0509-1-22-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/995f3fd88253/1752-0509-1-22-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/185a2b0bfe96/1752-0509-1-22-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/6be56968234d/1752-0509-1-22-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/e83b1ab05ab9/1752-0509-1-22-8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/04fe6f8548c6/1752-0509-1-22-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/9fbcf6f039e8/1752-0509-1-22-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/ead63dd73437/1752-0509-1-22-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/b652d99b9f07/1752-0509-1-22-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/995f3fd88253/1752-0509-1-22-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/185a2b0bfe96/1752-0509-1-22-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/6be56968234d/1752-0509-1-22-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb3d/1896132/e83b1ab05ab9/1752-0509-1-22-8.jpg

相似文献

1
RMBNToolbox: random models for biochemical networks.RMBNToolbox：生化网络的随机模型
BMC Syst Biol. 2007 May 24;1:22. doi: 10.1186/1752-0509-1-22.
2
METANNOGEN: compiling features of biochemical reactions needed for the reconstruction of metabolic networks.元生成器：编译代谢网络重建所需生化反应的特征。
BMC Syst Biol. 2007 Jan 9;1:5. doi: 10.1186/1752-0509-1-5.
3
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox.基于约束模型的细胞代谢定量预测：COBRA工具箱
Nat Protoc. 2007;2(3):727-38. doi: 10.1038/nprot.2007.99.
4
Computational modeling of biochemical networks using COPASI.使用COPASI对生化网络进行计算建模。
Methods Mol Biol. 2009;500:17-59. doi: 10.1007/978-1-59745-525-1_2.
5
Synthetic microarray data generation with RANGE and NEMO.使用RANGE和NEMO生成合成微阵列数据。
Bioinformatics. 2008 Jan 1;24(1):132-4. doi: 10.1093/bioinformatics/btm529. Epub 2007 Nov 3.
6
Gene expression complex networks: synthesis, identification, and analysis.基因表达复杂网络：合成、识别与分析。
J Comput Biol. 2011 Oct;18(10):1353-67. doi: 10.1089/cmb.2010.0118. Epub 2011 May 6.
7
A method for the generation of standardized qualitative dynamical systems of regulatory networks.一种生成调控网络标准化定性动力学系统的方法。
Theor Biol Med Model. 2006 Mar 16;3:13. doi: 10.1186/1742-4682-3-13.
8
Ingeneue: a software tool to simulate and explore genetic regulatory networks.Ingeneue：一款用于模拟和探索基因调控网络的软件工具。
Methods Mol Biol. 2009;500:169-200. doi: 10.1007/978-1-59745-525-1_6.
9
Parameter estimation using Simulated Annealing for S-system models of biochemical networks.使用模拟退火算法对生化网络的S-系统模型进行参数估计。
Bioinformatics. 2007 Feb 15;23(4):480-6. doi: 10.1093/bioinformatics/btl522. Epub 2006 Oct 11.
10
Integration of enzyme kinetic data from various sources.整合来自各种来源的酶动力学数据。
In Silico Biol. 2007;7(2 Suppl):S73-9.

引用本文的文献

1
Assessing and Resolving Model Misspecifications in Metabolic Flux Analysis.评估与解决代谢通量分析中的模型误设问题
Bioengineering (Basel). 2017 May 24;4(2):48. doi: 10.3390/bioengineering4020048.

本文引用的文献

1
COPASI--a COmplex PAthway SImulator.COPASI——一种复杂途径模拟器。
Bioinformatics. 2006 Dec 15;22(24):3067-74. doi: 10.1093/bioinformatics/btl485. Epub 2006 Oct 10.
2
Simulation of microarray data with realistic characteristics.具有现实特征的微阵列数据模拟。
BMC Bioinformatics. 2006 Jul 18;7:349. doi: 10.1186/1471-2105-7-349.
3
Metatool 5.0: fast and flexible elementary modes analysis.Metatool 5.0：快速灵活的基本模式分析
Bioinformatics. 2006 Aug 1;22(15):1930-1. doi: 10.1093/bioinformatics/btl267. Epub 2006 May 26.
4
SBMLToolbox: an SBML toolbox for MATLAB users.SBMLToolbox：面向MATLAB用户的一个SBML工具箱。
Bioinformatics. 2006 May 15;22(10):1275-7. doi: 10.1093/bioinformatics/btl111. Epub 2006 Mar 30.
5
The SBML ODE Solver Library: a native API for symbolic and fast numerical analysis of reaction networks.SBML常微分方程求解器库：用于反应网络符号和快速数值分析的原生应用程序编程接口。
Bioinformatics. 2006 Jun 1;22(11):1406-7. doi: 10.1093/bioinformatics/btl086. Epub 2006 Mar 9.
6
BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems.生物模型数据库：一个免费的、集中的数据库，收录经过整理、已发表的生化和细胞系统定量动力学模型。
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D689-91. doi: 10.1093/nar/gkj092.
7
Lethality and synthetic lethality in the genome-wide metabolic network of Escherichia coli.大肠杆菌全基因组代谢网络中的致死性和合成致死性
J Theor Biol. 2005 Dec 21;237(4):401-11. doi: 10.1016/j.jtbi.2005.04.025. Epub 2005 Jun 21.
8
Genome-scale analysis of Streptomyces coelicolor A3(2) metabolism.天蓝色链霉菌A3(2)代谢的全基因组规模分析。
Genome Res. 2005 Jun;15(6):820-9. doi: 10.1101/gr.3364705.
9
Identification and characterization of protein subcomplexes in yeast.酵母中蛋白质亚复合物的鉴定与表征
Proteomics. 2005 May;5(8):2082-9. doi: 10.1002/pmic.200401121.
10
An improved algorithm for stoichiometric network analysis: theory and applications.一种用于化学计量网络分析的改进算法：理论与应用
Bioinformatics. 2005 Apr 1;21(7):1203-10. doi: 10.1093/bioinformatics/bti127. Epub 2004 Nov 11.