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Combinatorial promoter design for engineering noisy gene expression

机译：工程噪声基因表达的组合启动子设计

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Understanding the behavior of basic biomolecular components as parts of larger systems is one of the goals of the developing field of synthetic biology. A multidisciplinary approach, involving mathematical and computational modeling in parallel with experimentation, is often crucial for gaining such insights and improving the efficiency of artificial gene network design. Here we used such an approach and developed a combinatorial promoter design strategy to characterize how the position and multiplicity of tetO_2 operator sites within the GAL1 promoter affect gene expression levels and gene expression noise in Saccharomyces cerevisiae. We observed stronger transcriptional repression and higher gene expression noise as a single operator site was moved closer to the TATA box, whereas for multiple operator-containing promoters, we found that the position and number of operator sites together determined the dose-response curve and gene expression noise. We developed a generic computational model that captured the experimentally observed differences for each of the promoters, and more detailed models to successively predict the behavior of multiple operator-containing promoters from single operator-containing promoters. Our results suggest that the independent binding of single repressors is not sufficient to explain the more complex behavior of the multiple operator-containing promoters. Taken together, our findings highlight the importance of joint experimental-computational efforts and some of the challenges of using a bottom-up approach based on well characterized, isolated biomolecular components for predicting the behavior of complex, synthetic gene networks, e.g., the whole can be different from the sum of its parts.
机译：了解基本生物分子成分作为较大系统的一部分的行为是合成生物学发展领域的目标之一。涉及数学和计算模型以及实验的多学科方法通常对于获得这种见解和提高人工基因网络设计的效率至关重要。在这里，我们使用了这种方法，并开发了组合启动子设计策略，以表征GAL1启动子中tetO_2操纵子位点的位置和多样性如何影响酿酒酵母中的基因表达水平和基因表达噪声。当单个操纵基因位点移近TATA框时，我们观察到更强的转录抑制和更高的基因表达噪音，而对于包含多个操纵基因的启动子，我们发现操纵基因位点的位置和数量共同决定了剂量反应曲线和基因表达噪音。我们开发了一个通用的计算模型，该模型捕获了每个启动子在实验上观察到的差异，并且建立了更详细的模型，以从单个包含操纵子的启动子中连续预测多个包含操纵子的启动子的行为。我们的结果表明，单个阻遏物的独立结合不足以解释包含多个操纵子的启动子的更复杂的行为。综上所述，我们的发现强调了联合实验计算工作的重要性，以及使用基于特征充分，分离的生物分子成分的自下而上方法预测复杂的合成基因网络（例如整个罐头）行为的挑战。与各部分的总和不同。

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