1100 个用于细胞过程动态建模的合成基准问题
1100 Synthetic Benchmark Problems for Dynamic Modeling of Cellular Processes
研究构建了 1100 个合成建模问题，基于 22 个已发表建模问题的常微分方程组与实验设计生成，并扩展了近期一种针对随机观测函数的时间序列数据模拟方法，使其覆盖多种实验条件下的真实测量模式。对数据与模型特征、优化性能和参数可识别性的分析显示，该合成问题集在真实性和多样性上扩展了现有问题空间。基准问题与算法已公开。
来源：Bioinformatics Advances · doi.org
Motivation
Systems biology strives to unravel the complex dynamics of cellular processes using ordinary differential equations. However, the sparsity of measured data and the strong non-linearity of common ordinary differential equations introduce severe numerical problems in modeling tasks. This motivated the development of computational algorithms that must be systematically evaluated to ensure optimal method choices. The amount of well-curated models for such benchmarking efforts is insufficient, as building and calibrating biologically reasonable models from experiments requires years of work.
Results
We present a large-scale collection of 1100 synthetic modeling problems, generated based on the systems of ordinary differential equations and experimental designs of 22 published modeling problems. This is achieved by extending a recent method for simulation of time-course data for randomly generated observation functions to also include realistic measurement patterns across multiple experimental conditions. By analyzing data and model characteristics, optimization performance and parameter identifiability, we show that the synthetic problems provide both a realistic and diverse extension of the existing problem space. Hence, the synthetic collection provides a valuable resource for benchmarking in dynamic modeling.
Availability and Implementation
Benchmark problems and algorithm are publicly available at https://github.com/niklasneubrand/1100SyntheticBenchmarksODE and https://doi.org/10.5281/zenodo.23211070.