期刊文献+

Construction, visualization, and analysis of aiological network models in Dynetica

Construction, visualization, and analysis of aiological network models in Dynetica
原文传递
导出
摘要 Mathematical modeling has become an increasingly important aspect of biological research. Computer simulations help to improve our understanding of complex systems by testing the validity of proposed mechanisms and generating experimentally testable hypotheses. However, significant overhead is generated by the creation, debugging, and perturbation of these computational models and their parameters, especially for researchers who are unfamiliar with programming or numerical methods. Dynetica 2.0 is a user-friendly dynamic network simulator designed to expedite this process. Models are created and visualized in an easy-to-use graphical interface, which displays all of the species and reactions involved in a graph layout. System inputs and outputs, indicators, and intermediate expressions may be incorporated into the model via the versatile "expression variable" entity. Models can also be modular, allowing for the quick construction of complex systems from simpler components. Dynetica 2.0 supports a number of deterministic and stochastic algorithms for performing time-course simulations. Additionally, Dynetica 2.0 provides built-in tools for performing sensitivity or dose response analysis for a number of different metrics. Its parameter searching tools can optimize specific objectives of the time course or dose response of the system. Systems can be translated from Dynetica 2.0 into MATLAB code or the Systems Biology Markup Language (SBML) format for further analysis or publication. Finally, since it is written in Java, Dynetica 2.0 is platform independent, allowing for easy sharing and collaboration between researchers. Mathematical modeling has become an increasingly important aspect of biological research. Computer simulations help to improve our understanding of complex systems by testing the validity of proposed mechanisms and generating experimentally testable hypotheses. However, significant overhead is generated by the creation, debugging, and perturbation of these computational models and their parameters, especially for researchers who are unfamiliar with programming or numerical methods. Dynetica 2.0 is a user-friendly dynamic network simulator designed to expedite this process. Models are created and visualized in an easy-to-use graphical interface, which displays all of the species and reactions involved in a graph layout. System inputs and outputs, indicators, and intermediate expressions may be incorporated into the model via the versatile "expression variable" entity. Models can also be modular, allowing for the quick construction of complex systems from simpler components. Dynetica 2.0 supports a number of deterministic and stochastic algorithms for performing time-course simulations. Additionally, Dynetica 2.0 provides built-in tools for performing sensitivity or dose response analysis for a number of different metrics. Its parameter searching tools can optimize specific objectives of the time course or dose response of the system. Systems can be translated from Dynetica 2.0 into MATLAB code or the Systems Biology Markup Language (SBML) format for further analysis or publication. Finally, since it is written in Java, Dynetica 2.0 is platform independent, allowing for easy sharing and collaboration between researchers.
出处 《Frontiers of Electrical and Electronic Engineering in China》 2014年第4期142-150,共9页 中国电气与电子工程前沿(英文版)
关键词 mathematical modeling systems biology synthetic biology quantitative biology gene circuits mathematical modeling systems biology synthetic biology quantitative biology gene circuits
  • 相关文献

参考文献20

  • 1Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutsehow, M. V., Jaeobs, J.M., Bolival, B. Jr., Assad-Garcia, N., Glass, J. I. and Covert, M. W. (2012) A whole-cell computational model predicts phenotype from genotype. Cell, 150, 389-401.
  • 2Locke, J. C. W., Young, J. W., Fontes, M., Hem dez Jim6nez, M. J. and Elowitz, M. B. (2011) Stochastic pulse regulation in bacterial stress response. Science, 334, 366-369.
  • 3Pai, A., Tanouchi, Y. and You, L. (2012) Optimality and robustness in quorum sensing (QS)-mediated regulation of a costly public good enzyme. Proc. Natl. Acad. Sci. USA, 109, 19810-19815.
  • 4Danino, T., Mondrag6n-Palomino, O., Tsimring, L. and Hasty, J. (2010) A synchronized quorum of genetic clocks. Nature, 463, 326-330.
  • 5Ferrezuelo, E, Colomina, N., Palmisano, A., Gad, E., Gallego, C., Csik sz-Nagy, A. and Aldea, M. (2012) The critical size is set at a single-cell level by growth rate to attain homeostasis and adaptation. Nat. Commun., 3, 1012.
  • 6Tan, C., Smith, R. P., Srimani, 3. K., Riccione, K. A., Prasada, S., Kuehn, M. and You, L. (2012) The inoeulum effect and band-pass bacterial response to periodic antibiotic treatment. Mol. Syst. Biol., 8, 617.
  • 7Yao, G., Tan, C., West, M., Nevins, J. R. and You, L. (2011) Origin of bistability underlying mammalian cell cycle entry. Mol. Syst. Biol., 7, 485.
  • 8Wong, J. W., Li, B. and You, L. (2012) Tension and robustness in multitasking cellular networks. PLoS Comput. Biol., 8, e1002491.
  • 9Wang, J., Li, C. and Wang, E. (2010) Potential and flux landscapes quantify the stability and robustness of budding yeast cell cycle network. Proe. Natl. Acad. Sci. USA, 107, 8195-8200.
  • 10Li, C., Donizelli, M., Rodriguez, N., Dhantri, H., Endler, L., Chelliah, V., Li, L., He, E., Henry, A., Stefan, M. I., et al. (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst. Biol., 4, 92.

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部