Systems biology informed deep learning for inferring parameters and hidden dynamics.
Systems biology informed deep learning for inferring parameters and hidden dynamics.
复制标题
系统生物学为推断参数和隐藏动态提供了深入的学习。
DOI：
10.1371/journal.pcbi.1007575
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发表时间：
2020-11
影响因子：
4.3
通讯作者：
Karniadakis GE
中科院分区：
文献类型：
--
作者：
Yazdani A;Lu L;Raissi M;Karniadakis GE
生物反应的数学模型在系统层面上导致一组常微分方程，其中有许多未知参数，需要使用相对较少的实验测量来推断。具有可靠和鲁棒性的参数推断和预测算法一直是系统生物学的核心课题之一，也是本研究的重点。我们开发了一种新的系统生物学深度学习算法，该算法将常微分方程系统纳入神经网络。强制执行这些方程有效地增加了约束的优化过程，表现为一个强加的结构上的观测数据。使用一些分散和嘈杂的测量，我们能够推断未观测到的物种，外部强迫和未知的模型参数的动态。我们已经成功地测试了三个不同的基准问题的算法。系统生物过程的动力学通常使用常微分方程（ODE）来建模，其引入了各种未知参数，这些参数仅需要从少数物种的噪声浓度测量中有效地估计。在这项工作中，我们提出了一个新的“系统知情的神经网络”来推断实验未观察到的物种的动力学以及方程系统中的未知参数。通过将系统的常微分方程的神经网络，我们有效地增加了约束的优化算法，这使得该方法的噪声和稀疏测量的鲁棒性。
Mathematical models of biological reactions at the system-level lead to a set of ordinary differential equations with many unknown parameters that need to be inferred using relatively few experimental measurements. Having a reliable and robust algorithm for parameter inference and prediction of the hidden dynamics has been one of the core subjects in systems biology, and is the focus of this study. We have developed a new systems-biology-informed deep learning algorithm that incorporates the system of ordinary differential equations into the neural networks. Enforcing these equations effectively adds constraints to the optimization procedure that manifests itself as an imposed structure on the observational data. Using few scattered and noisy measurements, we are able to infer the dynamics of unobserved species, external forcing, and the unknown model parameters. We have successfully tested the algorithm for three different benchmark problems. The dynamics of systems biological processes are usually modeled using ordinary differential equations (ODEs), which introduce various unknown parameters that need to be estimated efficiently from noisy measurements of concentration for a few species only. In this work, we present a new “systems-informed neural network” to infer the dynamics of experimentally unobserved species as well as the unknown parameters in the system of equations. By incorporating the system of ODEs into the neural networks, we effectively add constraints to the optimization algorithm, which makes the method robust to noisy and sparse measurements.
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影响因子：
4.3
作者：
Lillacci G;Khammash M
通讯作者：
Khammash M
影响因子：
2.3
作者：
Foo, J.;Sindi, S.;Karniadakis, G. E.
通讯作者：
Karniadakis, G. E.
影响因子：
4.6
作者：
Engelhardt B;Frőhlich H;Kschischo M
通讯作者：
Kschischo M
DOI：
10.1049/ip-syb:20050065
发表时间：
2006-11-01
期刊：
IEE PROCEEDINGS SYSTEMS BIOLOGY
影响因子：
--
作者：
Aldridge, B. B.;Haller, G.;Lauffenburger, D. A.
通讯作者：
Lauffenburger, D. A.
影响因子：
2.1
作者：
Albers, David J.;Blancquart, Paul-Adrien;Stuart, Andrew
通讯作者：
Stuart, Andrew