On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter
On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter
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DOI：
10.3390/pr6090148
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发表时间：
2018-09-01
期刊：
影响因子：
3.5
通讯作者：
Menolascina, Filippo
中科院分区：
文献类型：
--
作者：
Bandiera, Lucia;Hou, Zhaozheng;Menolascina, Filippo
合成生物学寻求设计生物部件和电路来实现细胞中的新功能。在这一领域已经取得了重大成就，但是预测合成基因回路在体内的先验行为是一个重大挑战。数学模型提供了解决这一瓶颈的方法。然而，在生物学中，建模被认为是一项昂贵且耗时的任务。事实上，预测的质量取决于参数的准确性，而这些参数传统上是从缺乏信息的数据中推断出来的。使用基于模型的最优实验设计（MBOED）可以提高多少参数精度？为了解决这个问题，我们考虑了酵母酿酒酵母的诱导启动子。利用体内数据，我们重新拟合了该成分的动态模型，然后比较了标准（例如，步进输入）和优化设计的参数推断实验的性能。我们发现MBOED将模型校准的质量提高了60%。当考虑在线优化实验设计（OED）时，结果进一步提高了84%。我们的计算机结果表明，MBOED在识别生物部件模型方面提供了显着优势，因此应将其集成到其表征中。
Synthetic biology seeks to design biological parts and circuits that implement new functions in cells. Major accomplishments have been reported in this field, yet predicting a priori the in vivo behaviour of synthetic gene circuits is major a challenge. Mathematical models offer a means to address this bottleneck. However, in biology, modelling is perceived as an expensive, time-consuming task. Indeed, the quality of predictions depends on the accuracy of parameters, which are traditionally inferred from poorly informative data. How much can parameter accuracy be improved by using model-based optimal experimental design (MBOED)? To tackle this question, we considered an inducible promoter in the yeast S. cerevisiae. Using in vivo data, we re-fit a dynamic model for this component and then compared the performance of standard (e.g., step inputs) and optimally designed experiments for parameter inference. We found that MBOED improves the quality of model calibration by similar to 60%. Results further improve up to 84% when considering on-line optimal experimental design (OED). Our in silico results suggest that MBOED provides a significant advantage in the identification of models of biological parts and should thus be integrated into their characterisation.