Learning (from) the errors of a systems biology model.
Learning (from) the errors of a systems biology model.
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DOI：
10.1038/srep20772
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发表时间：
2016-02-11
影响因子：
4.6
通讯作者：
Kschischo M
中科院分区：
文献类型：
--
作者：
Engelhardt B;Frőhlich H;Kschischo M
数学建模是一个劳动密集型过程，涉及对真实的数据进行多次迭代测试和手动模型修改。在生物学中，指导模型开发的领域知识在许多情况下本身是不完整和不确定的。这方面的一个主要问题是，生物系统是开放的。因此，模型中遗漏或未知的外部影响以及错误的相互作用可能导致严重误导的结果。在这里，我们介绍了动态弹性网络，数据驱动的数学方法，自动检测常微分方程（ODE）模型中的模型误差。我们证明了真实的和模拟数据，如何动态弹性网络方法可以用来自动（i）重建误差信号，（ii）识别模型误差的目标变量，（iii）重建真实的系统状态，即使是不完整的或初步的模型。我们的工作提供了一个系统的计算方法，促进不确定知识下的开放生物系统的建模。
Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open. Missed or unknown external influences as well as erroneous interactions in the model could thus lead to severely misleading results. Here we introduce the dynamic elastic-net, a data driven mathematical method which automatically detects such model errors in ordinary differential equation (ODE) models. We demonstrate for real and simulated data, how the dynamic elastic-net approach can be used to automatically (i) reconstruct the error signal, (ii) identify the target variables of model error, and (iii) reconstruct the true system state even for incomplete or preliminary models. Our work provides a systematic computational method facilitating modelling of open biological systems under uncertain knowledge.