常微分方程常被用于生物系统的数学建模。对特定细胞类型的机制进行识别对于构建有用的模型并对基础生物过程有深入了解至关重要。已经提出并应用了正则化技术来识别特定于两种细胞类型（例如正常细胞和癌细胞）的机制，包括LASSO（最小绝对收缩与选择算子）。然而，当分析超过两种细胞类型时，这些方法不一致，并需要选择一个参考细胞类型，这可能会影响结果。为了使正则化方法适用于识别任意数量细胞类型的特异机制，我们提出将聚类LASSO纳入常微分方程建模框架中，通过惩罚不同细胞类型中编码特定机制的对数折叠变化参数的成对差异。这种方法引入的对称性使结果独立于参考细胞类型。我们讨论了现代数值优化技术的必要调整以及该方法的模型选择过程。我们使用真实生物模型和合成数据来评估性能，并证明它胜过现有的方法。最后，我们还通过将结果与独立的生物测量相连接，举例说明了其在已发表的生物模型以及实验数据中的应用。版权：© 2023年Hauber等。本文件属于知识共享署名许可下的开放获取文章，允许在任何媒介中无限制使用、分发和复制，前提是原始作者和来源得到了认可。

Ordinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to gain insights into the underlying biological processes. Regularization techniques have been proposed and applied to identify mechanisms specific to two cell types, e.g., healthy and cancer cells, including the LASSO (least absolute shrinkage and selection operator). However, when analyzing more than two cell types, these approaches are not consistent, and require the selection of a reference cell type, which can affect the results. To make the regularization approach applicable to identifying cell-type specific mechanisms in any number of cell types, we propose to incorporate the clustered LASSO into the framework of ordinary differential equation modeling by penalizing the pairwise differences of the logarithmized fold-change parameters encoding a specific mechanism in different cell types. The symmetry introduced by this approach renders the results independent of the reference cell type. We discuss the necessary adaptations of state-of-the-art numerical optimization techniques and the process of model selection for this method. We assess the performance with realistic biological models and synthetic data, and demonstrate that it outperforms existing approaches. Finally, we also exemplify its application to published biological models including experimental data, and link the results to independent biological measurements.Copyright: © 2023 Hauber et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.