Nonlinear dose–response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Modeling such responses is a crucial component of toxicity testing and chemical screening. Traditional model fitting methods such as nonlinear least squares (NLS) are very sensitive to initial parameter values and often had convergence failure. The use of evolutionary algorithms (EAs) has been proposed to address many of the limitations of traditional approaches, but previous methods have been limited in the types of models they can fit. Therefore, we propose the use of an EA for dose–response modeling for a range of potential response model functional forms. This new method can not only fit the most commonly used nonlinear dose–response models (eg, exponential models and 3-, 4-, and 5-parameter logistic models) but also select the best model if no model assumption is made, which is especially useful in the case of high-throughput curve fitting. Compared with NLS, the new method provides stable and robust solutions without sensitivity to initial values.

非线性剂量反应关系广泛存在于细胞，生物化学和生理过程中，受到生物，化学或放射应力水平的变化的影响。对此类响应进行建模是毒性测试和化学筛选的关键组成部分。传统的模型拟合方法（例如非线性最小二乘（NLS））对初始参数值非常敏感，并且经常会收敛失败。已经提出使用进化算法（EA）来解决传统方法的许多局限性，但是以前的方法在它们适合的模型类型上受到限制。因此，我们建议使用EA进行一系列潜在反应模型功能形式的剂量反应模型。这种新方法不仅可以拟合最常用的非线性剂量反应模型（例如，指数模型和3、4和5参数逻辑模型），但如果没有模型假设，也要选择最佳模型，这在高通量曲线拟合的情况下尤其有用。与NLS相比，该新方法提供了稳定而强大的解决方案，并且对初始值不敏感。