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Efficient Optimization of Stimuli for Model-Based Design of Experiments to Resolve Dynamical Uncertainty

机译：高效优化模型的实验设计刺激，以解决动态不确定性

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This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters. Here we present an approach that is independent of the local parameter constraint. This approach is made computationally efficient and tractable by the use of: (1) sparse grid interpolation that approximates the biological system dynamics, (2) representative parameters that uniformly represent the data-consistent dynamical space, and (3) probability weights of the represented experimentally distinguishable dynamics. Our approach identifies data-consistent representative parameters using sparse grid interpolants, constructs the optimal input sequence from a greedy search, and defines the associated optimal measurements using a scenario tree. We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model. The 19-dimensional T-cell model also demonstrates the MBDOE algorithm’s scalability to higher dimensions. In both cases, the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico. Our results suggest that for resolving dynamical uncertainty, the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements.
机译：这种基于模型的实验设计（MBDOE）方法确定了应用的实验刺激的输入大小，以及应采取的相关测量，以最佳地限制在研究中生物系统的不确定动态。该实验设计问题的理想全球解决方案通常是由于生物系统的数学模型中的参数不确定性来计算地难以解决。其他人通过将解决方案限制为模型参数的本地估计来解决了这个问题。在这里，我们提出了一种独立于本地参数约束的方法。这种方法通过使用：（1）稀疏的网格插值来计算近似于稀疏的网格插值，该方法近似于生物系统动态，（2）统一代表数据一致的动态空间的代表性参数，以及所代表的概率权重实验可区分动态。我们的方法使用稀疏网格插值来识别数据一致的代表参数，从贪婪搜索构建最佳输入序列，并使用场景树定义相关的最佳测量。我们使用三维HES1模型和19维T细胞受体模型探索该MBDoE算法的最优性。 19维T细胞模型还展示了MBDoE算法的可扩展性更高的尺寸。在这两种情况下，在完成硅中的设计实验后，将目标系统状态的轨迹界定的动态不确定性区域分别降低了多达86％和99％。我们的研究结果表明，对于解决动态不确定性，设计与其相关测量的输入序列的能力在受测量的数量限制时尤为重要。

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