Dörr Alexander, Keller Roland, Zell Andreas, Dräger Andreas
Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Sand 1, 72076 Tübingen, Germany.
Systems Biology Research Group, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.
Computation (Basel). 2014 Dec;2(4):246-257. doi: 10.3390/computation2040246. Epub 2014 Dec 18.
The identification of suitable model parameters for biochemical reactions has been recognized as a quite difficult endeavor. Parameter values from literature or experiments can often not directly be combined in complex reaction systems. Nature-inspired optimization techniques can find appropriate sets of parameters that calibrate a model to experimentally obtained time series data. We present SBMLsimulator, a tool that combines the Systems Biology Simulation Core Library for dynamic simulation of biochemical models with the heuristic optimization framework EvA2. SBMLsimulator provides an intuitive graphical user interface with various options as well as a fully-featured command-line interface for large-scale and script-based model simulation and calibration. In a parameter estimation study based on a published model and artificial data we demonstrate the capability of SBMLsimulator to identify parameters. SBMLsimulator is useful for both, the interactive simulation and exploration of the parameter space and for the large-scale model calibration and estimation of uncertain parameter values.
确定生化反应的合适模型参数已被认为是一项相当困难的工作。文献或实验中的参数值通常不能直接应用于复杂的反应系统。受自然启发的优化技术可以找到合适的参数集，将模型校准到实验获得的时间序列数据。我们展示了SBMLsimulator，这是一种将用于生化模型动态模拟的系统生物学模拟核心库与启发式优化框架EvA2相结合的工具。SBMLsimulator提供了一个直观的图形用户界面，带有各种选项，以及一个功能齐全的命令行界面，用于大规模和基于脚本的模型模拟与校准。在一项基于已发表模型和人工数据的参数估计研究中，我们展示了SBMLsimulator识别参数的能力。SBMLsimulator对于交互式模拟和参数空间探索，以及大规模模型校准和不确定参数值的估计都很有用。
