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Updated: Jun 17, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Relationship between noise characteristics in protein expressions and regulatory structures of amino acid
1Osaka University, Department of Bioinformatics Engineering, Graduate School of Information Science and Technology, Osaka, Japan.
Abstract:
Gene regulatory dynamics involves several stochastic chemical reactions. As a consequence, the copy number of given protein varies greatly among cells even in the case of isogeneic cells. Recently, the characteristics of noise in gene expressions were studied by using simple artificial gene networks. However, the noise characteristics in natural regulatory networks having complex interactions still remain unclear. In this study, we have focused on the noise in natural regulatory networks to understand the relationship between the characteristics of the noise and the structures of regulatory interactions. We targeted the expressions of genes related to amino acid biosynthesis (AAB) because of their well known regulatory structures. By measuring the noise of AAB genes in isogeneic Escherichia coli cells using flow cytometry, we found the noise amplitude in AAB genes to depend on the structure of the regulatory network. We categorised the regulatory networks with feedback regulation into two cases. In one case, the gene expression is negatively regulated by the final products of the AAB pathway known as feedback repression, whereas in another case, the gene expression is negatively regulated as a result of depletion of the substrate that is located upstream of the AAB pathway and activates the expression of the corresponding gene. Our data revealed that the noise amplitude of AAB genes in the former case is significantly smaller than the noise amplitude in the latter case. Furthermore, we found that the response time as a result of environmental changes is generally longer in the former case. This result provides a basis for understanding the role of natural regulatory networks better.
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