Kostromins Andrejs, Mozga Ivars, Stalidzans Egils
Biosystems Group, Department of Computer Systems, Latvia University of Agriculture, Jelgava, Latvia.
Biosystems. 2012 Apr-Jun;108(1-3):73-7. doi: 10.1016/j.biosystems.2011.12.004. Epub 2011 Dec 29.
Dynamic models of biochemical networks usually are described as a system of nonlinear differential equations. In case of optimization of models for purpose of parameter estimation or design of new properties mainly numerical methods are used. That causes problems of optimization predictability as most of numerical optimization methods have stochastic properties and the convergence of the objective function to the global optimum is hardly predictable. Determination of suitable optimization method and necessary duration of optimization becomes critical in case of evaluation of high number of combinations of adjustable parameters or in case of large dynamic models. This task is complex due to variety of optimization methods, software tools and nonlinearity features of models in different parameter spaces. A software tool ConvAn is developed to analyze statistical properties of convergence dynamics for optimization runs with particular optimization method, model, software tool, set of optimization method parameters and number of adjustable parameters of the model. The convergence curves can be normalized automatically to enable comparison of different methods and models in the same scale. By the help of the biochemistry adapted graphical user interface of ConvAn it is possible to compare different optimization methods in terms of ability to find the global optima or values close to that as well as the necessary computational time to reach them. It is possible to estimate the optimization performance for different number of adjustable parameters. The functionality of ConvAn enables statistical assessment of necessary optimization time depending on the necessary optimization accuracy. Optimization methods, which are not suitable for a particular optimization task, can be rejected if they have poor repeatability or convergence properties. The software ConvAn is freely available on www.biosystems.lv/convan.
生化网络的动态模型通常被描述为一个非线性微分方程组。在为参数估计或新特性设计而优化模型时，主要使用数值方法。这就导致了优化可预测性的问题，因为大多数数值优化方法都具有随机性，目标函数收敛到全局最优值很难预测。在评估大量可调参数组合或大型动态模型时，确定合适的优化方法和必要的优化持续时间变得至关重要。由于优化方法、软件工具以及不同参数空间中模型的非线性特征多种多样，这项任务很复杂。开发了一个软件工具ConvAn，用于分析特定优化方法、模型、软件工具、优化方法参数集以及模型可调参数数量的优化运行的收敛动力学统计特性。收敛曲线可以自动归一化，以便在同一尺度上比较不同的方法和模型。借助ConvAn的生化适配图形用户界面，可以比较不同优化方法在找到全局最优值或接近全局最优值的能力方面，以及达到这些值所需的计算时间。可以估计不同数量可调参数的优化性能。ConvAn的功能能够根据所需的优化精度对必要的优化时间进行统计评估。如果优化方法的可重复性或收敛特性较差，则可以拒绝不适合特定优化任务的优化方法。软件ConvAn可在www.biosystems.lv/convan上免费获取。
