ConvAn: a convergence analyzing tool for optimization of biochemical networks.

Area of Science:

• Biochemistry
• Computational Biology
• Systems Biology

Background:

• Dynamic models of biochemical networks commonly use nonlinear differential equations.
• Numerical optimization methods are frequently employed for parameter estimation and model design, but their stochastic nature hinders predictability.
• Assessing optimization performance is complex due to numerous methods, software tools, and model nonlinearities.

Purpose of the Study:

• To develop a software tool, ConvAn, for analyzing the statistical properties of optimization convergence dynamics.
• To enable objective comparison of different optimization methods, models, and parameter settings.
• To facilitate the estimation of computational time and accuracy for biochemical model optimization.

Main Methods:

• Development of the ConvAn software with a biochemistry-adapted graphical user interface.
• Automatic normalization of convergence curves for cross-method and cross-model comparisons.
• Statistical analysis of optimization runs varying methods, models, software, parameters, and adjustable parameters.

Main Results:

• ConvAn allows comparison of optimization methods based on their ability to find global optima and computational efficiency.
• The software facilitates performance estimation across varying numbers of adjustable parameters.
• Statistical assessment of optimization time versus required accuracy is achievable.

Conclusions:

• ConvAn provides a robust framework for evaluating and selecting appropriate optimization strategies for dynamic biochemical models.
• The tool aids in rejecting unsuitable optimization methods by assessing repeatability and convergence properties.
• ConvAn is freely available, promoting reproducible and efficient research in biochemical network modeling.