Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate
Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate
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DOI：
10.1002/biot.201100084
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发表时间：
2011-07-01
影响因子：
4.7
通讯作者：
Jaramillo, Alfonso
中科院分区：
文献类型：
--
作者：
Carrera, Javier;Rodrigo, Guillermo;Jaramillo, Alfonso
合成生物学使用建模来促进新基因结构的设计。特别是，当表达异源系统时，模拟细胞底盘的反应是极其重要的。我们构建了一个数学模型的反应下异源表达的细菌细胞底盘。为此，我们依赖于以前的生长速率依赖于细胞资源可用性（在这种情况下，DNA和RNA聚合酶和核糖体）的表征。因此，我们估计细胞异源表达的最大能力为总RNA的46%和总蛋白的33%。为了实验验证我们的模型，我们设计了两种基因结构，其涉及在具有可调复制起点的载体中荧光报告基因的组成型表达。我们使用群体和单细胞荧光测量进行荧光测量。我们的模型预测细胞生长的几个异源结构在五种不同的培养条件和各种质粒拷贝数具有显着的准确性，并证实核糖体作为限制资源。我们的研究还证实，细菌对合成基因表达的反应可以根据对细胞资源的需求来理解，并且可以从相关的细胞参数来预测。
Synthetic biology uses modeling to facilitate the design of new genetic constructions. In particular, it is of utmost importance to model the reaction of the cellular chassis when expressing heterologous systems. We constructed a mathematical model for the response of a bacterial cell chassis under heterologous expression. For this, we relied on previous characterization of the growth-rate dependence on cellular resource availability (in this case, DNA and RNA polymerases and ribosomes). Accordingly, we estimated the maximum capacities of the cell for heterologous expression to be 46% of the total RNA and the 33% of the total protein. To experimentally validate our model, we engineered two genetic constructions that involved the constitutive expression of a fluorescent reporter in a vector with a tunable origin of replication. We performed fluorescent measurements using population and single-cell fluorescent measurements. Our model predicted cell growth for several heterologous constructions under five different culture conditions and various plasmid copy numbers with significant accuracy, and confirmed that ribosomes act as the limiting resource. Our study also confirmed that the bacterial response to synthetic gene expression could be understood in terms of the requirement for cellular resources and could be predicted from relevant cellular parameters.