Optimally Designed Model Selection for Synthetic Biology,ACS Synthetic Biology

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Optimally Designed Model Selection for Synthetic Biology
ACS Synthetic Biology ( IF 3.7 ) Pub Date : 2020-11-05 , DOI: 10.1021/acssynbio.0c00393
Lucia Bandiera _{1,

2} , David Gomez-Cabeza ₁ , James Gilman ₁ , Eva Balsa-Canto ₃ , Filippo Menolascina _{1,

2}

Affiliation

Modeling parts and circuits represents a significant roadblock to automating the Design-Build-Test-Learn cycle in synthetic biology. Once models are developed, discriminating among them requires informative data, computational resources, and skills that might not be readily available. The high cost entailed in model discrimination frequently leads to subjective choices on the selected structures and, in turn, to suboptimal models. Here, we outline frequentist and Bayesian approaches to model discrimination. We ranked three candidate models of a genetic toggle switch, which was adopted as a test case, according to the support from in vivo data. We show that, in each framework, efficient model discrimination can be achieved via optimally designed experiments. We offer a dynamical-systems interpretation of our optimization results and investigate their sensitivity to key parameters in the characterization of synthetic circuits. Our approach suggests that optimal experimental design is an effective strategy to discriminate between competing models of a gene regulatory network. Independent of the adopted framework, optimally designed perturbations exploit regions in the input space that maximally distinguish predictions from the competing models.

中文翻译：

合成生物学的优化设计模型选择

零件和电路建模是合成生物学中设计-构建-测试-学习循环自动化的重要障碍。一旦开发了模型，区分它们就需要信息数据、计算资源和可能不容易获得的技能。模型鉴别所带来的高成本经常导致对所选结构的主观选择，进而导致次优模型。在这里，我们概述了模型歧视的频率论和贝叶斯方法。根据in vivo的支持，我们对基因拨动开关的三个候选模型进行了排名，并作为测试用例数据。我们表明，在每个框架中，可以通过优化设计的实验来实现有效的模型区分。我们提供了对优化结果的动态系统解释，并研究了它们对合成电路表征中关键参数的敏感性。我们的方法表明，最佳实验设计是区分基因调控网络竞争模型的有效策略。独立于采用的框架，优化设计的扰动利用输入空间中的区域，最大限度地将预测与竞争模型区分开来。

更新日期：2020-11-21

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