Institute of Physics, University of Freiburg, Hermann-Herder-Str. 3, Freiburg, 79104, Germany.
Signalling Research Centres BIOSS and CIBSS, Schänzlestr. 18, Freiburg, 79104, Germany.
BMC Bioinformatics. 2019 Jul 16;20(1):395. doi: 10.1186/s12859-019-2976-1.
Ordinary differential equation systems are frequently utilized to model biological systems and to infer knowledge about underlying properties. For instance, the development of drugs requires the knowledge to which extent malign cells differ from healthy ones to provide a specific treatment with least side effects. As these cell-type specific properties may stem from any part of biochemical cell processes, systematic quantitative approaches are necessary to identify the relevant potential drug targets. An ℓ regularization for the maximum likelihood parameter estimation proved to be successful, but falsely predicted cell-type dependent behaviour had to be corrected manually by using a Profile Likelihood approach.
The choice of extended ℓ penalty functions significantly decreased the number of falsely detected cell-type specific parameters. Thus, the total accuracy of the prediction could be increased. This was tested on a realistic dynamical benchmark model used for the DREAM6 challenge. Among Elastic Net, Adaptive Lasso and a non-convex ℓ penalty, the latter one showed the best predictions whilst also requiring least computation time. All extended methods include a hyper-parameter in the regularization function. For an Erythropoietin (EPO) induced signalling pathway, the extended methods ℓ and Adaptive Lasso revealed an unpublished alternative parsimonious model when varying the respective hyper-parameters.
Using ℓ or Adaptive Lasso with an a-priori choice for the hyper-parameter can lead to a more specific and accurate result than ℓ. Scanning different hyper-parameters can yield additional pieces of information about the system.
常微分方程组常被用于对生物系统建模，并推导出有关基础特性的知识。例如，药物的开发需要了解恶性细胞与健康细胞的差异程度，以便提供副作用最小的特定治疗方法。由于这些细胞类型特异性的特性可能源于生化细胞过程的任何部分，因此需要系统的定量方法来识别相关的潜在药物靶点。最大似然参数估计的ℓ正则化已被证明是成功的，但需要使用似然比方法手动纠正假阳性的细胞类型依赖性行为。
扩展ℓ惩罚函数的选择显著减少了错误检测到的细胞类型特异性参数的数量。因此，可以提高预测的整体准确性。这在用于 DREAM6 挑战的现实动态基准模型上进行了测试。在弹性网络、自适应套索和非凸ℓ惩罚之间，后者显示出最佳的预测效果，同时也需要最少的计算时间。所有扩展方法都在正则化函数中包含一个超参数。对于红细胞生成素（EPO）诱导的信号通路，在调整各自的超参数时，扩展方法ℓ和自适应套索揭示了一个未公开的替代简约模型。
使用具有超参数先验选择的ℓ或自适应套索可以比ℓ得到更具体和准确的结果。扫描不同的超参数可以为系统提供额外的信息。
