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arXiv 2005.03214q-bio.MNq-bio.SC
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简单遗传电路信息处理能力的第一性原理预测

First-principles prediction of the information processing capacity of a simple genetic circuit

Manuel Razo-Mejia, Sarah Marzen, Griffin Chure, Rachel Taubman, Muir Morrison, Rob Phillips

发表信息：
Phys. Rev. E 102, 022404 (2020)
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摘要：

鉴于基因表达的随机性，遗传上相同的细胞暴露于相同的环境输入将产生不同的输出。这种异质性已经被假设为对细胞如何能够在不断变化的环境中生存具有影响。最近的工作探索了使用信息论作为一个框架，以了解细胞可以确定其周围环境的状态的准确性。然而，这些方法的预测能力是有限的，并没有经过严格的测试，使用精确的测量。为此，我们为一个简单的遗传电路生成一个最小模型，其中模型的所有参数值都来自独立发布的数据集。然后，我们预测的信息处理能力的遗传电路的一套生物物理参数，如蛋白质拷贝数和蛋白质-DNA亲和力。我们将这些无参数的预测与蛋白质表达分布的实验测定以及E. coli细胞。我们发现，我们的最小模型捕捉到的数据和推断的信息处理能力，我们的简单的遗传电路的系统偏差的细胞到细胞的变异的缩放。

英文摘要：

Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has been hypothesized to have consequences for how cells are able to survive in changing environments. Recent work has explored the use of information theory as a framework to understand the accuracy with which cells can ascertain the state of their surroundings. Yet the predictive power of these approaches is limited and has not been rigorously tested using precision measurements. To that end, we generate a minimal model for a simple genetic circuit in which all parameter values for the model come from independently published data sets. We then predict the information processing capacity of the genetic circuit for a suite of biophysical parameters such as protein copy number and protein-DNA affinity. We compare these parameter-free predictions with an experimental determination of protein expression distributions and the resulting information processing capacity of E. coli cells. We find that our minimal model captures the scaling of the cell-to-cell variability in the data and the inferred information processing capacity of our simple genetic circuit up to a systematic deviation.

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